diff --git a/README.md b/README.md
index 0fecdb92f8ff1ff8acc3bf915516be3c3de9023e..3fa8e3d85675ed0d0a8ba072b8a5c558070a76e5 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,122 @@
----
-title: GaussianAnything AIGC3D
-emoji: 📈
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 5.6.0
-app_file: app.py
-pinned: false
-license: other
-short_description: GaussianAnything generates high-quality and editable 2DGS.
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# GaussianAnything: arXiv 2024
+
+## setup the environment (the same env as LN3Diff)
+
+```bash
+conda create -n ga python=3.10
+conda activate ga
+pip intall -r requrements.txt # will install the surfel Gaussians environments automatically.
+```
+
+Then, install pytorch3d with 
+```bash
+pip install git+https://github.com/facebookresearch/pytorch3d.git@stable
+```
+
+
+### :dromedary_camel: TODO
+
+- [x] Release inference code and checkpoints.
+- [x] Release Training code.
+- [x] Release pre-extracted latent codes for 3D diffusion training.
+- [ ] Release Gradio Demo.
+- [ ] Release the evaluation code.
+- [ ] Lint the code.
+
+
+# Inference
+
+Be aware to change the $logdir in the bash file accordingly.
+
+To load the checkpoint automatically: please replace ```/mnt/sfs-common/yslan/open-source``` with ```yslan/GaussianAnything/ckpts/checkpoints```.
+
+
+
+## Text-2-3D:
+
+Please update the caption for 3D generation in ```datasets/caption-forpaper.txt```. T o change the number of samples to be generated, please change ```$num_samples``` in the bash file.
+
+**stage-1**:
+```
+bash shell_scripts/release/inference/t23d/stage1-t23d.sh
+```
+then, set the ```$stage_1_output_dir``` to the ```$logdir``` of the above stage.
+
+**stage-2**: 
+```
+bash shell_scripts/release/inference/t23d/stage2-t23d.sh
+```
+
+The results will be dumped to ```./logs/t23d/stage-2```
+
+## I23D (requires two stage generation):
+
+set the $data_dir accordingly. For some demo image, please download from [huggingfac.co/yslan/GaussianAnything/demo-img](https://huggingface.co/yslan/GaussianAnything/tree/main/demo-img).
+
+**stage-1**:
+```
+bash shell_scripts/release/inference/i23d/i23d-stage1.sh
+```
+
+then, set the $stage_1_output_dir to the $logdir of the above stage.
+
+**stage-2**: 
+```
+bash shell_scripts/release/inference/i23d/i23d-stage1.sh
+```
+
+## 3D VAE Reconstruction:
+
+To encode a 3D asset into the latent point cloud, please download the pre-trained VAE checkpoint from [huggingfac.co/yslan/gaussiananything/ckpts/vae/model_rec1965000.pt](https://huggingface.co/yslan/GaussianAnything/blob/main/ckpts/vae/model_rec1965000.pt) to ```./checkpoint/model_rec1965000.pt```.
+
+Then, run the inference script
+
+```bash 
+bash shell_scripts/release/inference/vae-3d.sh
+```
+
+This will encode the mulit-view 3D renderings in ```./assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0``` into the point-cloud structured latent code, and export them (along with the 2dgs mesh) in ```./logs/latent_dir/```. The exported latent code will be used for efficient 3D diffusion training.
+
+
+
+# Training (Flow Matching 3D Generation)
+All the training is conducted on 8 A100 (80GiB) with BF16 enabled. For training on V100, please use FP32 training by setting ```--use_amp``` False in the bash file. Feel free to tune the ```$batch_size``` in the bash file accordingly to match your VRAM.
+
+To facilitate reproducing the performance, we have uploaded the pre-extracted poind cloud-structured latent codes to the [huggingfac.co/yslan/gaussiananything/dataset/latent.tar.gz](https://huggingface.co/yslan/GaussianAnything/blob/main/dataset/latent.tar.gz) (34GiB required). Please download the pre extracted point cloud latent codes, unzip and set the ```$mv_latent_dir``` in the bash file accordingly.
+
+
+## Text to 3D:
+Please donwload the 3D caption from hugging face [huggingfac.co/yslan/GaussianAnything/dataset/text_captions_3dtopia.json](https://huggingface.co/yslan/GaussianAnything/blob/main/dataset/text_captions_3dtopia.json), and put it under ```dataset```.
+
+
+Note that if you want to train a specific class of Objaverse, just manually change the code at ```datasets/g_buffer_objaverse.py:3043```.
+
+**stage-1 training (point cloud generation)**:
+
+```
+bash shell_scripts/release/train/stage2-t23d/t23d-pcd-gen.sh
+```
+
+**stage-2 training (point cloud-conditioned KL feature generation)**:
+
+```
+bash shell_scripts/release/train/stage2-t23d/t23d-klfeat-gen.sh
+```
+
+## (single-view) Image to 3D
+Please download g-buffer dataset first.
+
+**stage-1 training (point cloud generation)**:
+
+```
+bash shell_scripts/release/train/stage2-i23d/i23d-pcd-gen.sh
+```
+
+**stage-2 training (point cloud-conditioned KL feature generation)**:
+
+```
+bash shell_scripts/release/train/stage2-i23d/i23d-klfeat-gen.sh
+```
+
+<!-- # Training (3D-aware VAE)
+Since the  -->
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c657a3220794ab9574ada584a7018a28ded9eab
--- /dev/null
+++ b/app.py
@@ -0,0 +1,397 @@
+import argparse
+import spaces
+import json
+import sys
+sys.path.append('.')
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+
+import os
+import gc
+import dnnlib
+from omegaconf import OmegaConf
+from PIL import Image 
+from dnnlib.util import EasyDict
+
+import gradio as gr
+
+import rembg
+
+from huggingface_hub import hf_hub_download
+
+
+"""
+Generate a large batch of image samples from a model and save them as a large
+numpy array. This can be used to produce samples for FID evaluation.
+"""
+
+import os
+
+
+from pdb import set_trace as st
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    NUM_CLASSES,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+    add_dict_to_argparser,
+    args_to_dict,
+    continuous_diffusion_defaults,
+    control_net_defaults,
+)
+
+th.backends.cuda.matmul.allow_tf32 = True
+th.backends.cudnn.allow_tf32 = True
+th.backends.cudnn.enabled = True
+
+from pathlib import Path
+
+from tqdm import tqdm, trange
+import dnnlib
+from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, AE_with_Diffusion, rendering_options_defaults, eg3d_options_default, dataset_defaults
+
+from datasets.shapenet import load_eval_data
+from torch.utils.data import Subset
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+from transport.train_utils import parse_transport_args
+
+from utils.infer_utils import remove_background, resize_foreground
+
+SEED = 0
+
+def resize_to_224(img):
+    img = transforms.functional.resize(img, 518, # required by dino.
+        interpolation=transforms.InterpolationMode.LANCZOS)
+    return img
+
+
+def set_white_background(image):
+    image = np.array(image).astype(np.float32) / 255.0
+    mask = image[:, :, 3:4]
+    image = image[:, :, :3] * mask + (1 - mask)
+    image = Image.fromarray((image * 255.0).astype(np.uint8))
+    return image
+
+
+def check_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image uploaded!")
+
+
+
+def main(args_1, args_2):
+
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '12355'
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    os.environ["RANK"] = "0"
+    os.environ["WORLD_SIZE"] = "1"
+
+    # args.rendering_kwargs = rendering_options_defaults(args)
+
+    dist_util.setup_dist(args_1)
+    logger.configure(dir=args_1.logdir)
+
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    # * set denoise model args
+    logger.log("creating model and diffusion...")
+    args_1.img_size = [args_1.image_size_encoder]
+    args_1.image_size = args_1.image_size_encoder  # 224, follow the triplane size
+
+    args_2.img_size = [args_2.image_size_encoder]
+    args_2.image_size = args_2.image_size_encoder  # 224, follow the triplane size
+
+    denoise_model_stage1, diffusion = create_model_and_diffusion(
+        **args_to_dict(args_1,
+                       model_and_diffusion_defaults().keys()))
+
+    denoise_model_stage2, diffusion = create_model_and_diffusion(
+        **args_to_dict(args_2,
+                       model_and_diffusion_defaults().keys()))
+
+    opts = eg3d_options_default()
+
+    denoise_model_stage1.to(dist_util.dev())
+    denoise_model_stage1.eval()
+    denoise_model_stage2.to(dist_util.dev())
+    denoise_model_stage2.eval()
+
+    # * auto-encoder reconstruction model
+    logger.log("creating 3DAE...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args_1,
+                       encoder_and_nsr_defaults().keys()))
+
+    auto_encoder.to(dist_util.dev())
+    auto_encoder.eval()
+
+    # faster inference
+    # denoise_model = denoise_model.to(th.bfloat16)
+    # auto_encoder = auto_encoder.to(th.bfloat16)
+
+    # TODO, how to set the scale?
+    logger.log("create dataset")
+
+    if args_1.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data
+    else:  # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data
+    
+    # load data if i23d
+    # if args.i23d:
+    #     data = load_eval_data(
+    #         file_path=args.eval_data_dir,
+    #         batch_size=args.eval_batch_size,
+    #         reso=args.image_size,
+    #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #         num_workers=args.num_workers,
+    #         load_depth=True,  # for evaluation
+    #         preprocess=auto_encoder.preprocess,
+    #         **args_to_dict(args,
+    #                         dataset_defaults().keys()))
+    # else:
+    data = None # t23d sampling, only caption required
+
+
+    TrainLoop = {
+        'flow_matching':
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine,
+        'flow_matching_gs':  
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine_gs, # slightly modified sampling and rendering for gs
+    }[args_1.trainer_name]
+
+    # continuous
+    sde_diffusion = None
+
+    auto_encoder.decoder.rendering_kwargs = args_1.rendering_kwargs
+    # stage_1_output_dir = args_2.stage_1_output_dir
+
+    training_loop_class_stage1 = TrainLoop(rec_model=auto_encoder,
+                                    denoise_model=denoise_model_stage1,
+                                    control_model=None, # to remove
+                                    diffusion=diffusion,
+                                    sde_diffusion=sde_diffusion,
+                                    loss_class=None,
+                                    data=data,
+                                    eval_data=None,
+                                    **args_1)
+
+    training_loop_class_stage2 = TrainLoop(rec_model=auto_encoder,
+                                    denoise_model=denoise_model_stage2,
+                                    control_model=None, # to remove
+                                    diffusion=diffusion,
+                                    sde_diffusion=sde_diffusion,
+                                    loss_class=None,
+                                    data=data,
+                                    eval_data=None,
+                                    **args_2)
+
+
+    css = """
+    h1 {
+        text-align: center;
+        display:block;
+    }
+    """
+
+
+    def preprocess(input_image, preprocess_background=True, foreground_ratio=0.85):
+        if preprocess_background:
+            rembg_session = rembg.new_session()
+            image = input_image.convert("RGB")
+            image = remove_background(image, rembg_session)
+            image = resize_foreground(image, foreground_ratio)
+            image = set_white_background(image)
+        else:
+            image = input_image
+            if image.mode == "RGBA":
+                image = set_white_background(image)
+        image = resize_to_224(image)
+        return image
+
+
+    @spaces.GPU(duration=50)
+    def cascaded_generation(processed_image, seed, cfg_scale):
+        # gc.collect()
+        # stage-1, generate pcd
+        stage_1_pcd = training_loop_class_stage1.eval_i23d_and_export_gradio(processed_image, seed, cfg_scale)
+        # stage-2, generate surfel Gaussians, tsdf mesh etc.
+        video_path, rgb_xyz_path, post_mesh_path = training_loop_class_stage2.eval_i23d_and_export_gradio(processed_image, seed, cfg_scale)
+        return video_path, rgb_xyz_path, post_mesh_path, stage_1_pcd
+
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            """
+            # GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation
+            **GaussianAnything (arXiv 2024)** [[code](https://github.com/NIRVANALAN/GaussianAnything), [project page](https://nirvanalan.github.io/projects/GA/)] is a native 3D diffusion model that supports high-quality 2D Gaussians generation. 
+            It first trains a 3D VAE on **Objaverse**, which compress each 3D asset into a compact point cloud-structured latent. 
+            After that, a image/text-conditioned diffusion model is trained following LDM paradigm.
+            The model used in the demo adopts 3D DiT architecture and flow-matching framework, and supports single-image condition.
+            It is trained on 8 A100 GPUs for 1M iterations with batch size 256.
+            Locally, on an NVIDIA A100/A10 GPU, each image-conditioned diffusion generation can be done within 20 seconds (time varies due to the adaptive-step ODE solver used in flow-mathcing.)
+            Upload an image of an object or click on one of the provided examples to see how the GaussianAnything works.
+
+            The 3D viewer will render a .glb point cloud exported from the centers of the surfel Gaussians, and an integrated TSDF mesh.
+            For best results run the demo locally and render locally - to do so, clone the [main repository](https://github.com/NIRVANALAN/GaussianAnything).
+            """
+            )
+        with gr.Row(variant="panel"):
+            with gr.Column():
+                with gr.Row():
+                    input_image = gr.Image(
+                        label="Input Image",
+                        image_mode="RGBA",
+                        sources="upload",
+                        type="pil",
+                        elem_id="content_image",
+                    )
+                    processed_image = gr.Image(label="Processed Image", interactive=False)
+
+                # params
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            # with gr.Group():
+
+                            cfg_scale = gr.Number(
+                                label="CFG-scale", value=4.0, interactive=True,
+                            )
+                            seed = gr.Number(
+                                label="Seed", value=42, interactive=True,
+                            )
+
+                            # num_steps = gr.Number(
+                            #     label="ODE Sampling Steps", value=250, interactive=True,
+                            # )
+
+                        # with gr.Column():
+                        # with gr.Row():
+                        #         mesh_size = gr.Number(
+                        #             label="Mesh Resolution", value=192, interactive=True,
+                        #     )
+
+                        #         mesh_thres = gr.Number(
+                        #             label="Mesh Iso-surface", value=10, interactive=True,
+                        #         )
+
+                with gr.Row():
+                    with gr.Group():
+                        preprocess_background = gr.Checkbox(
+                            label="Remove Background", value=False
+                        )
+                with gr.Row():
+                    submit = gr.Button("Generate", elem_id="generate", variant="primary")
+
+                with gr.Row(variant="panel"): 
+                    gr.Examples(
+                        examples=[
+                            str(path) for path in sorted(Path('./assets/demo-image-for-i23d/instantmesh').glob('**/*.png'))
+                        ] + [str(path) for path in sorted(Path('./assets/demo-image-for-i23d/gso').glob('**/*.png'))],
+                        inputs=[input_image],
+                        cache_examples=False,
+                        label="Examples",
+                        examples_per_page=20,
+                    )
+
+            with gr.Column():
+                with gr.Row():
+                    with gr.Tab("Stage-2 Output"):
+                        with gr.Column():
+                            output_video = gr.Video(value=None, width=512, label="Rendered Video (2 LoDs)", autoplay=True, loop=True)
+                            # output_video = gr.Video(value=None, width=256, label="Rendered Video", autoplay=True)
+                            output_gs = gr.Model3D(
+                                height=256,
+                                label="2DGS Center",
+                                pan_speed=0.5,
+                                clear_color=(1,1,1,1), # loading glb file only.
+                            )
+                            output_model = gr.Model3D(
+                                height=256,
+                                label="TSDF Mesh",
+                                pan_speed=0.5,
+                                clear_color=(1,1,1,1), # loading tsdf ply files.
+                            )
+
+                    with gr.Tab("Stage-1 Output"):
+                        with gr.Column():
+                            output_model_stage1 = gr.Model3D(
+                                height=256,
+                                label="Stage-1",
+                                pan_speed=0.5,
+                                clear_color=(1,1,1,1), # loading tsdf ply files.
+                            )
+
+
+
+        gr.Markdown(
+            """
+            ## Comments:
+            1. The sampling time varies since ODE-based sampling method (dopri5 by default) has adaptive internal step, and reducing sampling steps may not reduce the overal sampling time. Sampling steps=250 is the emperical value that works well in most cases.
+            2. The 3D viewer shows a colored .glb mesh extracted from volumetric tri-plane, and may differ slightly with the volume rendering result.
+            3. If you find your result unsatisfying, tune the CFG scale and change the random seed. Usually slightly increase the CFG value can lead to better performance.
+            3. Known limitations include:
+            - Texture details missing: since our VAE is trained on 192x192 resolution due the the resource constraints, the texture details generated by the final 3D-LDM may be blurry. We will keep improving the performance in the future.
+            4. Regarding reconstruction performance, our model is slightly inferior to state-of-the-art multi-view LRM-based method (e.g. InstantMesh), but offers much better diversity, flexibility and editing potential due to the intrinsic nature of diffusion model.
+
+            ## How does it work?
+
+            GaussianAnything is a native 3D Latent Diffusion Model that supports direct 3D asset generation via diffusion sampling. 
+            Compared to SDS-based ([DreamFusion](https://dreamfusion3d.github.io/)), mulit-view generation-based ([MVDream](https://arxiv.org/abs/2308.16512), [Zero123++](https://github.com/SUDO-AI-3D/zero123plus), [Instant3D](https://instant-3d.github.io/)) and feedforward 3D reconstruction-based ([LRM](https://yiconghong.me/LRM/), [InstantMesh](https://github.com/TencentARC/InstantMesh), [LGM](https://github.com/3DTopia/LGM)), 
+            GaussianAnything supports feedforward 3D generation with a unified framework.
+            Like 2D/Video AIGC pipeline, GaussianAnything first trains a 3D-VAE and then conduct LDM training (text/image conditioned) on the learned latent space. Some related methods from the industry ([Shape-E](https://github.com/openai/shap-e), [CLAY](https://github.com/CLAY-3D/OpenCLAY), [Meta 3D Gen](https://arxiv.org/abs/2303.05371)) also follow the same paradigm.
+            Though currently the performance of the origin 3D LDM's works are overall inferior to reconstruction-based methods, we believe the proposed method has much potential and scales better with more data and compute resources, and may yield better 3D editing performance due to its compatability with diffusion model.
+            For more results see the [project page](https://nirvanalan.github.io/projects/GA/).
+            """
+        )
+
+        submit.click(fn=check_input_image, inputs=[input_image]).success(
+            fn=preprocess,
+            inputs=[input_image, preprocess_background],
+            outputs=[processed_image],
+        ).success(
+            # fn=reconstruct_and_export,
+            # inputs=[processed_image],
+            # outputs=[output_model, output_video],
+            fn=cascaded_generation,
+            inputs=[processed_image, seed, cfg_scale],
+            # inputs=[processed_image, num_steps, seed, mesh_size, mesh_thres, unconditional_guidance_scale, args.stage_1_output_dir],
+            outputs=[output_video, output_gs, output_model, output_model_stage1],
+        )
+
+    demo.queue(max_size=1)
+    demo.launch(share=True)
+
+if __name__ == "__main__":
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    with open('configs/gradio_i23d_stage2_args.json') as f:
+        args_2 = json.load(f)
+        args_2 = EasyDict(args_2)
+        args_2.local_rank = 0
+        args_2.gpus = 1
+
+    with open('configs/gradio_i23d_stage1_args.json') as f:
+        args_1 = json.load(f)
+        args_1 = EasyDict(args_1)
+        args_1.local_rank = 0
+        args_1.gpus = 1
+
+    main(args_1, args_2)
diff --git a/assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0/10017/1/bbox.npy b/assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0/10017/1/bbox.npy
new file mode 100644
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+++ b/assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0/10017/1/bbox.npy
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diff --git a/assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0/10017/2/bbox.npy b/assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0/10017/2/bbox.npy
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diff --git a/assets/objv_eval_pose.pt b/assets/objv_eval_pose.pt
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diff --git a/checkpoint/.gitkeep b/checkpoint/.gitkeep
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+/mnt/sfs-common/yslan/open-source/vae/model_rec1965000.pt
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diff --git a/cldm/__pycache__/cldm.cpython-310.pyc b/cldm/__pycache__/cldm.cpython-310.pyc
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diff --git a/cldm/cldm.py b/cldm/cldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..e78e8c9e9299ad50259fef980d53c12400b371a9
--- /dev/null
+++ b/cldm/cldm.py
@@ -0,0 +1,456 @@
+import torch
+import torch as th
+import torch.nn as nn
+
+from ldm.modules.diffusionmodules.util import (
+    conv_nd,
+    linear,
+    zero_module,
+    timestep_embedding,
+)
+
+from einops import rearrange, repeat
+from torchvision.utils import make_grid
+from ldm.modules.attention import SpatialTransformer
+# from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
+from guided_diffusion.unet import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
+# from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.util import log_txt_as_img, exists # , instantiate_from_config
+# from ldm.models.diffusion.ddim import DDIMSampler
+from pdb import set_trace as st
+
+
+class ControlledUnetModel(UNetModel):
+    def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, get_attr='', **kwargs):
+
+        if get_attr != '': # not breaking the forward hooks
+            return getattr(self, get_attr)
+
+        hs = []
+        with torch.no_grad(): # fix middle_block, SD
+            t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+            emb = self.time_embed(t_emb)
+
+            if self.roll_out:
+                x = rearrange(x, 'b (n c) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+
+            h = x.type(self.dtype)
+            for module in self.input_blocks:
+                h = module(h, emb, context)
+                hs.append(h)
+            h = self.middle_block(h, emb, context)
+
+        assert control is not None
+        # if control is not None:
+        h += control.pop()
+
+        for i, module in enumerate(self.output_blocks):
+            if only_mid_control or control is None:
+                h = torch.cat([h, hs.pop()], dim=1)
+            else:
+                # st()
+                h = torch.cat([h, hs.pop() + control.pop()], dim=1)
+            h = module(h, emb, context)
+
+        h = h.type(x.dtype)
+        h = self.out(h)
+        if self.roll_out:
+            return rearrange(h, 'b c h (n w) -> b (n c) h w', n=3)
+        return h
+
+
+class ControlNet(nn.Module):
+    def __init__(
+            self,
+            image_size,
+            in_channels,
+            model_channels,
+            hint_channels,
+            num_res_blocks,
+            attention_resolutions,
+            dropout=0,
+            channel_mult=(1, 2, 4, 8),
+            conv_resample=True,
+            dims=2,
+            use_checkpoint=False,
+            use_fp16=False,
+            num_heads=-1,
+            num_head_channels=-1,
+            num_heads_upsample=-1,
+            use_scale_shift_norm=False,
+            resblock_updown=False,
+            use_new_attention_order=False,
+            # * new keys introduced in LDM
+            use_spatial_transformer=False,  # custom transformer support
+            transformer_depth=1,  # custom transformer support
+            context_dim=None,  # custom transformer support
+            n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
+            legacy=True,
+            disable_self_attentions=None,
+            num_attention_blocks=None,
+            disable_middle_self_attn=False,
+            use_linear_in_transformer=False,
+            roll_out=False,
+    ):
+        super().__init__()
+        self.roll_out = roll_out
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.dims = dims
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                  f"attention will still not be set.")
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        # self.use_checkpoint = use_checkpoint
+        self.use_checkpoint = False
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+
+        self.input_hint_block = TimestepEmbedSequential( # f=8
+            conv_nd(dims, hint_channels, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 32, 32, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 96, 96, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+        )
+
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        # num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self.zero_convs.append(self.make_zero_conv(ch))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                self.zero_convs.append(self.make_zero_conv(ch))
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            # num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
+                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+                use_checkpoint=use_checkpoint
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self.middle_block_out = self.make_zero_conv(ch)
+        self._feature_size += ch
+
+    def make_zero_conv(self, channels):
+        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+
+    def forward(self, x, hint, timesteps, context, **kwargs):
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb) # time condition embedding
+        guided_hint = self.input_hint_block(hint, emb, context) # B 320 8 8, if input resolution = 64
+
+        if self.roll_out:
+            x = rearrange(x, 'b (n c) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+            guided_hint = repeat(guided_hint, 'b c h w -> b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+
+        outs = []
+
+        h = x.type(self.dtype)
+        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
+            if guided_hint is not None: # f=8, shall send in 128x128 img_sr
+                h = module(h, emb, context) # B 320 16 16
+                h += guided_hint
+                guided_hint = None
+            else:
+                h = module(h, emb, context)
+            outs.append(zero_conv(h, emb, context))
+
+        h = self.middle_block(h, emb, context)
+        outs.append(self.middle_block_out(h, emb, context))
+
+        return outs
+
+# ! do not support PL here
+# class ControlLDM(LatentDiffusion):
+
+#     def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs):
+#         super().__init__(*args, **kwargs)
+#         self.control_model = instantiate_from_config(control_stage_config)
+#         self.control_key = control_key
+#         self.only_mid_control = only_mid_control
+#         self.control_scales = [1.0] * 13
+
+#     @torch.no_grad()
+#     def get_input(self, batch, k, bs=None, *args, **kwargs):
+#         x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
+#         control = batch[self.control_key]
+#         if bs is not None:
+#             control = control[:bs]
+#         control = control.to(self.device)
+#         control = einops.rearrange(control, 'b h w c -> b c h w')
+#         control = control.to(memory_format=torch.contiguous_format).float()
+#         return x, dict(c_crossattn=[c], c_concat=[control])
+
+#     def apply_model(self, x_noisy, t, cond, *args, **kwargs):
+#         assert isinstance(cond, dict)
+#         diffusion_model = self.model.diffusion_model
+
+#         cond_txt = torch.cat(cond['c_crossattn'], 1)
+
+#         if cond['c_concat'] is None:
+#             eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control)
+#         else:
+#             control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt)
+#             control = [c * scale for c, scale in zip(control, self.control_scales)]
+#             eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control)
+
+#         return eps
+
+#     @torch.no_grad()
+#     def get_unconditional_conditioning(self, N):
+#         return self.get_learned_conditioning([""] * N)
+
+#     @torch.no_grad()
+#     def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
+#                    quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+#                    plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
+#                    use_ema_scope=True,
+#                    **kwargs):
+#         use_ddim = ddim_steps is not None
+
+#         log = dict()
+#         z, c = self.get_input(batch, self.first_stage_key, bs=N)
+#         c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N]
+#         N = min(z.shape[0], N)
+#         n_row = min(z.shape[0], n_row)
+#         log["reconstruction"] = self.decode_first_stage(z)
+#         log["control"] = c_cat * 2.0 - 1.0
+#         log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16)
+
+#         if plot_diffusion_rows:
+#             # get diffusion row
+#             diffusion_row = list()
+#             z_start = z[:n_row]
+#             for t in range(self.num_timesteps):
+#                 if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+#                     t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+#                     t = t.to(self.device).long()
+#                     noise = torch.randn_like(z_start)
+#                     z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+#                     diffusion_row.append(self.decode_first_stage(z_noisy))
+
+#             diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+#             diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+#             diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+#             diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+#             log["diffusion_row"] = diffusion_grid
+
+#         if sample:
+#             # get denoise row
+#             samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+#                                                      batch_size=N, ddim=use_ddim,
+#                                                      ddim_steps=ddim_steps, eta=ddim_eta)
+#             x_samples = self.decode_first_stage(samples)
+#             log["samples"] = x_samples
+#             if plot_denoise_rows:
+#                 denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+#                 log["denoise_row"] = denoise_grid
+
+#         if unconditional_guidance_scale > 1.0:
+#             uc_cross = self.get_unconditional_conditioning(N)
+#             uc_cat = c_cat  # torch.zeros_like(c_cat)
+#             uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+#             samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+#                                              batch_size=N, ddim=use_ddim,
+#                                              ddim_steps=ddim_steps, eta=ddim_eta,
+#                                              unconditional_guidance_scale=unconditional_guidance_scale,
+#                                              unconditional_conditioning=uc_full,
+#                                              )
+#             x_samples_cfg = self.decode_first_stage(samples_cfg)
+#             log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+#         return log
+
+#     @torch.no_grad()
+#     def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+#         ddim_sampler = DDIMSampler(self)
+#         b, c, h, w = cond["c_concat"][0].shape
+#         shape = (self.channels, h // 8, w // 8)
+#         samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)
+#         return samples, intermediates
+
+#     def configure_optimizers(self):
+#         lr = self.learning_rate
+#         params = list(self.control_model.parameters())
+#         if not self.sd_locked:
+#             params += list(self.model.diffusion_model.output_blocks.parameters())
+#             params += list(self.model.diffusion_model.out.parameters())
+#         opt = torch.optim.AdamW(params, lr=lr)
+#         return opt
+
+#     def low_vram_shift(self, is_diffusing):
+#         if is_diffusing:
+#             self.model = self.model.cuda()
+#             self.control_model = self.control_model.cuda()
+#             self.first_stage_model = self.first_stage_model.cpu()
+#             self.cond_stage_model = self.cond_stage_model.cpu()
+#         else:
+#             self.model = self.model.cpu()
+#             self.control_model = self.control_model.cpu()
+#             self.first_stage_model = self.first_stage_model.cuda()
+#             self.cond_stage_model = self.cond_stage_model.cuda()
diff --git a/cldm/ddim_hacked.py b/cldm/ddim_hacked.py
new file mode 100644
index 0000000000000000000000000000000000000000..25b1bc947272ad14d7f7e5e4d1809005253b63d0
--- /dev/null
+++ b/cldm/ddim_hacked.py
@@ -0,0 +1,317 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
+
+
+class DDIMSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               dynamic_threshold=None,
+               ucg_schedule=None,
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                ctmp = conditioning[list(conditioning.keys())[0]]
+                while isinstance(ctmp, list): ctmp = ctmp[0]
+                cbs = ctmp.shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != batch_size:
+                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+        samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    dynamic_threshold=dynamic_threshold,
+                                                    ucg_schedule=ucg_schedule
+                                                    )
+        return samples, intermediates
+
+    @torch.no_grad()
+    def ddim_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+                      ucg_schedule=None):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            if ucg_schedule is not None:
+                assert len(ucg_schedule) == len(time_range)
+                unconditional_guidance_scale = ucg_schedule[i]
+
+            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      dynamic_threshold=dynamic_threshold)
+            img, pred_x0 = outs
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      dynamic_threshold=None):
+        b, *_, device = *x.shape, x.device
+
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            model_output = self.model.apply_model(x, t, c)
+        else:
+            model_t = self.model.apply_model(x, t, c)
+            model_uncond = self.model.apply_model(x, t, unconditional_conditioning)
+            model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+        if self.model.parameterization == "v":
+            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+        else:
+            e_t = model_output
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps", 'not implemented'
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        if self.model.parameterization != "v":
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        else:
+            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+        if dynamic_threshold is not None:
+            raise NotImplementedError()
+
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    @torch.no_grad()
+    def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+               unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        num_reference_steps = timesteps.shape[0]
+
+        assert t_enc <= num_reference_steps
+        num_steps = t_enc
+
+        if use_original_steps:
+            alphas_next = self.alphas_cumprod[:num_steps]
+            alphas = self.alphas_cumprod_prev[:num_steps]
+        else:
+            alphas_next = self.ddim_alphas[:num_steps]
+            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+        x_next = x0
+        intermediates = []
+        inter_steps = []
+        for i in tqdm(range(num_steps), desc='Encoding Image'):
+            t = torch.full((x0.shape[0],), timesteps[i], device=self.model.device, dtype=torch.long)
+            if unconditional_guidance_scale == 1.:
+                noise_pred = self.model.apply_model(x_next, t, c)
+            else:
+                assert unconditional_conditioning is not None
+                e_t_uncond, noise_pred = torch.chunk(
+                    self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+                                           torch.cat((unconditional_conditioning, c))), 2)
+                noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+            weighted_noise_pred = alphas_next[i].sqrt() * (
+                    (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+            x_next = xt_weighted + weighted_noise_pred
+            if return_intermediates and i % (
+                    num_steps // return_intermediates) == 0 and i < num_steps - 1:
+                intermediates.append(x_next)
+                inter_steps.append(i)
+            elif return_intermediates and i >= num_steps - 2:
+                intermediates.append(x_next)
+                inter_steps.append(i)
+            if callback: callback(i)
+
+        out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+        if return_intermediates:
+            out.update({'intermediates': intermediates})
+        return x_next, out
+
+    @torch.no_grad()
+    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+        # fast, but does not allow for exact reconstruction
+        # t serves as an index to gather the correct alphas
+        if use_original_steps:
+            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+        else:
+            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+        if noise is None:
+            noise = torch.randn_like(x0)
+        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+    @torch.no_grad()
+    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+               use_original_steps=False, callback=None):
+
+        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        timesteps = timesteps[:t_start]
+
+        time_range = np.flip(timesteps)
+        total_steps = timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+        x_dec = x_latent
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+                                          unconditional_guidance_scale=unconditional_guidance_scale,
+                                          unconditional_conditioning=unconditional_conditioning)
+            if callback: callback(i)
+        return x_dec
diff --git a/cldm/hack.py b/cldm/hack.py
new file mode 100644
index 0000000000000000000000000000000000000000..454361e9d036cd1a6a79122c2fd16b489e4767b1
--- /dev/null
+++ b/cldm/hack.py
@@ -0,0 +1,111 @@
+import torch
+import einops
+
+import ldm.modules.encoders.modules
+import ldm.modules.attention
+
+from transformers import logging
+from ldm.modules.attention import default
+
+
+def disable_verbosity():
+    logging.set_verbosity_error()
+    print('logging improved.')
+    return
+
+
+def enable_sliced_attention():
+    ldm.modules.attention.CrossAttention.forward = _hacked_sliced_attentin_forward
+    print('Enabled sliced_attention.')
+    return
+
+
+def hack_everything(clip_skip=0):
+    disable_verbosity()
+    ldm.modules.encoders.modules.FrozenCLIPEmbedder.forward = _hacked_clip_forward
+    ldm.modules.encoders.modules.FrozenCLIPEmbedder.clip_skip = clip_skip
+    print('Enabled clip hacks.')
+    return
+
+
+# Written by Lvmin
+def _hacked_clip_forward(self, text):
+    PAD = self.tokenizer.pad_token_id
+    EOS = self.tokenizer.eos_token_id
+    BOS = self.tokenizer.bos_token_id
+
+    def tokenize(t):
+        return self.tokenizer(t, truncation=False, add_special_tokens=False)["input_ids"]
+
+    def transformer_encode(t):
+        if self.clip_skip > 1:
+            rt = self.transformer(input_ids=t, output_hidden_states=True)
+            return self.transformer.text_model.final_layer_norm(rt.hidden_states[-self.clip_skip])
+        else:
+            return self.transformer(input_ids=t, output_hidden_states=False).last_hidden_state
+
+    def split(x):
+        return x[75 * 0: 75 * 1], x[75 * 1: 75 * 2], x[75 * 2: 75 * 3]
+
+    def pad(x, p, i):
+        return x[:i] if len(x) >= i else x + [p] * (i - len(x))
+
+    raw_tokens_list = tokenize(text)
+    tokens_list = []
+
+    for raw_tokens in raw_tokens_list:
+        raw_tokens_123 = split(raw_tokens)
+        raw_tokens_123 = [[BOS] + raw_tokens_i + [EOS] for raw_tokens_i in raw_tokens_123]
+        raw_tokens_123 = [pad(raw_tokens_i, PAD, 77) for raw_tokens_i in raw_tokens_123]
+        tokens_list.append(raw_tokens_123)
+
+    tokens_list = torch.IntTensor(tokens_list).to(self.device)
+
+    feed = einops.rearrange(tokens_list, 'b f i -> (b f) i')
+    y = transformer_encode(feed)
+    z = einops.rearrange(y, '(b f) i c -> b (f i) c', f=3)
+
+    return z
+
+
+# Stolen from https://github.com/basujindal/stable-diffusion/blob/main/optimizedSD/splitAttention.py
+def _hacked_sliced_attentin_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+    k = self.to_k(context)
+    v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    limit = k.shape[0]
+    att_step = 1
+    q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
+    k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
+    v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))
+
+    q_chunks.reverse()
+    k_chunks.reverse()
+    v_chunks.reverse()
+    sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
+    del k, q, v
+    for i in range(0, limit, att_step):
+        q_buffer = q_chunks.pop()
+        k_buffer = k_chunks.pop()
+        v_buffer = v_chunks.pop()
+        sim_buffer = torch.einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
+
+        del k_buffer, q_buffer
+        # attention, what we cannot get enough of, by chunks
+
+        sim_buffer = sim_buffer.softmax(dim=-1)
+
+        sim_buffer = torch.einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
+        del v_buffer
+        sim[i:i + att_step, :, :] = sim_buffer
+
+        del sim_buffer
+    sim = einops.rearrange(sim, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(sim)
diff --git a/cldm/logger.py b/cldm/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a8803846f2a8979f87f3cf9ea5b12869439e62f
--- /dev/null
+++ b/cldm/logger.py
@@ -0,0 +1,76 @@
+import os
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+
+class ImageLogger(Callback):
+    def __init__(self, batch_frequency=2000, max_images=4, clamp=True, increase_log_steps=True,
+                 rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
+                 log_images_kwargs=None):
+        super().__init__()
+        self.rescale = rescale
+        self.batch_freq = batch_frequency
+        self.max_images = max_images
+        if not increase_log_steps:
+            self.log_steps = [self.batch_freq]
+        self.clamp = clamp
+        self.disabled = disabled
+        self.log_on_batch_idx = log_on_batch_idx
+        self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
+        self.log_first_step = log_first_step
+
+    @rank_zero_only
+    def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
+        root = os.path.join(save_dir, "image_log", split)
+        for k in images:
+            grid = torchvision.utils.make_grid(images[k], nrow=4)
+            if self.rescale:
+                grid = (grid + 1.0) / 2.0  # -1,1 -> 0,1; c,h,w
+            grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
+            grid = grid.numpy()
+            grid = (grid * 255).astype(np.uint8)
+            filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
+            path = os.path.join(root, filename)
+            os.makedirs(os.path.split(path)[0], exist_ok=True)
+            Image.fromarray(grid).save(path)
+
+    def log_img(self, pl_module, batch, batch_idx, split="train"):
+        check_idx = batch_idx  # if self.log_on_batch_idx else pl_module.global_step
+        if (self.check_frequency(check_idx) and  # batch_idx % self.batch_freq == 0
+                hasattr(pl_module, "log_images") and
+                callable(pl_module.log_images) and
+                self.max_images > 0):
+            logger = type(pl_module.logger)
+
+            is_train = pl_module.training
+            if is_train:
+                pl_module.eval()
+
+            with torch.no_grad():
+                images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
+
+            for k in images:
+                N = min(images[k].shape[0], self.max_images)
+                images[k] = images[k][:N]
+                if isinstance(images[k], torch.Tensor):
+                    images[k] = images[k].detach().cpu()
+                    if self.clamp:
+                        images[k] = torch.clamp(images[k], -1., 1.)
+
+            self.log_local(pl_module.logger.save_dir, split, images,
+                           pl_module.global_step, pl_module.current_epoch, batch_idx)
+
+            if is_train:
+                pl_module.train()
+
+    def check_frequency(self, check_idx):
+        return check_idx % self.batch_freq == 0
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+        if not self.disabled:
+            self.log_img(pl_module, batch, batch_idx, split="train")
diff --git a/cldm/model.py b/cldm/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..fed3c31ac145b78907c7f771d1d8db6fb32d92ed
--- /dev/null
+++ b/cldm/model.py
@@ -0,0 +1,28 @@
+import os
+import torch
+
+from omegaconf import OmegaConf
+from ldm.util import instantiate_from_config
+
+
+def get_state_dict(d):
+    return d.get('state_dict', d)
+
+
+def load_state_dict(ckpt_path, location='cpu'):
+    _, extension = os.path.splitext(ckpt_path)
+    if extension.lower() == ".safetensors":
+        import safetensors.torch
+        state_dict = safetensors.torch.load_file(ckpt_path, device=location)
+    else:
+        state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
+    state_dict = get_state_dict(state_dict)
+    print(f'Loaded state_dict from [{ckpt_path}]')
+    return state_dict
+
+
+def create_model(config_path):
+    config = OmegaConf.load(config_path)
+    model = instantiate_from_config(config.model).cpu()
+    print(f'Loaded model config from [{config_path}]')
+    return model
diff --git a/configs/gradio_i23d_stage1_args.json b/configs/gradio_i23d_stage1_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..b3805f6ba41b9ad0427bd04c08aa4d83b4bef6a1
--- /dev/null
+++ b/configs/gradio_i23d_stage1_args.json
@@ -0,0 +1,312 @@
+{
+  "dataset_size": -1,
+  "diffusion_input_size": 32,
+  "trainer_name": "flow_matching_gs",
+  "use_amp": true,
+  "train_vae": false,
+  "triplane_scaling_divider": 1.0,
+  "overfitting": false,
+  "num_workers": 1,
+  "image_size": 512,
+  "image_size_encoder": 512,
+  "iterations": 5000001,
+  "schedule_sampler": "uniform",
+  "anneal_lr": false,
+  "lr": 0.0,
+  "weight_decay": 0.05,
+  "lr_anneal_steps": 0,
+  "batch_size": 1,
+  "eval_batch_size": 1,
+  "microbatch": 1,
+  "ema_rate": "0.9999",
+  "log_interval": 100,
+  "eval_interval": 5000,
+  "save_interval": 10000,
+  "resume_checkpoint": "/mnt/sfs-common/yslan/open-source/checkpoints/i23d/stage-1/model_joint_denoise_rec_model2335000.pt",
+  "resume_checkpoint_EG3D": "",
+  "use_fp16": false,
+  "fp16_scale_growth": 0.001,
+  "data_dir": "./assets/demo-image-for-i23d/instantmesh",
+  "eval_data_dir": "./assets/demo-image-for-i23d/instantmesh",
+  "load_depth": true,
+  "logdir": "./logs/i23d/stage-1/dino_img/",
+  "load_submodule_name": "",
+  "ignore_resume_opt": false,
+  "denoised_ae": true,
+  "diffusion_ce_anneal": true,
+  "use_lmdb": false,
+  "interval": 5,
+  "freeze_triplane_decoder": false,
+  "objv_dataset": true,
+  "use_eos_feature": false,
+  "clip_grad_throld": 1.0,
+  "allow_tf32": true,
+  "save_img": false,
+  "use_train_trajectory": false,
+  "unconditional_guidance_scale": 1.0,
+  "num_samples": 2,
+  "num_instances": 10,
+  "num_channels": 320,
+  "num_res_blocks": 2,
+  "num_heads": 8,
+  "num_heads_upsample": -1,
+  "num_head_channels": -1,
+  "attention_resolutions": "4,2,1",
+  "channel_mult": "",
+  "dropout": 0.0,
+  "class_cond": false,
+  "use_checkpoint": false,
+  "use_scale_shift_norm": true,
+  "resblock_updown": false,
+  "use_new_attention_order": false,
+  "denoise_in_channels": 3,
+  "denoise_out_channels": 3,
+  "create_controlnet": false,
+  "create_dit": true,
+  "i23d": true,
+  "create_unet_with_hint": false,
+  "dit_model_arch": "DiT-PixArt-PCD-CLAY-L",
+  "use_spatial_transformer": true,
+  "transformer_depth": 1,
+  "context_dim": 1024,
+  "pooling_ctx_dim": 768,
+  "roll_out": true,
+  "n_embed": null,
+  "legacy": true,
+  "mixing_logit_init": 10000,
+  "hint_channels": 3,
+  "learn_sigma": false,
+  "diffusion_steps": 1000,
+  "noise_schedule": "linear",
+  "standarization_xt": false,
+  "timestep_respacing": "",
+  "use_kl": false,
+  "predict_xstart": false,
+  "predict_v": false,
+  "rescale_timesteps": false,
+  "rescale_learned_sigmas": false,
+  "mixed_prediction": false,
+  "sde_time_eps": 0.01,
+  "sde_beta_start": 0.1,
+  "sde_beta_end": 20.0,
+  "sde_sde_type": "vpsde",
+  "sde_sigma2_0": 0.0,
+  "iw_sample_p": "drop_sigma2t_iw",
+  "iw_sample_q": "ll_iw",
+  "iw_subvp_like_vp_sde": false,
+  "pred_type": "x_start",
+  "p_rendering_loss": false,
+  "unfix_logit": false,
+  "loss_type": "eps",
+  "loss_weight": "simple",
+  "enable_mixing_normal": false,
+  "dino_version": "mv-sd-dit-srt-pcd-structured-nopcd",
+  "encoder_in_channels": 15,
+  "img_size": [
+    512
+  ],
+  "patch_size": 14,
+  "in_chans": 384,
+  "num_classes": 0,
+  "embed_dim": 384,
+  "depth": 6,
+  "mlp_ratio": 4.0,
+  "qkv_bias": false,
+  "qk_scale": null,
+  "drop_rate": 0.1,
+  "attn_drop_rate": 0.0,
+  "drop_path_rate": 0.0,
+  "norm_layer": "nn.LayerNorm",
+  "cls_token": false,
+  "encoder_cls_token": false,
+  "decoder_cls_token": false,
+  "sr_kwargs": {},
+  "sr_ratio": 2,
+  "use_clip": false,
+  "arch_encoder": "vits",
+  "arch_decoder": "vitb",
+  "load_pretrain_encoder": false,
+  "encoder_lr": 1e-05,
+  "encoder_weight_decay": 0.001,
+  "no_dim_up_mlp": true,
+  "dim_up_mlp_as_func": false,
+  "decoder_load_pretrained": true,
+  "uvit_skip_encoder": true,
+  "vae_p": 2,
+  "ldm_z_channels": 10,
+  "ldm_embed_dim": 10,
+  "use_conf_map": false,
+  "sd_E_ch": 64,
+  "z_channels": 10,
+  "latent_num": 768,
+  "sd_E_num_res_blocks": 1,
+  "num_frames": 8,
+  "arch_dit_decoder": "DiT2-B/2",
+  "return_all_dit_layers": false,
+  "lrm_decoder": false,
+  "gs_rendering": true,
+  "surfel_rendering": true,
+  "plane_n": 1,
+  "in_plane_attention": true,
+  "vae_dit_token_size": 16,
+  "decomposed": true,
+  "triplane_fg_bg": false,
+  "flexicube_decoder": false,
+  "cfg": "objverse_tuneray_aug_resolution_64_64_auto",
+  "density_reg": 0.0,
+  "density_reg_p_dist": 0.004,
+  "reg_type": "l1",
+  "triplane_decoder_lr": 5e-05,
+  "super_resolution_lr": 5e-05,
+  "c_scale": 1,
+  "nsr_lr": 0.02,
+  "triplane_size": 224,
+  "decoder_in_chans": 32,
+  "triplane_in_chans": 32,
+  "decoder_output_dim": 3,
+  "out_chans": 96,
+  "c_dim": 25,
+  "ray_start": 0.6,
+  "ray_end": 1.8,
+  "rendering_kwargs": {
+    "image_resolution": 256,
+    "disparity_space_sampling": false,
+    "clamp_mode": "softplus",
+    "c_gen_conditioning_zero": true,
+    "c_scale": 1,
+    "superresolution_noise_mode": "none",
+    "density_reg": 0.0,
+    "density_reg_p_dist": 0.004,
+    "reg_type": "l1",
+    "decoder_lr_mul": 1,
+    "decoder_activation": "sigmoid",
+    "sr_antialias": true,
+    "return_triplane_features": false,
+    "return_sampling_details_flag": true,
+    "superresolution_module": "torch_utils.components.NearestConvSR",
+    "depth_resolution": 64,
+    "depth_resolution_importance": 64,
+    "ray_start": "auto",
+    "ray_end": "auto",
+    "box_warp": 0.9,
+    "white_back": true,
+    "radius_range": [
+      1.5,
+      2
+    ],
+    "sampler_bbox_min": -0.45,
+    "sampler_bbox_max": 0.45,
+    "filter_out_of_bbox": true,
+    "PatchRaySampler": true,
+    "patch_rendering_resolution": 45,
+    "z_near": 1.05,
+    "z_far": 2.45
+  },
+  "sr_training": false,
+  "bcg_synthesis": false,
+  "bcg_synthesis_kwargs": {},
+  "patch_rendering_resolution": 45,
+  "vit_decoder_lr": 1e-05,
+  "vit_decoder_wd": 0.001,
+  "ae_classname": "vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded",
+  "use_wds": false,
+  "use_lmdb_compressed": false,
+  "compile": false,
+  "decode_encode_img_only": false,
+  "load_wds_diff": false,
+  "load_wds_latent": false,
+  "eval_load_wds_instance": true,
+  "shards_lst": "",
+  "eval_shards_lst": "",
+  "mv_input": true,
+  "duplicate_sample": false,
+  "orthog_duplicate": false,
+  "split_chunk_input": true,
+  "load_real": true,
+  "load_mv_real": false,
+  "load_gso": true,
+  "four_view_for_latent": false,
+  "single_view_for_i23d": false,
+  "shuffle_across_cls": true,
+  "load_extra_36_view": false,
+  "mv_latent_dir": "",
+  "append_depth": false,
+  "append_xyz": true,
+  "read_normal": true,
+  "plucker_embedding": true,
+  "perturb_pcd_scale": 0.0,
+  "gs_cam_format": true,
+  "frame_0_as_canonical": false,
+  "pcd_path": "",
+  "stage_1_output_dir": "",
+  "load_pcd": true,
+  "use_chunk": false,
+  "split_chunk_size": 16,
+  "load_caption_dataset": false,
+  "load_mv_dataset": false,
+  "export_mesh": false,
+  "color_criterion": "mse",
+  "l2_lambda": 1.0,
+  "lpips_lambda": 0.8,
+  "lpips_delay_iter": 0,
+  "sr_delay_iter": 0,
+  "kl_anneal": false,
+  "latent_lambda": 0.0,
+  "latent_criterion": "mse",
+  "kl_lambda": 0.0,
+  "pt_ft_kl": false,
+  "ft_kl": false,
+  "ssim_lambda": 0.0,
+  "l1_lambda": 0.0,
+  "id_lambda": 0.0,
+  "depth_lambda": 0.0,
+  "alpha_lambda": 1.0,
+  "fg_mse": false,
+  "bg_lamdba": 0.01,
+  "density_reg_every": 4,
+  "shape_uniform_lambda": 0.005,
+  "shape_importance_lambda": 0.01,
+  "shape_depth_lambda": 0.0,
+  "xyz_lambda": 0.0,
+  "emd_lambda": 0.0,
+  "cd_lambda": 0.0,
+  "pruning_ot_lambda": 0.0,
+  "lambda_normal": 0.0,
+  "lambda_dist": 0.0,
+  "lambda_scale_reg": 0.0,
+  "lambda_opa_reg": 0.0,
+  "rec_cvD_lambda": 0.01,
+  "nvs_cvD_lambda": 0.025,
+  "patchgan_disc_factor": 0.01,
+  "patchgan_disc_g_weight": 0.2,
+  "r1_gamma": 1.0,
+  "sds_lamdba": 1.0,
+  "nvs_D_lr_mul": 1,
+  "cano_D_lr_mul": 1,
+  "ce_balanced_kl": 1.0,
+  "p_eps_lambda": 1,
+  "symmetry_loss": false,
+  "depth_smoothness_lambda": 0.0,
+  "ce_lambda": 0.0,
+  "negative_entropy_lambda": 0.0,
+  "grad_clip": true,
+  "online_mask": false,
+  "fps_sampling": false,
+  "subset_fps_sampling": false,
+  "subset_half_fps_sampling": false,
+  "commitment_loss_lambda": 0.0,
+  "rand_aug_bg": false,
+  "only_mid_control": false,
+  "control_key": "img",
+  "normalize_clip_encoding": true,
+  "scale_clip_encoding": 1.0,
+  "cfg_dropout_prob": 0.1,
+  "cond_key": "img",
+  "path_type": "Linear",
+  "prediction": "velocity",
+  "sample_eps": null,
+  "train_eps": null,
+  "snr_type": "img-uniform-gvp-dino",
+  "local_rank": 0,
+  "gpus": 1
+}
\ No newline at end of file
diff --git a/configs/gradio_i23d_stage2_args.json b/configs/gradio_i23d_stage2_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..ffa9296f1b7c04274d7fe86ae810d06e9c2a3889
--- /dev/null
+++ b/configs/gradio_i23d_stage2_args.json
@@ -0,0 +1,312 @@
+{
+  "dataset_size": -1,
+  "diffusion_input_size": 32,
+  "trainer_name": "flow_matching_gs",
+  "use_amp": true,
+  "train_vae": false,
+  "triplane_scaling_divider": 1.0,
+  "overfitting": false,
+  "num_workers": 2,
+  "image_size": 512,
+  "image_size_encoder": 512,
+  "iterations": 5000001,
+  "schedule_sampler": "uniform",
+  "anneal_lr": false,
+  "lr": 0.0001,
+  "weight_decay": 0.05,
+  "lr_anneal_steps": 0,
+  "batch_size": 1,
+  "eval_batch_size": 1,
+  "microbatch": 0,
+  "ema_rate": "0.9999",
+  "log_interval": 100,
+  "eval_interval": 5000,
+  "save_interval": 10000,
+  "resume_checkpoint": "/mnt/sfs-common/yslan/open-source/checkpoints/i23d/stage-2/model_joint_denoise_rec_model2505000.pt",
+  "resume_checkpoint_EG3D": "",
+  "use_fp16": false,
+  "fp16_scale_growth": 0.001,
+  "data_dir": "./assets/demo-image-for-i23d/instantmesh",
+  "eval_data_dir": "./assets/demo-image-for-i23d/instantmesh",
+  "load_depth": true,
+  "logdir": "./logs/i23d/stage-2/dino_img/",
+  "load_submodule_name": "",
+  "ignore_resume_opt": false,
+  "denoised_ae": true,
+  "diffusion_ce_anneal": true,
+  "use_lmdb": false,
+  "interval": 5,
+  "freeze_triplane_decoder": false,
+  "objv_dataset": true,
+  "use_eos_feature": false,
+  "clip_grad_throld": 1.0,
+  "allow_tf32": true,
+  "save_img": false,
+  "use_train_trajectory": false,
+  "unconditional_guidance_scale": 1.0,
+  "num_samples": 4,
+  "num_instances": 10,
+  "num_channels": 320,
+  "num_res_blocks": 2,
+  "num_heads": 8,
+  "num_heads_upsample": -1,
+  "num_head_channels": -1,
+  "attention_resolutions": "4,2,1",
+  "channel_mult": "",
+  "dropout": 0.0,
+  "class_cond": false,
+  "use_checkpoint": false,
+  "use_scale_shift_norm": true,
+  "resblock_updown": false,
+  "use_new_attention_order": false,
+  "denoise_in_channels": 10,
+  "denoise_out_channels": 10,
+  "create_controlnet": false,
+  "create_dit": true,
+  "i23d": true,
+  "create_unet_with_hint": false,
+  "dit_model_arch": "DiT-PixArt-PCD-CLAY-stage2-L",
+  "use_spatial_transformer": true,
+  "transformer_depth": 1,
+  "context_dim": 1024,
+  "pooling_ctx_dim": 768,
+  "roll_out": true,
+  "n_embed": null,
+  "legacy": true,
+  "mixing_logit_init": 10000,
+  "hint_channels": 3,
+  "learn_sigma": false,
+  "diffusion_steps": 1000,
+  "noise_schedule": "linear",
+  "standarization_xt": false,
+  "timestep_respacing": "",
+  "use_kl": false,
+  "predict_xstart": false,
+  "predict_v": false,
+  "rescale_timesteps": false,
+  "rescale_learned_sigmas": false,
+  "mixed_prediction": false,
+  "sde_time_eps": 0.01,
+  "sde_beta_start": 0.1,
+  "sde_beta_end": 20.0,
+  "sde_sde_type": "vpsde",
+  "sde_sigma2_0": 0.0,
+  "iw_sample_p": "drop_sigma2t_iw",
+  "iw_sample_q": "ll_iw",
+  "iw_subvp_like_vp_sde": false,
+  "pred_type": "x_start",
+  "p_rendering_loss": false,
+  "unfix_logit": false,
+  "loss_type": "eps",
+  "loss_weight": "simple",
+  "enable_mixing_normal": false,
+  "dino_version": "mv-sd-dit-srt-pcd-structured-nopcd",
+  "encoder_in_channels": 15,
+  "img_size": [
+    512
+  ],
+  "patch_size": 14,
+  "in_chans": 384,
+  "num_classes": 0,
+  "embed_dim": 384,
+  "depth": 6,
+  "mlp_ratio": 4.0,
+  "qkv_bias": false,
+  "qk_scale": null,
+  "drop_rate": 0.1,
+  "attn_drop_rate": 0.0,
+  "drop_path_rate": 0.0,
+  "norm_layer": "nn.LayerNorm",
+  "cls_token": false,
+  "encoder_cls_token": false,
+  "decoder_cls_token": false,
+  "sr_kwargs": {},
+  "sr_ratio": 2,
+  "use_clip": false,
+  "arch_encoder": "vits",
+  "arch_decoder": "vitb",
+  "load_pretrain_encoder": false,
+  "encoder_lr": 1e-05,
+  "encoder_weight_decay": 0.001,
+  "no_dim_up_mlp": true,
+  "dim_up_mlp_as_func": false,
+  "decoder_load_pretrained": true,
+  "uvit_skip_encoder": true,
+  "vae_p": 2,
+  "ldm_z_channels": 10,
+  "ldm_embed_dim": 10,
+  "use_conf_map": false,
+  "sd_E_ch": 64,
+  "z_channels": 10,
+  "latent_num": 768,
+  "sd_E_num_res_blocks": 1,
+  "num_frames": 8,
+  "arch_dit_decoder": "DiT2-B/2",
+  "return_all_dit_layers": false,
+  "lrm_decoder": false,
+  "gs_rendering": true,
+  "surfel_rendering": true,
+  "plane_n": 1,
+  "in_plane_attention": true,
+  "vae_dit_token_size": 16,
+  "decomposed": true,
+  "triplane_fg_bg": false,
+  "flexicube_decoder": false,
+  "cfg": "objverse_tuneray_aug_resolution_64_64_auto",
+  "density_reg": 0.0,
+  "density_reg_p_dist": 0.004,
+  "reg_type": "l1",
+  "triplane_decoder_lr": 5e-05,
+  "super_resolution_lr": 5e-05,
+  "c_scale": 1,
+  "nsr_lr": 0.02,
+  "triplane_size": 224,
+  "decoder_in_chans": 32,
+  "triplane_in_chans": 32,
+  "decoder_output_dim": 3,
+  "out_chans": 96,
+  "c_dim": 25,
+  "ray_start": 0.6,
+  "ray_end": 1.8,
+  "rendering_kwargs": {
+    "image_resolution": 256,
+    "disparity_space_sampling": false,
+    "clamp_mode": "softplus",
+    "c_gen_conditioning_zero": true,
+    "c_scale": 1,
+    "superresolution_noise_mode": "none",
+    "density_reg": 0.0,
+    "density_reg_p_dist": 0.004,
+    "reg_type": "l1",
+    "decoder_lr_mul": 1,
+    "decoder_activation": "sigmoid",
+    "sr_antialias": true,
+    "return_triplane_features": false,
+    "return_sampling_details_flag": true,
+    "superresolution_module": "torch_utils.components.NearestConvSR",
+    "depth_resolution": 64,
+    "depth_resolution_importance": 64,
+    "ray_start": "auto",
+    "ray_end": "auto",
+    "box_warp": 0.9,
+    "white_back": true,
+    "radius_range": [
+      1.5,
+      2
+    ],
+    "sampler_bbox_min": -0.45,
+    "sampler_bbox_max": 0.45,
+    "filter_out_of_bbox": true,
+    "PatchRaySampler": true,
+    "patch_rendering_resolution": 45,
+    "z_near": 1.05,
+    "z_far": 2.45
+  },
+  "sr_training": false,
+  "bcg_synthesis": false,
+  "bcg_synthesis_kwargs": {},
+  "patch_rendering_resolution": 45,
+  "vit_decoder_lr": 1e-05,
+  "vit_decoder_wd": 0.001,
+  "ae_classname": "vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded",
+  "use_wds": false,
+  "use_lmdb_compressed": false,
+  "compile": false,
+  "decode_encode_img_only": false,
+  "load_wds_diff": false,
+  "load_wds_latent": false,
+  "eval_load_wds_instance": true,
+  "shards_lst": "",
+  "eval_shards_lst": "",
+  "mv_input": true,
+  "duplicate_sample": false,
+  "orthog_duplicate": false,
+  "split_chunk_input": true,
+  "load_real": true,
+  "load_mv_real": false,
+  "load_gso": true,
+  "four_view_for_latent": false,
+  "single_view_for_i23d": false,
+  "shuffle_across_cls": true,
+  "load_extra_36_view": false,
+  "mv_latent_dir": "",
+  "append_depth": false,
+  "append_xyz": true,
+  "read_normal": true,
+  "plucker_embedding": true,
+  "perturb_pcd_scale": 0.0,
+  "gs_cam_format": true,
+  "frame_0_as_canonical": false,
+  "pcd_path": "/cpfs01/user/lanyushi.p/data/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd/",
+  "stage_1_output_dir": "./logs/i23d/stage-1/dino_img/",
+  "load_pcd": true,
+  "use_chunk": false,
+  "split_chunk_size": 16,
+  "load_caption_dataset": false,
+  "load_mv_dataset": false,
+  "export_mesh": false,
+  "color_criterion": "mse",
+  "l2_lambda": 1.0,
+  "lpips_lambda": 0.8,
+  "lpips_delay_iter": 0,
+  "sr_delay_iter": 0,
+  "kl_anneal": false,
+  "latent_lambda": 0.0,
+  "latent_criterion": "mse",
+  "kl_lambda": 0.0,
+  "pt_ft_kl": false,
+  "ft_kl": false,
+  "ssim_lambda": 0.0,
+  "l1_lambda": 0.0,
+  "id_lambda": 0.0,
+  "depth_lambda": 0.0,
+  "alpha_lambda": 1.0,
+  "fg_mse": false,
+  "bg_lamdba": 0.01,
+  "density_reg_every": 4,
+  "shape_uniform_lambda": 0.005,
+  "shape_importance_lambda": 0.01,
+  "shape_depth_lambda": 0.0,
+  "xyz_lambda": 0.0,
+  "emd_lambda": 0.0,
+  "cd_lambda": 0.0,
+  "pruning_ot_lambda": 0.0,
+  "lambda_normal": 0.0,
+  "lambda_dist": 0.0,
+  "lambda_scale_reg": 0.0,
+  "lambda_opa_reg": 0.0,
+  "rec_cvD_lambda": 0.01,
+  "nvs_cvD_lambda": 0.025,
+  "patchgan_disc_factor": 0.01,
+  "patchgan_disc_g_weight": 0.2,
+  "r1_gamma": 1.0,
+  "sds_lamdba": 1.0,
+  "nvs_D_lr_mul": 1,
+  "cano_D_lr_mul": 1,
+  "ce_balanced_kl": 1.0,
+  "p_eps_lambda": 1,
+  "symmetry_loss": false,
+  "depth_smoothness_lambda": 0.0,
+  "ce_lambda": 0.0,
+  "negative_entropy_lambda": 0.0,
+  "grad_clip": true,
+  "online_mask": false,
+  "fps_sampling": false,
+  "subset_fps_sampling": false,
+  "subset_half_fps_sampling": false,
+  "commitment_loss_lambda": 0.0,
+  "rand_aug_bg": false,
+  "only_mid_control": false,
+  "control_key": "img",
+  "normalize_clip_encoding": true,
+  "scale_clip_encoding": 1.0,
+  "cfg_dropout_prob": 0.1,
+  "cond_key": "img-xyz",
+  "path_type": "Linear",
+  "prediction": "velocity",
+  "sample_eps": null,
+  "train_eps": null,
+  "snr_type": "img-uniform-gvp-dino-stage2",
+  "local_rank": 0,
+  "gpus": 1
+}
\ No newline at end of file
diff --git a/datasets/README.md b/datasets/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..336b8e83262764419aceae9c975c58bed0fbb47b
--- /dev/null
+++ b/datasets/README.md
@@ -0,0 +1,27 @@
+# Downloading datasets
+
+This directory includes instructions and scripts for downloading ImageNet and LSUN bedrooms for use in this codebase.
+
+## Class-conditional ImageNet
+
+For our class-conditional models, we use the official ILSVRC2012 dataset with manual center cropping and downsampling. To obtain this dataset, navigate to [this page on image-net.org](http://www.image-net.org/challenges/LSVRC/2012/downloads) and sign in (or create an account if you do not already have one). Then click on the link reading "Training images (Task 1 & 2)". This is a 138GB tar file containing 1000 sub-tar files, one per class.
+
+Once the file is downloaded, extract it and look inside. You should see 1000 `.tar` files. You need to extract each of these, which may be impractical to do by hand on your operating system. To automate the process on a Unix-based system, you can `cd` into the directory and run this short shell script:
+
+```
+for file in *.tar; do tar xf "$file"; rm "$file"; done
+```
+
+This will extract and remove each tar file in turn.
+
+Once all of the images have been extracted, the resulting directory should be usable as a data directory (the `--data_dir` argument for the training script). The filenames should all start with WNID (class ids) followed by underscores, like `n01440764_2708.JPEG`. Conveniently (but not by accident) this is how the automated data-loader expects to discover class labels.
+
+## LSUN bedroom
+
+To download and pre-process LSUN bedroom, clone [fyu/lsun](https://github.com/fyu/lsun) on GitHub and run their download script `python3 download.py bedroom`. The result will be an "lmdb" database named like `bedroom_train_lmdb`. You can pass this to our [lsun_bedroom.py](lsun_bedroom.py) script like so:
+
+```
+python lsun_bedroom.py bedroom_train_lmdb lsun_train_output_dir
+```
+
+This creates a directory called `lsun_train_output_dir`. This directory can be passed to the training scripts via the `--data_dir` argument.
diff --git a/datasets/__init__.py b/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bff2ea70a118ab9bba3645fbf2de42187d73c1d
--- /dev/null
+++ b/datasets/__init__.py
@@ -0,0 +1,2 @@
+from . import g_buffer_objaverse
+from . import shapenet
\ No newline at end of file
diff --git a/datasets/__pycache__/__init__.cpython-39.pyc b/datasets/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..68ca17d43f9a3f027f8f98e198291fe390899fe0
Binary files /dev/null and b/datasets/__pycache__/__init__.cpython-39.pyc differ
diff --git a/datasets/__pycache__/eg3d_dataset.cpython-39.pyc b/datasets/__pycache__/eg3d_dataset.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..68ceab7814f1baafd817c7ae006da82e87624596
Binary files /dev/null and b/datasets/__pycache__/eg3d_dataset.cpython-39.pyc differ
diff --git a/datasets/__pycache__/g_buffer_objaverse.cpython-39.pyc b/datasets/__pycache__/g_buffer_objaverse.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8d3818033ceb5bdcb878afa21d7a2050c5b8f4b0
Binary files /dev/null and b/datasets/__pycache__/g_buffer_objaverse.cpython-39.pyc differ
diff --git a/datasets/__pycache__/shapenet.cpython-39.pyc b/datasets/__pycache__/shapenet.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..32039e600121e192e12adfe4af4c29568f0a0a89
Binary files /dev/null and b/datasets/__pycache__/shapenet.cpython-39.pyc differ
diff --git a/datasets/caption-forpaper.txt b/datasets/caption-forpaper.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a6922ca6fb023f4529f8e766a6cc07cb2d4bd7a
--- /dev/null
+++ b/datasets/caption-forpaper.txt
@@ -0,0 +1,21 @@
+An 18th century Cannon
+Mech Suit
+A voxelized dog
+Giraffe
+Astral Beacon
+low-poly model of a green pine tree, also resembling a Christmas tree.
+low poly tree model with green leaves.
+Fire Hydrants
+low poly blue chess piece model.
+A yellow rubber duck with red mouth.
+low poly blue chess piece model.
+A brown wooden table with a rectangular shape, featuring visible legs and vintage look.
+a sword with a red handle.
+A cute and friendly pink teddy bear with sitting pose.
+White Giraffe
+a police car
+an orchid flower planted in a clay pot
+a palm tree, low poly 3d model
+a plate of delicious tacos
+a red eyed tree frog, low poly
+an amigurumi bulldozer
\ No newline at end of file
diff --git a/datasets/capton-for-pointe.txt b/datasets/capton-for-pointe.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e3ed971853afcce4a152e8aebf546b68afa24c80
--- /dev/null
+++ b/datasets/capton-for-pointe.txt
@@ -0,0 +1 @@
+low poly blue chess piece model.
\ No newline at end of file
diff --git a/datasets/eg3d_dataset.py b/datasets/eg3d_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..7799306fbde1eb29029b88e5e31dfdd56f63991d
--- /dev/null
+++ b/datasets/eg3d_dataset.py
@@ -0,0 +1,601 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Streaming images and labels from datasets created with dataset_tool.py."""
+
+import cv2
+import os
+import numpy as np
+import zipfile
+import PIL.Image
+import json
+import torch
+import dnnlib
+from torchvision import transforms
+
+from pdb import set_trace as st
+
+from .shapenet import LMDBDataset_MV_Compressed, decompress_array
+
+try:
+    import pyspng
+except ImportError:
+    pyspng = None
+
+#----------------------------------------------------------------------------
+
+
+# copide from eg3d/train.py
+def init_dataset_kwargs(data,
+                        class_name='datasets.eg3d_dataset.ImageFolderDataset',
+                        reso_gt=128):
+    # try:
+    # if data == 'None':
+    #     dataset_kwargs = dnnlib.EasyDict({})  #
+    #     dataset_kwargs.name = 'eg3d_dataset'
+    #     dataset_kwargs.resolution = 128
+    #     dataset_kwargs.use_labels = False
+    #     dataset_kwargs.max_size = 70000
+    #     return dataset_kwargs, 'eg3d_dataset'
+
+    dataset_kwargs = dnnlib.EasyDict(class_name=class_name,
+                                     reso_gt=reso_gt,
+                                     path=data,
+                                     use_labels=True,
+                                     max_size=None,
+                                     xflip=False)
+    dataset_obj = dnnlib.util.construct_class_by_name(
+        **dataset_kwargs)  # Subclass of training.dataset.Dataset.
+    dataset_kwargs.resolution = dataset_obj.resolution  # Be explicit about resolution.
+    dataset_kwargs.use_labels = dataset_obj.has_labels  # Be explicit about labels.
+    dataset_kwargs.max_size = len(
+        dataset_obj)  # Be explicit about dataset size.
+
+    return dataset_kwargs, dataset_obj.name
+    # except IOError as err:
+    #     raise click.ClickException(f'--data: {err}')
+
+
+class Dataset(torch.utils.data.Dataset):
+
+    def __init__(
+            self,
+            name,  # Name of the dataset.
+            raw_shape,  # Shape of the raw image data (NCHW).
+            reso_gt=128,
+            max_size=None,  # Artificially limit the size of the dataset. None = no limit. Applied before xflip.
+            use_labels=False,  # Enable conditioning labels? False = label dimension is zero.
+            xflip=False,  # Artificially double the size of the dataset via x-flips. Applied after max_size.
+            random_seed=0,  # Random seed to use when applying max_size.
+    ):
+        self._name = name
+        self._raw_shape = list(raw_shape)
+        self._use_labels = use_labels
+        self._raw_labels = None
+        self._label_shape = None
+
+        # self.reso_gt = 128
+        self.reso_gt = reso_gt  # ! hard coded
+        self.reso_encoder = 224
+
+        # Apply max_size.
+        self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64)
+        # self._raw_idx = np.arange(self.__len__(), dtype=np.int64)
+        if (max_size is not None) and (self._raw_idx.size > max_size):
+            np.random.RandomState(random_seed).shuffle(self._raw_idx)
+            self._raw_idx = np.sort(self._raw_idx[:max_size])
+
+        # Apply xflip.
+        self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8)
+        if xflip:
+            self._raw_idx = np.tile(self._raw_idx, 2)
+            self._xflip = np.concatenate(
+                [self._xflip, np.ones_like(self._xflip)])
+
+        # dino encoder normalizer
+        self.normalize_for_encoder_input = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+            transforms.Resize(size=(self.reso_encoder, self.reso_encoder),
+                              antialias=True),  # type: ignore
+        ])
+
+        self.normalize_for_gt = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            transforms.Resize(size=(self.reso_gt, self.reso_gt),
+                              antialias=True),  # type: ignore
+        ])
+
+    def _get_raw_labels(self):
+        if self._raw_labels is None:
+            self._raw_labels = self._load_raw_labels(
+            ) if self._use_labels else None
+            if self._raw_labels is None:
+                self._raw_labels = np.zeros([self._raw_shape[0], 0],
+                                            dtype=np.float32)
+            assert isinstance(self._raw_labels, np.ndarray)
+            # assert self._raw_labels.shape[0] == self._raw_shape[0]
+            assert self._raw_labels.dtype in [np.float32, np.int64]
+            if self._raw_labels.dtype == np.int64:
+                assert self._raw_labels.ndim == 1
+                assert np.all(self._raw_labels >= 0)
+            self._raw_labels_std = self._raw_labels.std(0)
+        return self._raw_labels
+
+    def close(self):  # to be overridden by subclass
+        pass
+
+    def _load_raw_image(self, raw_idx):  # to be overridden by subclass
+        raise NotImplementedError
+
+    def _load_raw_labels(self):  # to be overridden by subclass
+        raise NotImplementedError
+
+    def __getstate__(self):
+        return dict(self.__dict__, _raw_labels=None)
+
+    def __del__(self):
+        try:
+            self.close()
+        except:
+            pass
+
+    def __len__(self):
+        return self._raw_idx.size
+        # return self._get_raw_labels().shape[0]
+
+    def __getitem__(self, idx):
+        # print(self._raw_idx[idx], idx)
+
+        matte = self._load_raw_matte(self._raw_idx[idx])
+        assert isinstance(matte, np.ndarray)
+        assert list(matte.shape)[1:] == self.image_shape[1:]
+        if self._xflip[idx]:
+            assert matte.ndim == 1  # CHW
+            matte = matte[:, :, ::-1]
+        # matte_orig = matte.copy().astype(np.float32) / 255
+        matte_orig = matte.copy().astype(np.float32) # segmentation version
+        # assert matte_orig.max() == 1
+        matte = np.transpose(matte,
+                            #  (1, 2, 0)).astype(np.float32) / 255  # [0,1] range
+                             (1, 2, 0)).astype(np.float32)  # [0,1] range
+        matte = cv2.resize(matte, (self.reso_gt, self.reso_gt),
+                           interpolation=cv2.INTER_NEAREST)
+        assert matte.min() >= 0 and matte.max(
+        ) <= 1, f'{matte.min(), matte.max()}'
+
+        if matte.ndim == 3:  # H, W
+            matte = matte[..., 0]
+
+        image = self._load_raw_image(self._raw_idx[idx])
+
+        assert isinstance(image, np.ndarray)
+        assert list(image.shape) == self.image_shape
+        assert image.dtype == np.uint8
+        if self._xflip[idx]:
+            assert image.ndim == 3  # CHW
+            image = image[:, :, ::-1]
+
+        # blending
+        # blending = True
+        blending = False
+        if blending:
+            image = image * matte_orig + (1 - matte_orig) * cv2.GaussianBlur(
+                image, (5, 5), cv2.BORDER_DEFAULT)
+            # image = image * matte_orig
+
+        image = np.transpose(image, (1, 2, 0)).astype(
+            np.float32
+        ) / 255  # H W C for torchvision process, normalize to [0,1]
+
+        image_sr = torch.from_numpy(image)[..., :3].permute(
+            2, 0, 1) * 2 - 1  # normalize to [-1,1]
+        image_to_encoder = self.normalize_for_encoder_input(image)
+
+        image_gt = cv2.resize(image, (self.reso_gt, self.reso_gt),
+                              interpolation=cv2.INTER_AREA)
+        image_gt = torch.from_numpy(image_gt)[..., :3].permute(
+            2, 0, 1) * 2 - 1  # normalize to [-1,1]
+
+        return dict(
+            c=self.get_label(idx),
+            img_to_encoder=image_to_encoder,  # 224
+            img_sr=image_sr,  # 512
+            img=image_gt,  # [-1,1] range
+            # depth=torch.zeros_like(image_gt)[0, ...] # type: ignore
+            depth=matte,
+            depth_mask=matte,
+            # depth_mask=matte > 0,
+            # alpha=matte,
+        )  # return dict here
+
+    def get_label(self, idx):
+        label = self._get_raw_labels()[self._raw_idx[idx]]
+        if label.dtype == np.int64:
+            onehot = np.zeros(self.label_shape, dtype=np.float32)
+            onehot[label] = 1
+            label = onehot
+        return label.copy()
+
+    def get_details(self, idx):
+        d = dnnlib.EasyDict()
+        d.raw_idx = int(self._raw_idx[idx])
+        d.xflip = (int(self._xflip[idx]) != 0)
+        d.raw_label = self._get_raw_labels()[d.raw_idx].copy()
+        return d
+
+    def get_label_std(self):
+        return self._raw_labels_std
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def image_shape(self):
+        return list(self._raw_shape[1:])
+
+    @property
+    def num_channels(self):
+        assert len(self.image_shape) == 3  # CHW
+        return self.image_shape[0]
+
+    @property
+    def resolution(self):
+        assert len(self.image_shape) == 3  # CHW
+        assert self.image_shape[1] == self.image_shape[2]
+        return self.image_shape[1]
+
+    @property
+    def label_shape(self):
+        if self._label_shape is None:
+            raw_labels = self._get_raw_labels()
+            if raw_labels.dtype == np.int64:
+                self._label_shape = [int(np.max(raw_labels)) + 1]
+            else:
+                self._label_shape = raw_labels.shape[1:]
+        return list(self._label_shape)
+
+    @property
+    def label_dim(self):
+        assert len(self.label_shape) == 1
+        return self.label_shape[0]
+
+    @property
+    def has_labels(self):
+        return any(x != 0 for x in self.label_shape)
+
+    @property
+    def has_onehot_labels(self):
+        return self._get_raw_labels().dtype == np.int64
+
+
+#----------------------------------------------------------------------------
+
+
+class ImageFolderDataset(Dataset):
+
+    def __init__(
+            self,
+            path,  # Path to directory or zip.
+            resolution=None,  # Ensure specific resolution, None = highest available.
+            reso_gt=128,
+            **super_kwargs,  # Additional arguments for the Dataset base class.
+    ):
+        self._path = path
+        self._matte_path = path.replace('unzipped_ffhq_512',
+                                        'unzipped_ffhq_matte')
+        # self._matte_path = path.replace('unzipped_ffhq_512',
+        #                                 'ffhq_512_seg')
+        self._zipfile = None
+
+        if os.path.isdir(self._path):
+            self._type = 'dir'
+            self._all_fnames = {
+                os.path.relpath(os.path.join(root, fname), start=self._path)
+                for root, _dirs, files in os.walk(self._path)
+                for fname in files
+            }
+        elif self._file_ext(self._path) == '.zip':
+            self._type = 'zip'
+            self._all_fnames = set(self._get_zipfile().namelist())
+        else:
+            raise IOError('Path must point to a directory or zip')
+
+        PIL.Image.init()
+        self._image_fnames = sorted(
+            fname for fname in self._all_fnames
+            if self._file_ext(fname) in PIL.Image.EXTENSION)
+        if len(self._image_fnames) == 0:
+            raise IOError('No image files found in the specified path')
+
+        name = os.path.splitext(os.path.basename(self._path))[0]
+        raw_shape = [len(self._image_fnames)] + list(
+            self._load_raw_image(0).shape)
+        # raw_shape = [len(self._image_fnames)] + list(
+        #     self._load_raw_image(0).shape)
+        if resolution is not None and (raw_shape[2] != resolution
+                                       or raw_shape[3] != resolution):
+            raise IOError('Image files do not match the specified resolution')
+        super().__init__(name=name,
+                         raw_shape=raw_shape,
+                         reso_gt=reso_gt,
+                         **super_kwargs)
+
+    @staticmethod
+    def _file_ext(fname):
+        return os.path.splitext(fname)[1].lower()
+
+    def _get_zipfile(self):
+        assert self._type == 'zip'
+        if self._zipfile is None:
+            self._zipfile = zipfile.ZipFile(self._path)
+        return self._zipfile
+
+    def _open_file(self, fname):
+        if self._type == 'dir':
+            return open(os.path.join(self._path, fname), 'rb')
+        if self._type == 'zip':
+            return self._get_zipfile().open(fname, 'r')
+        return None
+
+    def _open_matte_file(self, fname):
+        if self._type == 'dir':
+            return open(os.path.join(self._matte_path, fname), 'rb')
+        # if self._type == 'zip':
+        #     return self._get_zipfile().open(fname, 'r')
+        # return None
+
+    def close(self):
+        try:
+            if self._zipfile is not None:
+                self._zipfile.close()
+        finally:
+            self._zipfile = None
+
+    def __getstate__(self):
+        return dict(super().__getstate__(), _zipfile=None)
+
+    def _load_raw_image(self, raw_idx):
+        fname = self._image_fnames[raw_idx]
+        with self._open_file(fname) as f:
+            if pyspng is not None and self._file_ext(fname) == '.png':
+                image = pyspng.load(f.read())
+            else:
+                image = np.array(PIL.Image.open(f))
+        if image.ndim == 2:
+            image = image[:, :, np.newaxis]  # HW => HWC
+        image = image.transpose(2, 0, 1)  # HWC => CHW
+        return image
+
+    def _load_raw_matte(self, raw_idx):
+        # ! from seg version
+        fname = self._image_fnames[raw_idx]
+        with self._open_matte_file(fname) as f:
+            if pyspng is not None and self._file_ext(fname) == '.png':
+                image = pyspng.load(f.read())
+            else:
+                image = np.array(PIL.Image.open(f))
+        # if image.max() != 1:
+        image = (image > 0).astype(np.float32) # process segmentation
+        if image.ndim == 2:
+            image = image[:, :, np.newaxis]  # HW => HWC
+        image = image.transpose(2, 0, 1)  # HWC => CHW
+        return image
+
+    def _load_raw_matte_orig(self, raw_idx):
+        fname = self._image_fnames[raw_idx]
+        with self._open_matte_file(fname) as f:
+            if pyspng is not None and self._file_ext(fname) == '.png':
+                image = pyspng.load(f.read())
+            else:
+                image = np.array(PIL.Image.open(f))
+        st() # process segmentation
+        if image.ndim == 2:
+            image = image[:, :, np.newaxis]  # HW => HWC
+        image = image.transpose(2, 0, 1)  # HWC => CHW
+        return image
+
+    def _load_raw_labels(self):
+        fname = 'dataset.json'
+        if fname not in self._all_fnames:
+            return None
+        with self._open_file(fname) as f:
+            # st()
+            labels = json.load(f)['labels']
+        if labels is None:
+            return None
+        labels = dict(labels)
+        labels_ = []
+        for fname, _ in labels.items():
+            # if 'mirror' not in fname:
+            labels_.append(labels[fname])
+        labels = labels_
+        # !
+        # labels = [
+        #     labels[fname.replace('\\', '/')] for fname in self._image_fnames
+        # ]
+        labels = np.array(labels)
+        labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])
+        self._raw_labels = labels
+        return labels
+
+
+#----------------------------------------------------------------------------
+
+
+# class ImageFolderDatasetUnzipped(ImageFolderDataset):
+
+#     def __init__(self, path, resolution=None, **super_kwargs):
+#         super().__init__(path, resolution, **super_kwargs)
+
+
+# class ImageFolderDatasetPose(ImageFolderDataset):
+
+#     def __init__(
+#             self,
+#             path,  # Path to directory or zip.
+#             resolution=None,  # Ensure specific resolution, None = highest available.
+#             **super_kwargs,  # Additional arguments for the Dataset base class.
+#     ):
+#         super().__init__(path, resolution, **super_kwargs)
+#         # only return labels
+
+#     def __len__(self):
+#         return self._raw_idx.size
+#         # return self._get_raw_labels().shape[0]
+
+#     def __getitem__(self, idx):
+#         # image = self._load_raw_image(self._raw_idx[idx])
+#         # assert isinstance(image, np.ndarray)
+#         # assert list(image.shape) == self.image_shape
+#         # assert image.dtype == np.uint8
+#         # if self._xflip[idx]:
+#         # assert image.ndim == 3  # CHW
+#         # image = image[:, :, ::-1]
+#         return dict(c=self.get_label(idx), )  # return dict here
+
+
+class ImageFolderDatasetLMDB(ImageFolderDataset):
+    def __init__(self, path, resolution=None, reso_gt=128, **super_kwargs):
+        super().__init__(path, resolution, reso_gt, **super_kwargs)
+    
+    def __getitem__(self, idx):
+        # print(self._raw_idx[idx], idx)
+
+        matte = self._load_raw_matte(self._raw_idx[idx])
+        assert isinstance(matte, np.ndarray)
+        assert list(matte.shape)[1:] == self.image_shape[1:]
+        if self._xflip[idx]:
+            assert matte.ndim == 1  # CHW
+            matte = matte[:, :, ::-1]
+        # matte_orig = matte.copy().astype(np.float32) / 255
+        matte_orig = matte.copy().astype(np.float32) # segmentation version
+        assert matte_orig.max() <= 1 # some ffhq images are dirty, so may be all zero
+        matte = np.transpose(matte,
+                            #  (1, 2, 0)).astype(np.float32) / 255  # [0,1] range
+                             (1, 2, 0)).astype(np.float32)  # [0,1] range
+
+        # ! load 512 matte
+        # matte = cv2.resize(matte, (self.reso_gt, self.reso_gt),
+        #                    interpolation=cv2.INTER_NEAREST)
+
+        assert matte.min() >= 0 and matte.max(
+        ) <= 1, f'{matte.min(), matte.max()}'
+
+        if matte.ndim == 3:  # H, W
+            matte = matte[..., 0]
+
+        image = self._load_raw_image(self._raw_idx[idx])
+
+        assert isinstance(image, np.ndarray)
+        assert list(image.shape) == self.image_shape
+        assert image.dtype == np.uint8
+        if self._xflip[idx]:
+            assert image.ndim == 3  # CHW
+            image = image[:, :, ::-1]
+
+        # blending
+        # blending = True
+        # blending = False
+        # if blending:
+        #     image = image * matte_orig + (1 - matte_orig) * cv2.GaussianBlur(
+        #         image, (5, 5), cv2.BORDER_DEFAULT)
+            # image = image * matte_orig
+
+        # image = np.transpose(image, (1, 2, 0)).astype(
+        #     np.float32
+        # ) / 255  # H W C for torchvision process, normalize to [0,1]
+
+        # image_sr = torch.from_numpy(image)[..., :3].permute(
+        #     2, 0, 1) * 2 - 1  # normalize to [-1,1]
+        # image_to_encoder = self.normalize_for_encoder_input(image)
+
+        # image_gt = cv2.resize(image, (self.reso_gt, self.reso_gt),
+        #                       interpolation=cv2.INTER_AREA)
+        # image_gt = torch.from_numpy(image_gt)[..., :3].permute(
+        #     2, 0, 1) * 2 - 1  # normalize to [-1,1]
+
+        return dict(
+            c=self.get_label(idx),
+            # img_to_encoder=image_to_encoder,  # 224
+            # img_sr=image_sr,  # 512
+            img=image,  # [-1,1] range
+            # depth=torch.zeros_like(image_gt)[0, ...] # type: ignore
+            # depth=matte,
+            depth_mask=matte,
+        )  # return dict here
+
+class LMDBDataset_MV_Compressed_eg3d(LMDBDataset_MV_Compressed):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 **kwargs):
+        super().__init__(lmdb_path, reso, reso_encoder, imgnet_normalize,
+                         **kwargs)
+
+        self.normalize_for_encoder_input = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+            transforms.Resize(size=(self.reso_encoder, self.reso_encoder),
+                              antialias=True),  # type: ignore
+        ])
+
+        self.normalize_for_gt = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            transforms.Resize(size=(self.reso, self.reso),
+                              antialias=True),  # type: ignore
+        ])
+
+    def __getitem__(self, idx):
+        # sample = super(LMDBDataset).__getitem__(idx)
+
+        # do gzip uncompress online
+        with self.env.begin(write=False) as txn:
+            img_key = f'{idx}-img'.encode('utf-8')
+            image = self.load_image_fn(txn.get(img_key))
+
+            depth_key = f'{idx}-depth_mask'.encode('utf-8')
+            # depth = decompress_array(txn.get(depth_key), (512,512), np.float32)
+            depth = decompress_array(txn.get(depth_key), (64,64), np.float32)
+
+            c_key = f'{idx}-c'.encode('utf-8')
+            c = decompress_array(txn.get(c_key), (25, ), np.float32)
+
+        # ! post processing, e.g., normalizing
+        depth = cv2.resize(depth, (self.reso, self.reso),
+                           interpolation=cv2.INTER_NEAREST)
+
+        image = np.transpose(image, (1, 2, 0)).astype(
+            np.float32
+        ) / 255  # H W C for torchvision process, normalize to [0,1]
+
+        image_sr = torch.from_numpy(image)[..., :3].permute(
+            2, 0, 1) * 2 - 1  # normalize to [-1,1]
+        image_to_encoder = self.normalize_for_encoder_input(image)
+
+        image_gt = cv2.resize(image, (self.reso, self.reso),
+                              interpolation=cv2.INTER_AREA)
+        image_gt = torch.from_numpy(image_gt)[..., :3].permute(
+            2, 0, 1) * 2 - 1  # normalize to [-1,1]
+
+
+        return {
+            'img_to_encoder': image_to_encoder,  # 224
+            'img_sr': image_sr,  # 512
+            'img': image_gt,  # [-1,1] range
+            'c': c,
+            'depth': depth,
+            'depth_mask': depth,
+        }
diff --git a/datasets/g_buffer_objaverse.py b/datasets/g_buffer_objaverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..635110c2dc408c4b79dbf39ffee97f4cb21b611f
--- /dev/null
+++ b/datasets/g_buffer_objaverse.py
@@ -0,0 +1,6363 @@
+import os
+from tqdm import tqdm
+import kiui
+from kiui.op import recenter
+import kornia
+import collections
+import math
+import time
+import itertools
+import pickle
+from typing import Any
+import lmdb
+import cv2
+import trimesh
+
+cv2.setNumThreads(0)  # disable multiprocess
+# import imageio
+import imageio.v3 as imageio
+import numpy as np
+from PIL import Image
+import Imath
+import OpenEXR
+from pdb import set_trace as st
+from pathlib import Path
+import torchvision
+from torchvision.transforms import v2
+
+from einops import rearrange, repeat
+from functools import partial
+import io
+from scipy.stats import special_ortho_group
+import gzip
+import random
+import torch
+import torch as th
+from torch import nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+from torch.utils.data.distributed import DistributedSampler
+from pathlib import Path
+import lz4.frame
+from nsr.volumetric_rendering.ray_sampler import RaySampler
+import point_cloud_utils as pcu
+
+import torch.multiprocessing
+
+# torch.multiprocessing.set_sharing_strategy('file_system')
+
+from utils.general_utils import PILtoTorch, matrix_to_quaternion
+
+from guided_diffusion import logger
+import json
+
+import webdataset as wds
+from webdataset.shardlists import expand_source
+# st()
+
+from .shapenet import LMDBDataset, LMDBDataset_MV_Compressed, decompress_and_open_image_gzip, decompress_array
+from kiui.op import safe_normalize
+
+from utils.gs_utils.graphics_utils import getWorld2View2, getProjectionMatrix, getView2World
+
+from nsr.camera_utils import generate_input_camera
+
+
+def random_rotation_matrix():
+    # Generate a random rotation matrix in 3D
+    random_rotation_3d = special_ortho_group.rvs(3)
+
+    # Embed the 3x3 rotation matrix into a 4x4 matrix
+    rotation_matrix_4x4 = np.eye(4)
+    rotation_matrix_4x4[:3, :3] = random_rotation_3d
+
+    return rotation_matrix_4x4
+
+
+def fov2focal(fov, pixels):
+    return pixels / (2 * math.tan(fov / 2))
+
+
+def focal2fov(focal, pixels):
+    return 2 * math.atan(pixels / (2 * focal))
+
+
+def resize_depth_mask(depth_to_resize, resolution):
+    depth_resized = cv2.resize(depth_to_resize, (resolution, resolution),
+                               interpolation=cv2.INTER_LANCZOS4)
+    #    interpolation=cv2.INTER_AREA)
+    return depth_resized, depth_resized > 0  # type: ignore
+
+
+def resize_depth_mask_Tensor(depth_to_resize, resolution):
+
+    if depth_to_resize.shape[-1] != resolution:
+        depth_resized = torch.nn.functional.interpolate(
+            input=depth_to_resize.unsqueeze(1),
+            size=(resolution, resolution),
+            # mode='bilinear',
+            mode='nearest',
+            # align_corners=False,
+        ).squeeze(1)
+    else:
+        depth_resized = depth_to_resize
+
+    return depth_resized.float(), depth_resized > 0  # type: ignore
+
+
+class PostProcess:
+
+    def __init__(
+        self,
+        reso,
+        reso_encoder,
+        imgnet_normalize,
+        plucker_embedding,
+        decode_encode_img_only,
+        mv_input,
+        split_chunk_input,
+        duplicate_sample,
+        append_depth,
+        gs_cam_format,
+        orthog_duplicate,
+        frame_0_as_canonical,
+        pcd_path=None,
+        load_pcd=False,
+        split_chunk_size=8,
+        append_xyz=False,
+    ) -> None:
+
+        self.load_pcd = load_pcd
+
+        if pcd_path is None:  # hard-coded
+            pcd_path = '/cpfs01/user/lanyushi.p/data/FPS_PCD/pcd-V=6_256_again/fps-pcd/'
+
+        self.pcd_path = Path(pcd_path)
+
+        self.append_xyz = append_xyz
+        if append_xyz:
+            assert append_depth is False
+        self.frame_0_as_canonical = frame_0_as_canonical
+        self.gs_cam_format = gs_cam_format
+        self.append_depth = append_depth
+        self.plucker_embedding = plucker_embedding
+        self.decode_encode_img_only = decode_encode_img_only
+        self.duplicate_sample = duplicate_sample
+        self.orthog_duplicate = orthog_duplicate
+
+        self.zfar = 100.0
+        self.znear = 0.01
+
+        transformations = []
+        if not split_chunk_input:
+            transformations.append(transforms.ToTensor())
+
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+
+        self.reso_encoder = reso_encoder
+        self.reso = reso
+        self.instance_data_length = 40
+        # self.pair_per_instance = 1 # compat
+        self.mv_input = mv_input
+        self.split_chunk_input = split_chunk_input  # 8
+        self.chunk_size = split_chunk_size if split_chunk_input else 40
+        # assert self.chunk_size in [8, 10]
+        self.V = self.chunk_size // 2  # 4 views as input
+        # else:
+        #     assert self.chunk_size == 20
+        #     self.V = 12  # 6 + 6 here
+
+        # st()
+        assert split_chunk_input
+        self.pair_per_instance = 1
+        # else:
+        #     self.pair_per_instance = 4 if mv_input else 2  # check whether improves IO
+
+        self.ray_sampler = RaySampler()  # load xyz
+
+    def gen_rays(self, c):
+        # Generate rays
+        intrinsics, c2w = c[16:], c[:16].reshape(4, 4)
+        self.h = self.reso_encoder
+        self.w = self.reso_encoder
+        yy, xx = torch.meshgrid(
+            torch.arange(self.h, dtype=torch.float32) + 0.5,
+            torch.arange(self.w, dtype=torch.float32) + 0.5,
+            indexing='ij')
+
+        # normalize to 0-1 pixel range
+        yy = yy / self.h
+        xx = xx / self.w
+
+        # K = np.array([f_x, 0, w / 2, 0, f_y, h / 2, 0, 0, 1]).reshape(3, 3)
+        cx, cy, fx, fy = intrinsics[2], intrinsics[5], intrinsics[
+            0], intrinsics[4]
+        # cx *= self.w
+        # cy *= self.h
+
+        # f_x = f_y = fx * h / res_raw
+        c2w = torch.from_numpy(c2w).float()
+
+        xx = (xx - cx) / fx
+        yy = (yy - cy) / fy
+        zz = torch.ones_like(xx)
+        dirs = torch.stack((xx, yy, zz), dim=-1)  # OpenCV convention
+        dirs /= torch.norm(dirs, dim=-1, keepdim=True)
+        dirs = dirs.reshape(-1, 3, 1)
+        del xx, yy, zz
+        # st()
+        dirs = (c2w[None, :3, :3] @ dirs)[..., 0]
+
+        origins = c2w[None, :3, 3].expand(self.h * self.w, -1).contiguous()
+        origins = origins.view(self.h, self.w, 3)
+        dirs = dirs.view(self.h, self.w, 3)
+
+        return origins, dirs
+
+    def _post_process_batch_sample(self,
+                                   sample):  # sample is an instance batch here
+        caption, ins = sample[-2:]
+        instance_samples = []
+
+        for instance_idx in range(sample[0].shape[0]):
+            instance_samples.append(
+                self._post_process_sample(item[instance_idx]
+                                          for item in sample[:-2]))
+
+        return (*instance_samples, caption, ins)
+
+    def _post_process_sample(self, data_sample):
+        # raw_img, depth, c, bbox, caption, ins = data_sample
+        # st()
+        raw_img, depth, c, bbox = data_sample
+
+        bbox = (bbox * (self.reso / 256)).astype(
+            np.uint8)  # normalize bbox to the reso range
+
+        if raw_img.shape[-2] != self.reso_encoder:
+            img_to_encoder = cv2.resize(raw_img,
+                                        (self.reso_encoder, self.reso_encoder),
+                                        interpolation=cv2.INTER_LANCZOS4)
+        else:
+            img_to_encoder = raw_img
+
+        img_to_encoder = self.normalize(img_to_encoder)
+        if self.plucker_embedding:
+            rays_o, rays_d = self.gen_rays(c)
+            rays_plucker = torch.cat(
+                [torch.cross(rays_o, rays_d, dim=-1), rays_d],
+                dim=-1).permute(2, 0, 1)  # [h, w, 6] -> 6,h,w
+            img_to_encoder = torch.cat([img_to_encoder, rays_plucker], 0)
+
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+
+        img = torch.from_numpy(img).permute(2, 0, 1) / 127.5 - 1
+
+        if self.decode_encode_img_only:
+            depth_reso, fg_mask_reso = depth, depth
+        else:
+            depth_reso, fg_mask_reso = resize_depth_mask(depth, self.reso)
+
+        # return {
+        #     # **sample,
+        #     'img_to_encoder': img_to_encoder,
+        #     'img': img,
+        #     'depth_mask': fg_mask_reso,
+        #     # 'img_sr': img_sr,
+        #     'depth': depth_reso,
+        #     'c': c,
+        #     'bbox': bbox,
+        #     'caption': caption,
+        #     'ins': ins
+        #     # ! no need to load img_sr for now
+        # }
+        # if len(data_sample) == 4:
+        return (img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox)
+        # else:
+        #     return (img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox, data_sample[-2], data_sample[-1])
+
+    def canonicalize_pts(self, c, pcd, for_encoder=True, canonical_idx=0):
+        # pcd: sampled in world space
+
+        assert c.shape[0] == self.chunk_size
+        assert for_encoder
+
+        # st()
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+
+        cam_radius = np.linalg.norm(
+            c[[0, self.V]][:, :16].reshape(2, 4, 4)[:, :3, 3],
+            axis=-1,
+            keepdims=False)  # since g-buffer adopts dynamic radius here.
+        frame1_fixed_pos = np.repeat(np.eye(4)[None], 2, axis=0)
+        frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+        transform = frame1_fixed_pos @ np.linalg.inv(camera_poses[[0, self.V
+                                                                   ]])  # B 4 4
+        transform = np.expand_dims(transform, axis=1)  # B 1 4 4
+        # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+        # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+        repeated_homo_pcd = np.repeat(np.concatenate(
+            [pcd, np.ones_like(pcd[..., 0:1])], -1)[None],
+                                      2,
+                                      axis=0)[..., None]  # B N 4 1
+        new_pcd = (transform @ repeated_homo_pcd)[..., :3, 0]  # 2 N 3
+
+        return new_pcd
+
+    def canonicalize_pts_v6(self, c, pcd, for_encoder=True, canonical_idx=0):
+        exit()  # deprecated function
+        # pcd: sampled in world space
+
+        assert c.shape[0] == self.chunk_size
+        assert for_encoder
+        encoder_canonical_idx = [0, 6, 12, 18]
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+
+        cam_radius = np.linalg.norm(
+            c[encoder_canonical_idx][:, :16].reshape(4, 4, 4)[:, :3, 3],
+            axis=-1,
+            keepdims=False)  # since g-buffer adopts dynamic radius here.
+        frame1_fixed_pos = np.repeat(np.eye(4)[None], 4, axis=0)
+        frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+        transform = frame1_fixed_pos @ np.linalg.inv(
+            camera_poses[encoder_canonical_idx])  # B 4 4
+        transform = np.expand_dims(transform, axis=1)  # B 1 4 4
+        # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+        # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+        repeated_homo_pcd = np.repeat(np.concatenate(
+            [pcd, np.ones_like(pcd[..., 0:1])], -1)[None],
+                                      4,
+                                      axis=0)[..., None]  # B N 4 1
+        new_pcd = (transform @ repeated_homo_pcd)[..., :3, 0]  # 2 N 3
+
+        return new_pcd
+
+    def normalize_camera(self, c, for_encoder=True, canonical_idx=0):
+        assert c.shape[0] == self.chunk_size  # 8 o r10
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+
+        if for_encoder:
+            encoder_canonical_idx = [0, self.V]
+            # st()
+            cam_radius = np.linalg.norm(
+                c[encoder_canonical_idx][:, :16].reshape(2, 4, 4)[:, :3, 3],
+                axis=-1,
+                keepdims=False)  # since g-buffer adopts dynamic radius here.
+            frame1_fixed_pos = np.repeat(np.eye(4)[None], 2, axis=0)
+            frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+            transform = frame1_fixed_pos @ np.linalg.inv(
+                camera_poses[encoder_canonical_idx])
+            # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+            # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+            new_camera_poses = np.repeat(
+                transform, self.V, axis=0
+            ) @ camera_poses  # [V, 4, 4]. np.repeat() is th.repeat_interleave()
+
+        else:
+            cam_radius = np.linalg.norm(
+                c[canonical_idx][:16].reshape(4, 4)[:3, 3],
+                axis=-1,
+                keepdims=False)  # since g-buffer adopts dynamic radius here.
+            frame1_fixed_pos = np.eye(4)
+            frame1_fixed_pos[2, -1] = -cam_radius
+
+            transform = frame1_fixed_pos @ np.linalg.inv(
+                camera_poses[canonical_idx])  # 4,4
+            # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+            # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+            new_camera_poses = np.repeat(transform[None],
+                                         self.chunk_size,
+                                         axis=0) @ camera_poses  # [V, 4, 4]
+
+        c = np.concatenate([new_camera_poses.reshape(B, 16), c[:, 16:]],
+                           axis=-1)
+
+        return c
+
+    def normalize_camera_v6(self, c, for_encoder=True, canonical_idx=0):
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+
+        if for_encoder:
+            assert c.shape[0] == 24
+            encoder_canonical_idx = [0, 6, 12, 18]
+            cam_radius = np.linalg.norm(
+                c[encoder_canonical_idx][:, :16].reshape(4, 4, 4)[:, :3, 3],
+                axis=-1,
+                keepdims=False)  # since g-buffer adopts dynamic radius here.
+            frame1_fixed_pos = np.repeat(np.eye(4)[None], 4, axis=0)
+            frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+            transform = frame1_fixed_pos @ np.linalg.inv(
+                camera_poses[encoder_canonical_idx])
+            # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+            # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+            new_camera_poses = np.repeat(transform, 6,
+                                         axis=0) @ camera_poses  # [V, 4, 4]
+
+        else:
+            assert c.shape[0] == 12
+            cam_radius = np.linalg.norm(
+                c[canonical_idx][:16].reshape(4, 4)[:3, 3],
+                axis=-1,
+                keepdims=False)  # since g-buffer adopts dynamic radius here.
+            frame1_fixed_pos = np.eye(4)
+            frame1_fixed_pos[2, -1] = -cam_radius
+
+            transform = frame1_fixed_pos @ np.linalg.inv(
+                camera_poses[canonical_idx])  # 4,4
+            # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+            # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+            new_camera_poses = np.repeat(transform[None], 12,
+                                         axis=0) @ camera_poses  # [V, 4, 4]
+
+        c = np.concatenate([new_camera_poses.reshape(B, 16), c[:, 16:]],
+                           axis=-1)
+
+        return c
+
+    def get_plucker_ray(self, c):
+        rays_plucker = []
+        for idx in range(c.shape[0]):
+            rays_o, rays_d = self.gen_rays(c[idx])
+            rays_plucker.append(
+                torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d],
+                          dim=-1).permute(2, 0, 1))  # [h, w, 6] -> 6,h,w
+        rays_plucker = torch.stack(rays_plucker, 0)
+        return rays_plucker
+
+    def _unproj_depth_given_c(self, c, depth):
+        # get xyz hxw for each pixel, like MCC
+        # img_size = self.reso
+        img_size = depth.shape[-1]
+
+        B = c.shape[0]
+
+        cam2world_matrix = c[:, :16].reshape(B, 4, 4)
+        intrinsics = c[:, 16:25].reshape(B, 3, 3)
+
+        ray_origins, ray_directions = self.ray_sampler(  # shape: 
+            cam2world_matrix, intrinsics, img_size)[:2]
+
+        depth = depth.reshape(B, -1).unsqueeze(-1)
+
+        xyz = ray_origins + depth * ray_directions  # BV HW 3, already in the world space
+        xyz = xyz.reshape(B, img_size, img_size, 3).permute(0, 3, 1,
+                                                            2)  # B 3 H W
+        xyz = xyz.clip(
+            -0.45, 0.45)  # g-buffer saves depth with anti-alias = True .....
+        xyz = torch.where(xyz.abs() == 0.45, 0, xyz)  # no boundary here? Yes.
+
+        return xyz
+
+    def _post_process_sample_batch(self, data_sample):
+        # raw_img, depth, c, bbox, caption, ins = data_sample
+
+        alpha = None
+        if len(data_sample) == 4:
+            raw_img, depth, c, bbox = data_sample
+        else:
+            raw_img, depth, c, alpha, bbox = data_sample  # put c to position 2
+
+        if isinstance(depth, tuple):
+            self.append_normal = True
+            depth, normal = depth
+        else:
+            self.append_normal = False
+            normal = None
+
+        # if raw_img.shape[-1] == 4:
+        #     depth_reso, _ = resize_depth_mask_Tensor(
+        #         torch.from_numpy(depth), self.reso)
+        #     raw_img, fg_mask_reso = raw_img[..., :3], raw_img[..., -1]
+        #     # st() # ! check has 1 dim in alpha?
+        # else:
+        if not isinstance(depth, torch.Tensor):
+            depth = torch.from_numpy(depth).float()
+        else:
+            depth = depth.float()
+
+        depth_reso, fg_mask_reso = resize_depth_mask_Tensor(depth, self.reso)
+
+        if alpha is None:
+            alpha = fg_mask_reso
+        else:
+            # ! resize first
+            # st()
+            alpha = torch.from_numpy(alpha / 255.0).float()
+            if alpha.shape[-1] != self.reso:  # bilinear inteprolate reshape
+                alpha = torch.nn.functional.interpolate(
+                    input=alpha.unsqueeze(1),
+                    size=(self.reso, self.reso),
+                    mode='bilinear',
+                    align_corners=False,
+                ).squeeze(1)
+
+        if self.reso < 256:
+            bbox = (bbox * (self.reso / 256)).astype(
+                np.uint8)  # normalize bbox to the reso range
+        else:  # 3dgs
+            bbox = bbox.astype(np.uint8)
+
+        # st() # ! shall compat with 320 input
+
+        # assert raw_img.shape[-2] == self.reso_encoder
+
+        # img_to_encoder = cv2.resize(
+        #     raw_img, (self.reso_encoder, self.reso_encoder),
+        #     interpolation=cv2.INTER_LANCZOS4)
+        # else:
+        # img_to_encoder = raw_img
+
+        raw_img = torch.from_numpy(raw_img).permute(0, 3, 1,
+                                                    2) / 255.0  # [0,1]
+
+        if normal is not None:
+            normal = torch.from_numpy(normal).permute(0,3,1,2)
+
+        # if raw_img.shape[-1] != self.reso:
+
+
+        if raw_img.shape[1] != self.reso_encoder:
+            img_to_encoder = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso_encoder, self.reso_encoder),
+                mode='bilinear',
+                align_corners=False,)
+            img_to_encoder = self.normalize(img_to_encoder)
+
+            if normal is not None:
+                normal_for_encoder = torch.nn.functional.interpolate(
+                    input=normal,
+                    size=(self.reso_encoder, self.reso_encoder),
+                    # mode='bilinear',
+                    mode='nearest',
+                    # align_corners=False,
+                )
+
+        else:
+            img_to_encoder = self.normalize(raw_img)
+            normal_for_encoder = normal
+
+        if raw_img.shape[-1] != self.reso:
+            img = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso, self.reso),
+                mode='bilinear',
+                align_corners=False,
+            )  # [-1,1] range
+            img = img * 2 - 1  # as gt
+
+            if normal is not None:
+                normal = torch.nn.functional.interpolate(
+                    input=normal,
+                    size=(self.reso, self.reso),
+                    # mode='bilinear',
+                    mode='nearest',
+                    # align_corners=False,
+                )
+
+        else:
+            img = raw_img * 2 - 1
+        
+
+        # fg_mask_reso = depth[..., -1:] # ! use
+
+        pad_v6_fn = lambda x: torch.concat([x, x[:4]], 0) if isinstance(
+            x, torch.Tensor) else np.concatenate([x, x[:4]], 0)
+
+        # ! processing encoder input image.
+
+        # ! normalize camera feats
+        if self.frame_0_as_canonical:  # 4 views as input per batch
+
+            # if self.chunk_size in [8, 10]:
+            if True:
+                # encoder_canonical_idx = [0, 4]
+                # encoder_canonical_idx = [0, self.chunk_size//2]
+                encoder_canonical_idx = [0, self.V]
+
+                c_for_encoder = self.normalize_camera(c, for_encoder=True)
+                c_for_render = self.normalize_camera(
+                    c,
+                    for_encoder=False,
+                    canonical_idx=encoder_canonical_idx[0]
+                )  # allocated to nv_c, frame0 (in 8 views) as the canonical
+                c_for_render_nv = self.normalize_camera(
+                    c,
+                    for_encoder=False,
+                    canonical_idx=encoder_canonical_idx[1]
+                )  # allocated to nv_c, frame0 (in 8 views) as the canonical
+                c_for_render = np.concatenate([c_for_render, c_for_render_nv],
+                                              axis=-1)  # for compat
+                # st()
+
+            else:
+                assert self.chunk_size == 20
+                c_for_encoder = self.normalize_camera_v6(c,
+                                                         for_encoder=True)  #
+
+                paired_c_0 = np.concatenate([c[0:6], c[12:18]])
+                paired_c_1 = np.concatenate([c[6:12], c[18:24]])
+
+                def process_paired_camera(paired_c):
+                    c_for_render = self.normalize_camera_v6(
+                        paired_c, for_encoder=False, canonical_idx=0
+                    )  # allocated to nv_c, frame0 (in 8 views) as the canonical
+                    c_for_render_nv = self.normalize_camera_v6(
+                        paired_c, for_encoder=False, canonical_idx=6
+                    )  # allocated to nv_c, frame0 (in 8 views) as the canonical
+
+                    c_for_render = np.concatenate(
+                        [c_for_render, c_for_render_nv], axis=-1)  # for compat
+
+                    return c_for_render
+
+                paired_c_for_render_0 = process_paired_camera(paired_c_0)
+                paired_c_for_render_1 = process_paired_camera(paired_c_1)
+
+                c_for_render = np.empty(shape=(24, 50))
+                c_for_render[list(range(6)) +
+                             list(range(12, 18))] = paired_c_for_render_0
+                c_for_render[list(range(6, 12)) +
+                             list(range(18, 24))] = paired_c_for_render_1
+
+        else:  # use g-buffer canonical c
+            c_for_encoder, c_for_render = c, c
+
+        if self.append_normal and normal is not None:
+            img_to_encoder = torch.cat([img_to_encoder, normal_for_encoder],
+            # img_to_encoder = torch.cat([img_to_encoder, normal],
+                                       1)  # concat in C dim
+
+        if self.plucker_embedding:
+            # rays_plucker = self.get_plucker_ray(c)
+            rays_plucker = self.get_plucker_ray(c_for_encoder)
+            img_to_encoder = torch.cat([img_to_encoder, rays_plucker],
+                                       1)  # concat in C dim
+
+        # torchvision.utils.save_image(raw_img, 'tmp/inp.png', normalize=True, value_range=(0,1), nrow=1, padding=0)
+        # torchvision.utils.save_image(rays_plucker[:,:3], 'tmp/plucker.png', normalize=True, value_range=(-1,1), nrow=1, padding=0)
+        # torchvision.utils.save_image(depth_reso.unsqueeze(1), 'tmp/depth.png', normalize=True, nrow=1, padding=0)
+
+        c = torch.from_numpy(c_for_render).to(torch.float32)
+
+        if self.append_depth:
+            normalized_depth = torch.from_numpy(depth_reso).clone().unsqueeze(
+                1)  # min=0
+            # normalized_depth -= torch.min(normalized_depth) # always 0 here
+            # normalized_depth /= torch.max(normalized_depth)
+            # normalized_depth = normalized_depth.unsqueeze(1) * 2 - 1 # normalize to [-1,1]
+            # st()
+            img_to_encoder = torch.cat([img_to_encoder, normalized_depth],
+                                       1)  # concat in C dim
+        elif self.append_xyz:
+            depth_for_unproj = depth.clone()
+            depth_for_unproj[depth_for_unproj ==
+                  0] = 1e10  # so that rays_o will not appear in the final pcd.
+            xyz = self._unproj_depth_given_c(c.float(), depth)
+            # pcu.save_mesh_v(f'unproj_xyz_before_Nearest.ply', xyz[0:9].float().detach().permute(0,2,3,1).reshape(-1,3).cpu().numpy(),)
+
+            if xyz.shape[-1] != self.reso_encoder:
+                xyz = torch.nn.functional.interpolate(
+                    input=xyz,  # [-1,1]
+                    # size=(self.reso, self.reso),
+                    size=(self.reso_encoder, self.reso_encoder),
+                    mode='nearest',
+                )
+
+            # pcu.save_mesh_v(f'unproj_xyz_afterNearest.ply', xyz[0:9].float().detach().permute(0,2,3,1).reshape(-1,3).cpu().numpy(),)
+            # st()
+            img_to_encoder = torch.cat([img_to_encoder, xyz], 1)
+
+        return (img_to_encoder, img, alpha, depth_reso, c,
+                torch.from_numpy(bbox))
+
+    def rand_sample_idx(self):
+        return random.randint(0, self.instance_data_length - 1)
+
+    def rand_pair(self):
+        return (self.rand_sample_idx() for _ in range(2))
+
+    def paired_post_process(self, sample):
+        # repeat n times?
+        all_inp_list = []
+        all_nv_list = []
+        caption, ins = sample[-2:]
+        # expanded_return = []
+        for _ in range(self.pair_per_instance):
+            cano_idx, nv_idx = self.rand_pair()
+            cano_sample = self._post_process_sample(item[cano_idx]
+                                                    for item in sample[:-2])
+            nv_sample = self._post_process_sample(item[nv_idx]
+                                                  for item in sample[:-2])
+            all_inp_list.extend(cano_sample)
+            all_nv_list.extend(nv_sample)
+        return (*all_inp_list, *all_nv_list, caption, ins)
+        # return [cano_sample, nv_sample, caption, ins]
+        # return (*cano_sample, *nv_sample, caption, ins)
+
+    def get_source_cw2wT(self, source_cameras_view_to_world):
+        return matrix_to_quaternion(
+            source_cameras_view_to_world[:3, :3].transpose(0, 1))
+
+    def c_to_3dgs_format(self, pose):
+        # TODO, switch to torch version (batched later)
+
+        c2w = pose[:16].reshape(4, 4)  # 3x4
+
+        # ! load cam
+        w2c = np.linalg.inv(c2w)
+        R = np.transpose(
+            w2c[:3, :3])  # R is stored transposed due to 'glm' in CUDA code
+        T = w2c[:3, 3]
+        fx = pose[16]
+        FovX = focal2fov(fx, 1)
+        FovY = focal2fov(fx, 1)
+
+        tanfovx = math.tan(FovX * 0.5)
+        tanfovy = math.tan(FovY * 0.5)
+
+        assert tanfovx == tanfovy
+
+        trans = np.array([0.0, 0.0, 0.0])
+        scale = 1.0
+
+        view_world_transform = torch.tensor(getView2World(R, T, trans,
+                                                          scale)).transpose(
+                                                              0, 1)
+
+        world_view_transform = torch.tensor(getWorld2View2(R, T, trans,
+                                                           scale)).transpose(
+                                                               0, 1)
+        projection_matrix = getProjectionMatrix(znear=self.znear,
+                                                zfar=self.zfar,
+                                                fovX=FovX,
+                                                fovY=FovY).transpose(0, 1)
+        full_proj_transform = (world_view_transform.unsqueeze(0).bmm(
+            projection_matrix.unsqueeze(0))).squeeze(0)
+        camera_center = world_view_transform.inverse()[3, :3]
+
+        # ! check pytorch3d camera system alignment.
+
+        # item.update(viewpoint_cam=[viewpoint_cam])
+        c = {}
+        #
+        c["source_cv2wT_quat"] = self.get_source_cw2wT(view_world_transform)
+        c.update(
+            # projection_matrix=projection_matrix, # K
+            R=torch.from_numpy(R),
+            T=torch.from_numpy(T),
+            cam_view=world_view_transform,  # world_view_transform
+            cam_view_proj=full_proj_transform,  # full_proj_transform
+            cam_pos=camera_center,
+            tanfov=tanfovx,  # TODO, fix in the renderer
+            orig_pose=torch.from_numpy(pose),
+            orig_c2w=torch.from_numpy(c2w),
+            orig_w2c=torch.from_numpy(w2c),
+            orig_intrin=torch.from_numpy(pose[16:]).reshape(3,3),
+            # tanfovy=tanfovy,
+        )
+
+        return c  # dict for gs rendering
+
+    def paired_post_process_chunk(self, sample):
+        # st()
+
+        # sample_npz, ins, caption = sample_pyd # three items
+        # sample = *(sample[0][k] for k in ['raw_img', 'depth', 'c', 'bbox']), sample[-1], sample[-2]
+
+        # repeat n times?
+        all_inp_list = []
+        all_nv_list = []
+        auxiliary_sample = list(sample[-2:])
+        # caption, ins = sample[-2:]
+        ins = sample[-1]
+
+        assert sample[0].shape[0] == self.chunk_size  # random chunks
+        # expanded_return = []
+
+        if self.load_pcd:
+            # fps_pcd = pcu.load_mesh_v(
+            #     # str(self.pcd_path / ins / 'fps-24576.ply'))  # N, 3
+            #     str(self.pcd_path / ins / 'fps-4096.ply'))  # N, 3
+            # #   'fps-4096.ply'))  # N, 3
+            fps_pcd = trimesh.load(str(self.pcd_path / ins / 'fps-4096.ply')).vertices
+
+            auxiliary_sample += [fps_pcd]
+
+        assert self.duplicate_sample
+        # st()
+        if self.duplicate_sample:
+            # ! shuffle before process, since frame_0_as_canonical fixed c.
+
+            if self.chunk_size in [20, 18, 16, 12]:
+                shuffle_sample = sample[:-2]  # no order shuffle required
+            else:
+                shuffle_sample = []
+                # indices = torch.randperm(self.chunk_size)
+                indices = np.random.permutation(self.chunk_size)
+                for _, item in enumerate(sample[:-2]):
+                    shuffle_sample.append(item[indices])  # random shuffle
+
+            processed_sample = self._post_process_sample_batch(shuffle_sample)
+
+            # ! process pcd if frmae_0 alignment
+
+            if self.load_pcd:
+                if self.frame_0_as_canonical:
+                    # ! normalize camera feats
+
+                    # normalized camera feats as in paper (transform the first pose to a fixed position)
+                    # if self.chunk_size == 20:
+                    #     auxiliary_sample[-1] = self.canonicalize_pts_v6(
+                    #         c=shuffle_sample[2],
+                    #         pcd=auxiliary_sample[-1],
+                    #         for_encoder=True)  # B N 3
+                    # else:
+                    auxiliary_sample[-1] = self.canonicalize_pts(
+                        c=shuffle_sample[2],
+                        pcd=auxiliary_sample[-1],
+                        for_encoder=True)  # B N 3
+                else:
+                    auxiliary_sample[-1] = np.repeat(
+                        auxiliary_sample[-1][None], 2,
+                        axis=0)  # share the same camera syste, just repeat
+
+            assert not self.orthog_duplicate
+
+            # if self.chunk_size == 8:
+            all_inp_list.extend(item[:self.V] for item in processed_sample)
+            all_nv_list.extend(item[self.V:] for item in processed_sample)
+
+            # elif self.chunk_size == 20:  # V=6
+            #     # indices_v6 = [np.random.permutation(self.chunk_size)[:12] for _ in range(2)] # random sample 6 views from chunks
+            #     all_inp_list.extend(item[:12] for item in processed_sample)
+            #     # indices_v6 = np.concatenate([np.arange(12, 20), np.arange(0,4)])
+            #     all_nv_list.extend(
+            #         item[12:] for item in
+            #         processed_sample)  # already repeated inside batch fn
+            # else:
+            #     raise NotImplementedError(self.chunk_size)
+
+            # else:
+            #     all_inp_list.extend(item[:8] for item in processed_sample)
+            #     all_nv_list.extend(item[8:] for item in processed_sample)
+
+            # st()
+
+            return (*all_inp_list, *all_nv_list, *auxiliary_sample)
+
+        else:
+            processed_sample = self._post_process_sample_batch(  # avoid shuffle shorten processing time
+                item[:4] for item in sample[:-2])
+
+            all_inp_list.extend(item for item in processed_sample)
+            all_nv_list.extend(item
+                               for item in processed_sample)  # ! placeholder
+
+        # return (*all_inp_list, *all_nv_list, caption, ins)
+        return (*all_inp_list, *all_nv_list, *auxiliary_sample)
+
+        # randomly shuffle 8 views, avoid overfitting
+
+    def single_sample_create_dict_noBatch(self, sample, prefix=''):
+        # if len(sample) == 1:
+        #     sample = sample[0]
+        # assert len(sample) == 6
+        img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox = sample
+
+        if self.gs_cam_format:
+            # TODO, can optimize later after model converges
+            B, V, _ = c.shape  # B 4 25
+            c = rearrange(c, 'B V C -> (B V) C').cpu().numpy()
+            # c = c.cpu().numpy()
+            all_gs_c = [self.c_to_3dgs_format(pose) for pose in c]
+            # st()
+            # all_gs_c = self.c_to_3dgs_format(c.cpu().numpy())
+            c = {
+                k:
+                rearrange(torch.stack([gs_c[k] for gs_c in all_gs_c]),
+                          '(B V) ... -> B V ...',
+                          B=B,
+                          V=V)
+                # torch.stack([gs_c[k] for gs_c in all_gs_c])
+                if isinstance(all_gs_c[0][k], torch.Tensor) else all_gs_c[0][k]
+                for k in all_gs_c[0].keys()
+            }
+            # c = collate_gs_c
+
+        return {
+            # **sample,
+            f'{prefix}img_to_encoder': img_to_encoder,
+            f'{prefix}img': img,
+            f'{prefix}depth_mask': fg_mask_reso,
+            f'{prefix}depth': depth_reso,
+            f'{prefix}c': c,
+            f'{prefix}bbox': bbox,
+        }
+
+    def single_sample_create_dict(self, sample, prefix=''):
+        # if len(sample) == 1:
+        #     sample = sample[0]
+        # assert len(sample) == 6
+        img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox = sample
+
+        if self.gs_cam_format:
+            # TODO, can optimize later after model converges
+            B, V, _ = c.shape  # B 4 25
+            c = rearrange(c, 'B V C -> (B V) C').cpu().numpy()
+            all_gs_c = [self.c_to_3dgs_format(pose) for pose in c]
+            c = {
+                k:
+                rearrange(torch.stack([gs_c[k] for gs_c in all_gs_c]),
+                          '(B V) ... -> B V ...',
+                          B=B,
+                          V=V)
+                if isinstance(all_gs_c[0][k], torch.Tensor) else all_gs_c[0][k]
+                for k in all_gs_c[0].keys()
+            }
+            # c = collate_gs_c
+
+        return {
+            # **sample,
+            f'{prefix}img_to_encoder': img_to_encoder,
+            f'{prefix}img': img,
+            f'{prefix}depth_mask': fg_mask_reso,
+            f'{prefix}depth': depth_reso,
+            f'{prefix}c': c,
+            f'{prefix}bbox': bbox,
+        }
+
+    def single_instance_sample_create_dict(self, sample, prfix=''):
+        assert len(sample) == 42
+
+        inp_sample_list = [[] for _ in range(6)]
+
+        for item in sample[:40]:
+            for item_idx in range(6):
+                inp_sample_list[item_idx].append(item[0][item_idx])
+
+        inp_sample = self.single_sample_create_dict(
+            (torch.stack(item_list) for item_list in inp_sample_list),
+            prefix='')
+
+        return {
+            **inp_sample,  # 
+            'caption': sample[-2],
+            'ins': sample[-1]
+        }
+
+    def decode_gzip(self, sample_pyd, shape=(256, 256)):
+        # sample_npz, ins, caption = sample_pyd # three items
+        # c, bbox, depth, ins, caption, raw_img = sample_pyd[:5], sample_pyd[5:]
+
+        # wds.to_tuple('raw_img.jpeg', 'depth.jpeg',
+        # 'd_near.npy',
+        # 'd_far.npy',
+        # "c.npy", 'bbox.npy', 'ins.txt', 'caption.txt'),
+
+        # raw_img, depth, alpha_mask, d_near, d_far, c, bbox, ins, caption = sample_pyd
+        raw_img, depth_alpha, = sample_pyd
+        # return raw_img, depth_alpha
+        # raw_img, caption = sample_pyd
+        # return raw_img, caption
+        # st()
+        raw_img = rearrange(raw_img, 'h (b w) c -> b h w c', b=self.chunk_size)
+
+        depth = rearrange(depth, 'h (b w) c -> b h w c', b=self.chunk_size)
+
+        alpha_mask = rearrange(
+            alpha_mask, 'h (b w) c -> b h w c', b=self.chunk_size) / 255.0
+
+        d_far = d_far.reshape(self.chunk_size, 1, 1, 1)
+        d_near = d_near.reshape(self.chunk_size, 1, 1, 1)
+        # d = 1 / ( (d_normalized / 255) * (far-near) + near)
+        depth = 1 / ((depth / 255) * (d_far - d_near) + d_near)
+        depth = depth[..., 0]  # decoded from jpeg
+
+        # depth = decompress_array(depth['depth'], (self.chunk_size, *shape),
+        #                          np.float32,
+        #                          decompress=True,
+        #                          decompress_fn=lz4.frame.decompress)
+
+        # return raw_img, depth, d_near, d_far,  c, bbox, caption, ins
+
+        raw_img = np.concatenate([raw_img, alpha_mask[..., 0:1]], -1)
+
+        return raw_img, depth, c, bbox, caption, ins
+
+    def decode_zip(
+        self,
+        sample_pyd,
+    ):
+        shape = (self.reso_encoder, self.reso_encoder)
+        if isinstance(sample_pyd, tuple):
+            sample_pyd = sample_pyd[0]
+        assert isinstance(sample_pyd, dict)
+
+        raw_img = decompress_and_open_image_gzip(
+            sample_pyd['raw_img'],
+            is_img=True,
+            decompress=True,
+            decompress_fn=lz4.frame.decompress)
+
+        caption = sample_pyd['caption'].decode('utf-8')
+        ins = sample_pyd['ins'].decode('utf-8')
+
+        c = decompress_array(sample_pyd['c'], (
+            self.chunk_size,
+            25,
+        ),
+                             np.float32,
+                             decompress=True,
+                             decompress_fn=lz4.frame.decompress)
+
+        bbox = decompress_array(
+            sample_pyd['bbox'],
+            (
+                self.chunk_size,
+                4,
+            ),
+            np.float32,
+            # decompress=False)
+            decompress=True,
+            decompress_fn=lz4.frame.decompress)
+
+        if self.decode_encode_img_only:
+            depth = np.zeros(shape=(self.chunk_size,
+                                    *shape))  # save loading time
+        else:
+            depth = decompress_array(sample_pyd['depth'],
+                                     (self.chunk_size, *shape),
+                                     np.float32,
+                                     decompress=True,
+                                     decompress_fn=lz4.frame.decompress)
+
+        # return {'raw_img': raw_img, 'depth': depth, 'bbox': bbox, 'caption': caption, 'ins': ins, 'c': c}
+        # return raw_img, depth, c, bbox, caption, ins
+        # return raw_img, bbox, caption, ins
+        # return bbox, caption, ins
+        return raw_img, depth, c, bbox, caption, ins
+        # ! run single-instance pipeline first
+        # return raw_img[0], depth[0], c[0], bbox[0], caption, ins
+
+    def create_dict_nobatch(self, sample):
+        # sample = [item[0] for item in sample] # wds wrap items in []
+
+        sample_length = 6
+        # if self.load_pcd:
+        #     sample_length += 1
+
+        cano_sample_list = [[] for _ in range(sample_length)]
+        nv_sample_list = [[] for _ in range(sample_length)]
+        # st()
+        # bs = (len(sample)-2) // 6
+        for idx in range(0, self.pair_per_instance):
+
+            cano_sample = sample[sample_length * idx:sample_length * (idx + 1)]
+            nv_sample = sample[sample_length * self.pair_per_instance +
+                               sample_length * idx:sample_length *
+                               self.pair_per_instance + sample_length *
+                               (idx + 1)]
+
+            for item_idx in range(sample_length):
+                if self.frame_0_as_canonical:
+                    # ! cycle input/output view for more pairs
+                    if item_idx == 4:
+                        cano_sample_list[item_idx].append(
+                            cano_sample[item_idx][..., :25])
+                        nv_sample_list[item_idx].append(
+                            nv_sample[item_idx][..., :25])
+
+                        cano_sample_list[item_idx].append(
+                            nv_sample[item_idx][..., 25:])
+                        nv_sample_list[item_idx].append(
+                            cano_sample[item_idx][..., 25:])
+
+                    else:
+                        cano_sample_list[item_idx].append(
+                            cano_sample[item_idx])
+                        nv_sample_list[item_idx].append(nv_sample[item_idx])
+
+                        cano_sample_list[item_idx].append(nv_sample[item_idx])
+                        nv_sample_list[item_idx].append(cano_sample[item_idx])
+
+                else:
+                    cano_sample_list[item_idx].append(cano_sample[item_idx])
+                    nv_sample_list[item_idx].append(nv_sample[item_idx])
+
+                    cano_sample_list[item_idx].append(nv_sample[item_idx])
+                    nv_sample_list[item_idx].append(cano_sample[item_idx])
+
+        cano_sample = self.single_sample_create_dict_noBatch(
+            (torch.stack(item_list, 0) for item_list in cano_sample_list),
+            prefix=''
+        )  # torch.Size([5, 10, 256, 256]). Since no batch dim here for now.
+
+        nv_sample = self.single_sample_create_dict_noBatch(
+            (torch.stack(item_list, 0) for item_list in nv_sample_list),
+            prefix='nv_')
+
+        ret_dict = {
+            **cano_sample,
+            **nv_sample,
+        }
+
+        if not self.load_pcd:
+            ret_dict.update({'caption': sample[-2], 'ins': sample[-1]})
+
+        else:
+            # if self.frame_0_as_canonical:
+            #     # fps_pcd = rearrange( sample[-1], 'B V ... -> (B V) ...')  # ! wrong order.
+            #     # if self.chunk_size == 8:
+            #     fps_pcd = rearrange(
+            #         sample[-1], 'B V ... -> (V B) ...')  # mimic torch.repeat
+            #     # else:
+            #     #     fps_pcd = rearrange( sample[-1], 'B V ... -> (B V) ...')  # ugly code to match the input format...
+            # else:
+            #     fps_pcd = sample[-1].repeat(
+            #         2, 1,
+            #         1)  # mimic torch.cat(), from torch.Size([3, 4096, 3])
+
+            # ! TODO, check fps_pcd order
+
+            ret_dict.update({
+                'caption': sample[-3],
+                'ins': sample[-2],
+                'fps_pcd': sample[-1]
+            })
+
+        return ret_dict
+
+    def create_dict(self, sample):
+        # sample = [item[0] for item in sample] # wds wrap items in []
+        # st()
+
+        sample_length = 6
+        # if self.load_pcd:
+        #     sample_length += 1
+
+        cano_sample_list = [[] for _ in range(sample_length)]
+        nv_sample_list = [[] for _ in range(sample_length)]
+        # st()
+        # bs = (len(sample)-2) // 6
+        for idx in range(0, self.pair_per_instance):
+
+            cano_sample = sample[sample_length * idx:sample_length * (idx + 1)]
+            nv_sample = sample[sample_length * self.pair_per_instance +
+                               sample_length * idx:sample_length *
+                               self.pair_per_instance + sample_length *
+                               (idx + 1)]
+
+            for item_idx in range(sample_length):
+                if self.frame_0_as_canonical:
+                    # ! cycle input/output view for more pairs
+                    if item_idx == 4:
+                        cano_sample_list[item_idx].append(
+                            cano_sample[item_idx][..., :25])
+                        nv_sample_list[item_idx].append(
+                            nv_sample[item_idx][..., :25])
+
+                        cano_sample_list[item_idx].append(
+                            nv_sample[item_idx][..., 25:])
+                        nv_sample_list[item_idx].append(
+                            cano_sample[item_idx][..., 25:])
+
+                    else:
+                        cano_sample_list[item_idx].append(
+                            cano_sample[item_idx])
+                        nv_sample_list[item_idx].append(nv_sample[item_idx])
+
+                        cano_sample_list[item_idx].append(nv_sample[item_idx])
+                        nv_sample_list[item_idx].append(cano_sample[item_idx])
+
+                else:
+                    cano_sample_list[item_idx].append(cano_sample[item_idx])
+                    nv_sample_list[item_idx].append(nv_sample[item_idx])
+
+                    cano_sample_list[item_idx].append(nv_sample[item_idx])
+                    nv_sample_list[item_idx].append(cano_sample[item_idx])
+
+        # if self.split_chunk_input:
+        #     cano_sample = self.single_sample_create_dict(
+        #         (torch.cat(item_list, 0) for item_list in cano_sample_list),
+        #         prefix='')
+        #     nv_sample = self.single_sample_create_dict(
+        #         (torch.cat(item_list, 0) for item_list in nv_sample_list),
+        #         prefix='nv_')
+
+    # else:
+
+    # st()
+        cano_sample = self.single_sample_create_dict(
+            (torch.cat(item_list, 0) for item_list in cano_sample_list),
+            prefix='')  # torch.Size([4, 4, 10, 256, 256])
+
+        nv_sample = self.single_sample_create_dict(
+            (torch.cat(item_list, 0) for item_list in nv_sample_list),
+            prefix='nv_')
+
+        ret_dict = {
+            **cano_sample,
+            **nv_sample,
+        }
+
+        if not self.load_pcd:
+            ret_dict.update({'caption': sample[-2], 'ins': sample[-1]})
+
+        else:
+            if self.frame_0_as_canonical:
+                # fps_pcd = rearrange( sample[-1], 'B V ... -> (B V) ...')  # ! wrong order.
+                # if self.chunk_size == 8:
+                fps_pcd = rearrange(
+                    sample[-1], 'B V ... -> (V B) ...')  # mimic torch.repeat
+                # else:
+                #     fps_pcd = rearrange( sample[-1], 'B V ... -> (B V) ...')  # ugly code to match the input format...
+            else:
+                fps_pcd = sample[-1].repeat(
+                    2, 1,
+                    1)  # mimic torch.cat(), from torch.Size([3, 4096, 3])
+
+            ret_dict.update({
+                'caption': sample[-3],
+                'ins': sample[-2],
+                'fps_pcd': fps_pcd
+            })
+
+        return ret_dict
+
+    def prepare_mv_input(self, sample):
+
+        # sample = [item[0] for item in sample] # wds wrap items in []
+        bs = len(sample['caption'])  # number of instances
+        chunk_size = sample['img'].shape[0] // bs
+
+        assert self.split_chunk_input
+
+        for k, v in sample.items():
+            if isinstance(v, torch.Tensor) and k != 'fps_pcd':
+                sample[k] = rearrange(v, "b f c ... -> (b f) c ...",
+                                      f=self.V).contiguous()
+
+        # # ! shift nv
+        # else:
+        #     for k, v in sample.items():
+        #         if k not in ['ins', 'caption']:
+
+        #             rolled_idx = torch.LongTensor(
+        #                 list(
+        #                     itertools.chain.from_iterable(
+        #                         list(range(i, sample['img'].shape[0], bs))
+        #                         for i in range(bs))))
+
+        #             v = torch.index_select(v, dim=0, index=rolled_idx)
+        #         sample[k] = v
+
+        #     # img = sample['img']
+        #     # gt = sample['nv_img']
+        #     # torchvision.utils.save_image(img[0], 'inp.jpg', normalize=True)
+        #     # torchvision.utils.save_image(gt[0], 'nv.jpg', normalize=True)
+
+        #     for k, v in sample.items():
+        #         if 'nv' in k:
+        #             rolled_idx = torch.LongTensor(
+        #                 list(
+        #                     itertools.chain.from_iterable(
+        #                         list(
+        #                             np.roll(
+        #                                 np.arange(i * chunk_size, (i + 1) *
+        #                                           chunk_size), 4)
+        #                             for i in range(bs)))))
+
+        #             v = torch.index_select(v, dim=0, index=rolled_idx)
+        #             sample[k] = v
+
+        # torchvision.utils.save_image(sample['nv_img'], 'nv.png', normalize=True)
+        # torchvision.utils.save_image(sample['img'], 'inp.png', normalize=True)
+
+        return sample
+
+
+def load_dataset(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_lmdb=False,
+        use_wds=False,
+        use_chunk=False,
+        use_lmdb_compressed=False,
+        infi_sampler=True):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # st()
+    if use_wds:
+        return load_wds_data(file_path, reso, reso_encoder, batch_size,
+                             num_workers)
+
+    if use_lmdb:
+        logger.log('using LMDB dataset')
+        # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+
+        if use_lmdb_compressed:
+            if 'nv' in trainer_name:
+                dataset_cls = Objv_LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+            else:
+                dataset_cls = Objv_LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        else:
+            if 'nv' in trainer_name:
+                dataset_cls = Objv_LMDBDataset_NV_NoCompressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+            else:
+                dataset_cls = Objv_LMDBDataset_MV_NoCompressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+
+        # dataset = dataset_cls(file_path)
+    elif use_chunk:
+        dataset_cls = ChunkObjaverseDataset
+    else:
+        if 'nv' in trainer_name:
+            dataset_cls = NovelViewObjverseDataset
+        else:
+            dataset_cls = MultiViewObjverseDataset  # 1.5-2iter/s
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size)
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+
+    if use_chunk:
+
+        def chunk_collate_fn(sample):
+            # st()
+            default_collate_sample = torch.utils.data.default_collate(
+                sample[0])
+            st()
+            return default_collate_sample
+
+        collate_fn = chunk_collate_fn
+    else:
+        collate_fn = None
+
+    loader = DataLoader(dataset,
+                        batch_size=batch_size,
+                        num_workers=num_workers,
+                        drop_last=False,
+                        pin_memory=True,
+                        persistent_workers=num_workers > 0,
+                        shuffle=use_chunk,
+                        collate_fn=collate_fn)
+    return loader
+
+
+def chunk_collate_fn(sample):
+    sample = torch.utils.data.default_collate(sample)
+    # ! change from stack to cat
+    # sample = self.post_process.prepare_mv_input(sample)
+
+    bs = len(sample['caption'])  # number of instances
+    # chunk_size = sample['img'].shape[0] // bs
+
+    def merge_internal_batch(sample, merge_b_only=False):
+        for k, v in sample.items():
+            if isinstance(v, torch.Tensor):
+                if v.ndim > 1:
+                    if k == 'fps_pcd' or merge_b_only:
+                        sample[k] = rearrange(
+                            v,
+                            "b1 b2 ... -> (b1 b2) ...").float().contiguous()
+
+                    else:
+                        sample[k] = rearrange(
+                            v, "b1 b2 f c ... -> (b1 b2 f) c ...").float(
+                            ).contiguous()
+                elif k == 'tanfov':
+                    sample[k] = v[0].float().item()  # tanfov.
+
+    if isinstance(sample['c'], dict):  # 3dgs
+        merge_internal_batch(sample['c'], merge_b_only=True)
+        merge_internal_batch(sample['nv_c'], merge_b_only=True)
+
+    merge_internal_batch(sample)
+
+    return sample
+
+def chunk_ddpm_collate_fn(sample):
+    sample = torch.utils.data.default_collate(sample)
+    # ! change from stack to cat
+    # sample = self.post_process.prepare_mv_input(sample)
+
+    # bs = len(sample['caption'])  # number of instances
+    # chunk_size = sample['img'].shape[0] // bs
+
+    def merge_internal_batch(sample, merge_b_only=False):
+        for k, v in sample.items():
+            if isinstance(v, torch.Tensor):
+                if v.ndim > 1:
+                    # if k in ['c', 'latent']:
+                    sample[k] = rearrange(
+                        v,
+                        "b1 b2 ... -> (b1 b2) ...").float().contiguous()
+
+                    # else: # img
+                    #     sample[k] = rearrange(
+                    #         v, "b1 b2 f ... -> (b1 b2 f) ...").float(
+                    #         ).contiguous()
+
+            else: # caption & ins
+                v = [v[i][0] for i in range(len(v))]
+
+    merge_internal_batch(sample)
+
+    # if 'caption' in sample:
+    #     sample['caption'] = sample['caption'][0] + sample['caption'][1]
+
+    return sample
+
+
+
+
+def load_data_cls(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_lmdb=False,
+        use_wds=False,
+        use_chunk=False,
+        use_lmdb_compressed=False,
+        # plucker_embedding=False,
+        # frame_0_as_canonical=False,
+        infi_sampler=True,
+        load_latent=False,
+        return_dataset=False,
+        load_caption_dataset=False,
+        load_mv_dataset=False,
+        **kwargs):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # st()
+    # if use_lmdb:
+    #     logger.log('using LMDB dataset')
+    #     # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+    #     if 'nv' in trainer_name:
+    #         dataset_cls = Objv_LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+    #     else:
+    #         dataset_cls = Objv_LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+
+    #     # dataset = dataset_cls(file_path)
+
+    collate_fn = None
+
+    if use_lmdb:
+        logger.log('using LMDB dataset')
+        # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+
+        if use_lmdb_compressed:
+            if 'nv' in trainer_name:
+                dataset_cls = Objv_LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+            else:
+                dataset_cls = Objv_LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        else:
+            if 'nv' in trainer_name:
+                dataset_cls = Objv_LMDBDataset_NV_NoCompressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+            else:
+                dataset_cls = Objv_LMDBDataset_MV_NoCompressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+
+    elif use_chunk:
+        if load_latent:
+
+            # if 'gs_cam_format' in kwargs:
+            if kwargs['gs_cam_format']:
+                if load_caption_dataset:
+                    dataset_cls = ChunkObjaverseDatasetDDPMgsT23D
+                    collate_fn = chunk_ddpm_collate_fn
+                else:
+                    if load_mv_dataset:
+                        # dataset_cls = ChunkObjaverseDatasetDDPMgsMV23D # ! if multi-view 
+                        dataset_cls = ChunkObjaverseDatasetDDPMgsMV23DSynthetic # ! if multi-view 
+                        # collate_fn = chunk_ddpm_collate_fn
+                        collate_fn = None
+                    else:
+                        dataset_cls = ChunkObjaverseDatasetDDPMgsI23D
+                        collate_fn = None
+            else:
+                dataset_cls = ChunkObjaverseDatasetDDPM
+                collate_fn = chunk_ddpm_collate_fn
+        else:
+            dataset_cls = ChunkObjaverseDataset
+            collate_fn = chunk_collate_fn
+
+    else:
+        if 'nv' in trainer_name:
+            dataset_cls = NovelViewObjverseDataset  # 1.5-2iter/s
+        else:
+            dataset_cls = MultiViewObjverseDataset
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size,
+                          **kwargs
+                          #   plucker_embedding=plucker_embedding
+                          )
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+
+    # st()
+    return dataset
+
+
+
+def load_data(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_lmdb=False,
+        use_wds=False,
+        use_chunk=False,
+        use_lmdb_compressed=False,
+        # plucker_embedding=False,
+        # frame_0_as_canonical=False,
+        infi_sampler=True,
+        load_latent=False,
+        return_dataset=False,
+        load_caption_dataset=False,
+        load_mv_dataset=False,
+        **kwargs):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # st()
+    # if use_lmdb:
+    #     logger.log('using LMDB dataset')
+    #     # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+    #     if 'nv' in trainer_name:
+    #         dataset_cls = Objv_LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+    #     else:
+    #         dataset_cls = Objv_LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+
+    #     # dataset = dataset_cls(file_path)
+
+    collate_fn = None
+
+    if use_lmdb:
+        logger.log('using LMDB dataset')
+        # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+
+        if use_lmdb_compressed:
+            if 'nv' in trainer_name:
+                dataset_cls = Objv_LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+            else:
+                dataset_cls = Objv_LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        else:
+            if 'nv' in trainer_name:
+                dataset_cls = Objv_LMDBDataset_NV_NoCompressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+            else:
+                dataset_cls = Objv_LMDBDataset_MV_NoCompressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+
+    elif use_chunk:
+        # st()
+        if load_latent:
+
+            if kwargs['gs_cam_format']:
+                if load_caption_dataset:
+                    dataset_cls = ChunkObjaverseDatasetDDPMgsT23D
+                    # collate_fn = chunk_ddpm_collate_fn
+                    collate_fn = None
+                else:
+                    if load_mv_dataset:
+                        # dataset_cls = ChunkObjaverseDatasetDDPMgsMV23D
+                        dataset_cls = ChunkObjaverseDatasetDDPMgsMV23DSynthetic # ! if multi-view 
+                        # collate_fn = chunk_ddpm_collate_fn
+                        collate_fn = None
+                    else:
+                        # dataset_cls = ChunkObjaverseDatasetDDPMgsI23D # load i23d
+                        # collate_fn = None
+                        # load mv dataset for i23d
+                        dataset_cls = ChunkObjaverseDatasetDDPMgsI23D_loadMV
+                        collate_fn = chunk_ddpm_collate_fn
+            else:
+                dataset_cls = ChunkObjaverseDatasetDDPM
+                collate_fn = chunk_ddpm_collate_fn
+        else:
+            dataset_cls = ChunkObjaverseDataset
+            collate_fn = chunk_collate_fn
+
+    else:
+        if 'nv' in trainer_name:
+            dataset_cls = NovelViewObjverseDataset  # 1.5-2iter/s
+        else:
+            dataset_cls = MultiViewObjverseDataset
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size,
+                          **kwargs
+                          #   plucker_embedding=plucker_embedding
+                          )
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+
+    # st()
+    if return_dataset:
+        return dataset
+
+    assert infi_sampler
+    if infi_sampler:
+        train_sampler = DistributedSampler(dataset=dataset,
+                                           shuffle=True,
+                                           drop_last=True)
+
+        loader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            drop_last=True,
+            pin_memory=True,
+            persistent_workers=num_workers > 0,
+            sampler=train_sampler,
+            collate_fn=collate_fn,
+            # prefetch_factor=3 if num_workers>0 else None,
+        )
+
+        while True:
+            yield from loader
+
+    # else:
+    #     # loader = DataLoader(dataset,
+    #     #                     batch_size=batch_size,
+    #     #                     num_workers=num_workers,
+    #     #                     drop_last=False,
+    #     #                     pin_memory=True,
+    #     #                     persistent_workers=num_workers > 0,
+    #     #                     shuffle=False)
+    #     st()
+    #     return dataset
+
+
+def load_eval_data(
+    file_path="",
+    reso=64,
+    reso_encoder=224,
+    batch_size=1,
+    num_workers=1,
+    load_depth=False,
+    preprocess=None,
+    imgnet_normalize=True,
+    interval=1,
+    use_lmdb=False,
+    plucker_embedding=False,
+    load_real=False,
+    load_mv_real=False,
+    load_gso=False,
+    four_view_for_latent=False,
+    shuffle_across_cls=False,
+    load_extra_36_view=False,
+    gs_cam_format=False,
+    single_view_for_i23d=False,
+    use_chunk=False,
+    **kwargs,
+):
+    collate_fn = None
+
+    if use_lmdb:
+        logger.log('using LMDB dataset')
+        dataset_cls = Objv_LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        dataset = dataset_cls(file_path,
+                              reso,
+                              reso_encoder,
+                              test=True,
+                              preprocess=preprocess,
+                              load_depth=load_depth,
+                              imgnet_normalize=imgnet_normalize,
+                              interval=interval)
+    elif use_chunk:
+        dataset = ChunkObjaverseDataset(
+            file_path,
+            reso,
+            reso_encoder,
+            test=False,
+            preprocess=preprocess,
+            load_depth=load_depth,
+            imgnet_normalize=imgnet_normalize,
+            #   dataset_size=dataset_size,
+            gs_cam_format=gs_cam_format,
+            plucker_embedding=plucker_embedding,
+            wds_split_all=2,
+            #   frame_0_as_canonical=frame_0_as_canonical,
+            **kwargs)
+        collate_fn = chunk_collate_fn
+
+    elif load_real:
+        if load_mv_real:
+            dataset_cls = RealMVDataset
+        elif load_gso:
+            # st()
+            dataset_cls = RealDataset_GSO
+        else: # single-view i23d
+            dataset_cls = RealDataset
+         
+        dataset = dataset_cls(file_path,
+                              reso,
+                              reso_encoder,
+                              preprocess=preprocess,
+                              load_depth=load_depth,
+                              test=True,
+                              imgnet_normalize=imgnet_normalize,
+                              interval=interval,
+                              plucker_embedding=plucker_embedding)
+
+    else:
+        dataset = MultiViewObjverseDataset(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            load_depth=load_depth,
+            test=True,
+            imgnet_normalize=imgnet_normalize,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            four_view_for_latent=four_view_for_latent,
+            load_extra_36_view=load_extra_36_view,
+            shuffle_across_cls=shuffle_across_cls,
+            gs_cam_format=gs_cam_format,
+            single_view_for_i23d=single_view_for_i23d,
+            **kwargs)
+
+    print('eval dataset size: {}'.format(len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset)
+    loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+        shuffle=False,
+        collate_fn=collate_fn,
+    )
+    # sampler=train_sampler)
+    # return loader
+    return iter(loader)
+
+
+def load_data_for_lmdb(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        shuffle_across_cls=False,
+        four_view_for_latent=False,
+        wds_split=1):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # if 'nv' in trainer_name:
+    #     dataset_cls = NovelViewDataset
+    # else:
+    # dataset_cls = MultiViewDataset
+    # st()
+    # dataset_cls = MultiViewObjverseDatasetforLMDB
+    dataset_cls = MultiViewObjverseDatasetforLMDB_nocaption
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size,
+                          shuffle_across_cls=shuffle_across_cls,
+                          wds_split=wds_split,
+                          four_view_for_latent=four_view_for_latent)
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset, shuffle=True, drop_last=True)
+    loader = DataLoader(
+        dataset,
+        shuffle=False,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+        # prefetch_factor=2,
+        # prefetch_factor=3,
+        pin_memory=True,
+        persistent_workers=num_workers > 0,
+    )
+    # sampler=train_sampler)
+
+    # while True:
+    #     yield from loader
+    return loader, dataset.dataset_name, len(dataset)
+
+
+def load_lmdb_for_lmdb(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        trainer_name='input_rec'):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # if 'nv' in trainer_name:
+    #     dataset_cls = NovelViewDataset
+    # else:
+    # dataset_cls = MultiViewDataset
+    # st()
+    dataset_cls = Objv_LMDBDataset_MV_Compressed_for_lmdb
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size)
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset, shuffle=True, drop_last=True)
+    loader = DataLoader(
+        dataset,
+        shuffle=False,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+        # prefetch_factor=2,
+        # prefetch_factor=3,
+        pin_memory=True,
+        persistent_workers=True,
+    )
+    # sampler=train_sampler)
+
+    # while True:
+    #     yield from loader
+    return loader, len(dataset)
+
+
+def load_memory_data(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        num_workers=1,
+        #  load_depth=True,
+        preprocess=None,
+        imgnet_normalize=True,
+        use_chunk=True,
+        **kwargs):
+    # load a single-instance into the memory to speed up training IO
+    # dataset = MultiViewObjverseDataset(file_path,
+
+    collate_fn = None
+
+    if use_chunk:
+        dataset_cls = ChunkObjaverseDataset
+        collate_fn = chunk_collate_fn
+    else:
+        dataset_cls = NovelViewObjverseDataset
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          preprocess=preprocess,
+                          load_depth=True,
+                          test=False,
+                          overfitting=True,
+                          imgnet_normalize=imgnet_normalize,
+                          overfitting_bs=batch_size,
+                          **kwargs)
+    logger.log('!!!!!!! memory dataset size: {} !!!!!!'.format(len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset)
+    loader = DataLoader(
+        dataset,
+        batch_size=len(dataset),
+        num_workers=num_workers,
+        drop_last=False,
+        shuffle=False,
+        collate_fn = collate_fn
+    )
+
+    all_data: dict = next(
+        iter(loader)
+    )  # torchvision.utils.save_image(all_data['img'], 'gt.jpg', normalize=True, value_range=(-1,1))
+
+    # st()
+
+    if kwargs.get('gs_cam_format', False):  # gs rendering pipeline
+        # ! load V=4 images for training in a batch.
+        while True:
+            # st()
+
+            # indices = torch.randperm(len(dataset))[:4]
+            indices = torch.randperm(
+                len(dataset) * 2)[:batch_size]  # all instances
+            # indices2 = torch.randperm(len(dataset))[:] # all instances
+
+            batch_c = collections.defaultdict(dict)
+            V = all_data['c']['source_cv2wT_quat'].shape[1]
+            for k in ['c', 'nv_c']:
+                for k_c, v_c in all_data[k].items():
+                    if k_c == 'tanfov':
+                        continue
+                    try:
+                        batch_c[k][
+                            k_c] = torch.index_select(  # ! chunk data reading pipeline
+                                v_c,
+                                dim=0,
+                                index=indices
+                            ).reshape(batch_size, V, *v_c.shape[2:]).float(
+                            ) if isinstance(
+
+                                v_c,
+                                torch.Tensor) else v_c  # float
+                    except Exception as e:
+                        st()
+                        print(e)
+
+            # ! read chunk not required, already float
+            batch_c['c']['tanfov'] = all_data['c']['tanfov']
+            batch_c['nv_c']['tanfov'] = all_data['nv_c']['tanfov']
+
+            indices_range = torch.arange(indices[0]*V, (indices[0]+1)*V)
+            batch_data = {}
+            for k, v in all_data.items():
+                if k not in ['c', 'nv_c']:
+                    try:
+                        if k == 'fps_pcd':
+                            batch_data[k] = torch.index_select(
+                                v, dim=0, index=indices).float() if isinstance(
+                                    v, torch.Tensor) else v  # float
+                        else:
+                            batch_data[k] = torch.index_select(
+                                v, dim=0, index=indices_range).float() if isinstance(
+                                    v, torch.Tensor) else v  # float
+                    except:
+                        st()
+                        print(e)
+
+            memory_batch_data = {
+                **batch_data,
+                **batch_c,
+            }
+
+
+            yield memory_batch_data
+
+    else:
+        while True:
+            start_idx = np.random.randint(0, len(dataset) - batch_size + 1)
+            yield {
+                k: v[start_idx:start_idx + batch_size]
+                for k, v in all_data.items()
+            }
+
+
+def read_dnormal(normald_path, cond_pos, h=None, w=None):
+    cond_cam_dis = np.linalg.norm(cond_pos, 2)
+
+    near = 0.867  #sqrt(3) * 0.5
+    near_distance = cond_cam_dis - near
+
+    normald = cv2.imread(normald_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
+    normal, depth = normald[..., :3], normald[..., 3:]
+
+    depth[depth < near_distance] = 0
+
+    if h is not None:
+        assert w is not None
+        if depth.shape[1] != h:
+            depth = cv2.resize(depth, (h, w), interpolation=cv2.INTER_NEAREST
+                               )  # 512,512, 1 -> self.reso, self.reso
+            # depth = cv2.resize(depth, (h, w), interpolation=cv2.INTER_LANCZOS4
+            #                    )  # ! may fail if nearest. dirty data.
+            # st()
+        else:
+            depth = depth[..., 0]
+
+        if normal.shape[1] != h:
+            normal = cv2.resize(normal, (h, w),
+                                interpolation=cv2.INTER_NEAREST
+                                )  # 512,512, 1 -> self.reso, self.reso
+
+    else:
+        depth = depth[..., 0]
+
+    return torch.from_numpy(depth).float(), torch.from_numpy(normal).float()
+
+
+def get_intri(target_im=None, h=None, w=None, normalize=False):
+    if target_im is None:
+        assert (h is not None and w is not None)
+    else:
+        h, w = target_im.shape[:2]
+
+    fx = fy = 1422.222
+    res_raw = 1024
+    f_x = f_y = fx * h / res_raw
+    K = np.array([f_x, 0, w / 2, 0, f_y, h / 2, 0, 0, 1]).reshape(3, 3)
+    if normalize:  # center is [0.5, 0.5], eg3d renderer tradition
+        K[:6] /= h
+    # print("intr: ", K)
+    return K
+
+
+def convert_pose(C2W):
+    # https://github.com/modelscope/richdreamer/blob/c3d9a77fa15fc42dbae12c2d41d64aaec14efd37/dataset/gobjaverse/depth_warp_example.py#L402
+    flip_yz = np.eye(4)
+    flip_yz[1, 1] = -1
+    flip_yz[2, 2] = -1
+    C2W = np.matmul(C2W, flip_yz)
+    return torch.from_numpy(C2W)
+
+
+def read_camera_matrix_single(json_file):
+    with open(json_file, 'r', encoding='utf8') as reader:
+        json_content = json.load(reader)
+    '''
+    # NOTE that different from unity2blender experiments.
+    camera_matrix = np.eye(4)
+    camera_matrix[:3, 0] = np.array(json_content['x'])
+    camera_matrix[:3, 1] = -np.array(json_content['y'])
+    camera_matrix[:3, 2] = -np.array(json_content['z'])
+    camera_matrix[:3, 3] = np.array(json_content['origin'])
+
+
+    '''
+    camera_matrix = np.eye(4)  # blender-based
+    camera_matrix[:3, 0] = np.array(json_content['x'])
+    camera_matrix[:3, 1] = np.array(json_content['y'])
+    camera_matrix[:3, 2] = np.array(json_content['z'])
+    camera_matrix[:3, 3] = np.array(json_content['origin'])
+    # print(camera_matrix)
+    # '''
+
+    # return convert_pose(camera_matrix)
+    return camera_matrix
+
+
+def unity2blender(normal):
+    normal_clone = normal.copy()
+    normal_clone[..., 0] = -normal[..., -1]
+    normal_clone[..., 1] = -normal[..., 0]
+    normal_clone[..., 2] = normal[..., 1]
+
+    return normal_clone
+
+def unity2blender_fix(normal): # up blue, left green, front (towards inside) red
+    normal_clone = normal.copy()
+    # normal_clone[..., 0] = -normal[..., 2]
+    # normal_clone[..., 1] = -normal[..., 0]
+    normal_clone[..., 0] = -normal[..., 0] # swap r and g
+    normal_clone[..., 1] = -normal[..., 2]
+    normal_clone[..., 2] = normal[..., 1]
+
+    return normal_clone
+
+def unity2blender_th(normal):
+    assert normal.shape[1] == 3 # B 3 H W...
+    normal_clone = normal.clone()
+    normal_clone[:, 0, ...] = -normal[:, -1, ...]
+    normal_clone[:, 1, ...] = -normal[:, 0, ...]
+    normal_clone[:, 2, ...] =  normal[:, 1, ...]
+
+    return normal_clone
+
+
+def blender2midas(img):
+    '''Blender: rub
+    midas: lub
+    '''
+    img[..., 0] = -img[..., 0]
+    img[..., 1] = -img[..., 1]
+    img[..., -1] = -img[..., -1]
+    return img
+
+
+def current_milli_time():
+    return round(time.time() * 1000)
+
+
+# modified from ShapeNet class
+class MultiViewObjverseDataset(Dataset):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=False,
+            **kwargs):
+        self.load_extra_36_view = load_extra_36_view
+        # st()
+        self.gs_cam_format = gs_cam_format
+        self.frame_0_as_canonical = frame_0_as_canonical
+        self.four_view_for_latent = four_view_for_latent  # export 0 12 30 36, 4 views for reconstruction
+        self.single_view_for_i23d = single_view_for_i23d
+        self.file_path = file_path
+        self.overfitting = overfitting
+        self.scene_scale = scene_scale
+        self.reso = reso
+        self.reso_encoder = reso_encoder
+        self.classes = False
+        self.load_depth = load_depth
+        self.preprocess = preprocess
+        self.plucker_embedding = plucker_embedding
+        self.intrinsics = get_intri(h=self.reso, w=self.reso,
+                                    normalize=True).reshape(9)
+
+        assert not self.classes, "Not support class condition now."
+
+        dataset_name = Path(self.file_path).stem.split('_')[0]
+        self.dataset_name = dataset_name
+
+        self.zfar = 100.0
+        self.znear = 0.01
+
+        # if test:
+        #     self.ins_list = sorted(os.listdir(self.file_path))[0:1]  # the first 1 instance for evaluation reference.
+        # else:
+        # ! TODO, read from list?
+
+        def load_single_cls_instances(file_path):
+            ins_list = []  # the first 1 instance for evaluation reference.
+            # '''
+            # for dict_dir in os.listdir(file_path)[:]:
+            # for dict_dir in os.listdir(file_path)[:]:
+            for dict_dir in os.listdir(file_path):
+                # for dict_dir in os.listdir(file_path)[:2]:
+                for ins_dir in os.listdir(os.path.join(file_path, dict_dir)):
+
+                    # self.ins_list.append(os.path.join(self.file_path, dict_dir, ins_dir,))
+                    # /nas/shared/V2V/yslan/logs/nips23/Reconstruction/final/objav/vae/MV/170K/infer-latents/189w/v=6-rotate/latent_dir
+                    # st() # check latent whether saved
+                    # root = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/final/objav/vae/MV/170K/infer-latents/189w/v=6-rotate/latent_dir'
+                    # if os.path.exists(os.path.join(root,file_path.split('/')[-1], dict_dir, ins_dir, 'latent.npy') ):
+                    # continue
+                    # pcd_root = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/pcd-V=8_24576_polish'
+                    # pcd_root = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/pcd-V=10_4096_polish'
+                    # if os.path.exists(
+                    #         os.path.join(pcd_root, 'fps-pcd',
+                    #                      file_path.split('/')[-1], dict_dir,
+                    #                      ins_dir, 'fps-4096.ply')):
+                    #     continue
+
+                    # ! split=8 has some missing instances
+                    # root = '/cpfs01/user/lanyushi.p/data/chunk-jpeg-normal/bs_16_fixsave3/170K/384/'
+                    # if os.path.exists(os.path.join(root,file_path.split('/')[-1], dict_dir, ins_dir,) ):
+                    #     continue
+                    # else:
+                    # ins_list.append(
+                    #     os.path.join(file_path, dict_dir, ins_dir,
+                    #                 'campos_512_v4'))
+
+                    # filter out some data
+                    if not os.path.exists(os.path.join(file_path, dict_dir, ins_dir, 'campos_512_v2')):
+                        continue
+                    if not os.path.exists(os.path.join(file_path, dict_dir, ins_dir, 'campos_512_v2', '00025', '00025.png')):
+                        continue
+                    if len(os.listdir(os.path.join(file_path, dict_dir, ins_dir, 'campos_512_v2'))) != 40:
+                        continue
+
+                    ins_list.append(
+                        os.path.join(file_path, dict_dir, ins_dir,
+                                    'campos_512_v2'))
+
+            # '''
+            # check pcd performnace
+            # ins_list.append(
+            #     os.path.join(file_path, '0', '10634',
+            #                     'campos_512_v4'))
+            return ins_list
+
+        # st()
+        self.ins_list = []
+        # for subset in ['Animals', 'Transportations_tar', 'Furnitures']:
+        # for subset in ['Furnitures']:
+        # selected subset for training
+        # if False:
+        if True:
+            for subset in [  # ! around 17W instances in total. 
+                    # 'Animals',
+                    # 'BuildingsOutdoor',
+                    # 'daily-used',
+                    # 'Furnitures',
+                    # 'Food',
+                    # 'Plants',
+                    # 'Electronics',
+                    # 'Transportations_tar',
+                    # 'Human-Shape',
+                    'gobjaverse_alignment_unzip',
+            ]:  # selected subset for training
+
+                # if os.path.exists(f'{self.file_path}/{subset}.txt'):
+                # dataset_list = f'{self.file_path}/{subset}_filtered.txt'
+                dataset_list = f'{self.file_path}/{subset}_filtered_more.txt'
+                assert os.path.exists(dataset_list)
+                if os.path.exists(dataset_list):
+                    with open(dataset_list, 'r') as f:
+                        self.ins_list += [os.path.join(self.file_path, item.strip()) for item in f.readlines()]
+                else:
+                    self.ins_list += load_single_cls_instances(
+                        os.path.join(self.file_path, subset))
+
+                # st()
+                # current_time = int(current_milli_time()
+                #                    )  # randomly shuffle given current time
+                # random.seed(current_time)
+                # random.shuffle(self.ins_list)
+
+        else:  # preprocess single class
+            self.ins_list = load_single_cls_instances(self.file_path)
+
+        self.ins_list = sorted(self.ins_list)
+
+        if overfitting:
+            self.ins_list = self.ins_list[:1]
+
+        self.rgb_list = []
+        self.frame0_pose_list = []
+        self.pose_list = []
+        self.depth_list = []
+        self.data_ins_list = []
+        self.instance_data_length = -1
+
+        # self.pcd_path = Path('/cpfs01/shared/V2V/V2V_hdd/yslan/logs/nips23/Reconstruction/pcd-V=6/fps-pcd')
+        self.pcd_path = Path(
+            '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/pcd-V=6/fps-pcd')
+
+        with open(
+                '/nas/shared/public/yslan/data/text_captions_cap3d.json') as f:
+                # '/nas/shared/V2V/yslan/aigc3d/text_captions_cap3d.json') as f:
+            self.caption_data = json.load(f)
+
+        self.shuffle_across_cls = shuffle_across_cls
+
+
+        # for ins in self.ins_list[47000:]:
+        if four_view_for_latent:  # also saving dense pcd
+            # self.wds_split_all = 1  # ! when dumping latent
+            # self.wds_split_all = 2  # ! when dumping latent
+            # self.wds_split_all = 4
+            # self.wds_split_all = 6
+            # self.wds_split_all = 4
+            # self.wds_split_all = 5
+            # self.wds_split_all = 6
+            # self.wds_split_all = 7
+            # self.wds_split_all = 1
+            self.wds_split_all = 8
+            # self.wds_split_all = 2
+            # ins_list_to_process = self.ins_list
+            all_ins_size = len(self.ins_list)
+            ratio_size = all_ins_size // self.wds_split_all + 1
+            # ratio_size = int(all_ins_size / self.wds_split_all) + 1
+
+            ins_list_to_process = self.ins_list[ratio_size *
+                                                (wds_split):ratio_size *
+                                                (wds_split + 1)]
+
+        else:  # ! create shards dataset
+            # self.wds_split_all = 4
+            self.wds_split_all = 8
+            # self.wds_split_all = 1
+            all_ins_size = len(self.ins_list)
+
+            random.seed(0)
+            random.shuffle(self.ins_list) # avoid same category appears in the same shard
+
+            ratio_size = all_ins_size // self.wds_split_all + 1
+
+            ins_list_to_process = self.ins_list[ratio_size * # 1 - 8
+                                                (wds_split - 1):ratio_size *
+                                                wds_split]
+
+        # uniform_sample = False
+        uniform_sample = True
+        # st()
+        for ins in tqdm(ins_list_to_process):
+            # ins = os.path.join(
+            #     # self.file_path, ins , 'campos_512_v4'
+            #     self.file_path, ins ,
+            #     # 'compos_512_v4'
+            # )
+            # cur_rgb_path = os.path.join(self.file_path, ins, 'compos_512_v4')
+            # cur_pose_path = os.path.join(self.file_path, ins, 'pose')
+
+            # st()
+            # ][:27])
+
+            if self.four_view_for_latent:
+                # cur_all_fname = [t.split('.')[0] for t in os.listdir(ins)
+                #                  ]  # use full set for training
+                # cur_all_fname = [f'{idx:05d}' for idx in [0, 12, 30, 36]
+                # cur_all_fname = [f'{idx:05d}' for idx in [6,12,18,24]
+                # cur_all_fname = [f'{idx:05d}' for idx in [7,16,24,25]
+                # cur_all_fname = [f'{idx:05d}' for idx in [25,26,0,9,18,27,33,39]]
+                cur_all_fname = [
+                    f'{idx:05d}'
+                    for idx in [25, 26, 6, 12, 18, 24, 27, 31, 35, 39] # ! for extracting PCD
+                ]
+                # cur_all_fname = [f'{idx:05d}' for idx in [25,26,0,9,18,27,30,33,36,39]] # more down side for better bottom coverage.
+                # cur_all_fname = [f'{idx:05d}' for idx in [25,0, 7,15]]
+                # cur_all_fname = [f'{idx:05d}' for idx in [4,12,20,25,26]
+                # cur_all_fname = [f'{idx:05d}' for idx in [6,12,18,24,25,26]
+                # cur_all_fname = [f'{idx:05d}' for idx in [6,12,18,24,25,26, 39, 33, 27]
+                # cur_all_fname = [f'{idx:05d}' for idx in [6,12,18,24,25,26, 39, 33, 27]
+
+                # cur_all_fname = [
+                #     f'{idx:05d}' for idx in [25, 26, 27, 30, 33, 36]
+                # ]  # for pcd unprojection
+
+                # cur_all_fname = [
+                #     f'{idx:05d}' for idx in [25, 26, 27, 30] # ! for infer latents
+                # ]  #
+
+                # cur_all_fname = [
+                #     f'{idx:05d}' for idx in [25, 27, 29, 31, 33, 35, 37
+                #                              ]  # ! for infer latents
+                # ]  #
+
+                # cur_all_fname = [
+                #     f'{idx:05d}' for idx in [25, 27, 31, 35
+                #                              ]  # ! for infer latents
+                # ]  #
+
+                # cur_all_fname += [f'{idx:05d}' for idx in range(40) if idx not in [0,12,30,36]] # ! four views for inference
+            elif self.single_view_for_i23d:
+                # cur_all_fname = [f'{idx:05d}'
+                #                  for idx in [16]]  # 20 is also fine
+                cur_all_fname = [f'{idx:05d}'
+                                 for idx in [2]]  # ! furniture side view
+
+            else:
+                cur_all_fname = [t.split('.')[0] for t in os.listdir(ins)
+                                 ]  # use full set for training
+
+                if shuffle_across_cls:
+                    if uniform_sample:
+                        cur_all_fname = sorted(cur_all_fname)
+                        # 0-24, 25 views
+                        # 25,26, 2 views
+                        # 27-39, 13 views
+                        uniform_all_fname = []
+
+                        # !!!! if bs=9 or 8
+                        for idx in range(6):
+                            if idx % 2 == 0:
+                                chunk_all_fname = [25]
+                            else:
+                                chunk_all_fname = [26]
+                            # chunk_all_fname = [25] # no bottom view required as input
+                            # start_1 = np.random.randint(0,5) # for first 24 views
+                            # chunk_all_fname += [start_1+uniform_idx for uniform_idx in range(0,25,5)]
+
+                            start_1 = np.random.randint(0,4) # for first 24 views, v=8
+                            chunk_all_fname += [start_1+uniform_idx for uniform_idx in range(0,25,7)] # [0-21]
+
+                            start_2 = np.random.randint(0,5) + 27 # for first 24 views
+                            chunk_all_fname += [start_2, start_2 + 4, start_2 + 8]
+                            assert len(chunk_all_fname) == 8, len(chunk_all_fname)
+                            uniform_all_fname += [cur_all_fname[fname] for fname in chunk_all_fname]
+
+                        # ! if bs=6
+                        # for idx in range(8):
+
+                        #     if idx % 2 == 0:
+                        #         chunk_all_fname = [
+                        #             25
+                        #         ]  # no bottom view required as input
+                        #     else:
+                        #         chunk_all_fname = [
+                        #             26
+                        #         ]  # no bottom view required as input
+
+                        #     start_1 = np.random.randint(
+                        #         0, 7)  # for first 24 views
+                        #     # chunk_all_fname += [start_1+uniform_idx for uniform_idx in range(0,25,5)]
+                        #     chunk_all_fname += [
+                        #         start_1 + uniform_idx
+                        #         for uniform_idx in range(0, 25, 9)
+                        #     ]  # 0 9 18
+                        #     start_2 = np.random.randint(
+                        #         0, 7) + 27  # for first 24 views
+                        #     # chunk_all_fname += [start_2, start_2 + 4, start_2 + 8]
+                        #     chunk_all_fname += [start_2,
+                        #                         start_2 + 6]  # 2 frames
+                        #     assert len(chunk_all_fname) == 6
+                        #     uniform_all_fname += [
+                        #         cur_all_fname[fname]
+                        #         for fname in chunk_all_fname
+                        #     ]
+
+                        cur_all_fname = uniform_all_fname
+
+                    else:
+                        current_time = int(current_milli_time(
+                        ))  # randomly shuffle given current time
+                        random.seed(current_time)
+                        random.shuffle(cur_all_fname)
+                else:
+                    cur_all_fname = sorted(cur_all_fname)
+
+                # ! skip the check
+                # if self.instance_data_length == -1:
+                #     self.instance_data_length = len(cur_all_fname)
+                # else:
+                #     try:  # data missing?
+                #         assert len(cur_all_fname) == self.instance_data_length
+                #     except:
+                #         # with open('error_log.txt', 'a') as f:
+                #         #     f.write(str(e) + '\n')
+                #         with open('missing_ins_new2.txt', 'a') as f:
+                #             f.write(str(Path(ins.parent)) +
+                #                     '\n')  # remove the "campos_512_v4"
+                #         continue
+
+            # if test: # use middle image as the novel view model input
+            #     mid_index = len(cur_all_fname) // 3 * 2
+            #     cur_all_fname.insert(0, cur_all_fname[mid_index])
+
+            self.frame0_pose_list += ([
+                os.path.join(ins, fname, fname + '.json')
+                for fname in [cur_all_fname[0]]
+            ] * len(cur_all_fname))
+
+            self.pose_list += ([
+                os.path.join(ins, fname, fname + '.json')
+                for fname in cur_all_fname
+            ])
+            self.rgb_list += ([
+                os.path.join(ins, fname, fname + '.png')
+                for fname in cur_all_fname
+            ])
+
+            self.depth_list += ([
+                os.path.join(ins, fname, fname + '_nd.exr')
+                for fname in cur_all_fname
+            ])
+            self.data_ins_list += ([ins] * len(cur_all_fname))
+
+        # check
+
+        # ! setup normalizataion
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        # st()
+        self.normalize = transforms.Compose(transformations)
+
+    def get_source_cw2wT(self, source_cameras_view_to_world):
+        return matrix_to_quaternion(
+            source_cameras_view_to_world[:3, :3].transpose(0, 1))
+
+    def c_to_3dgs_format(self, pose):
+        # TODO, switch to torch version (batched later)
+
+        c2w = pose[:16].reshape(4, 4)  # 3x4
+
+        # ! load cam
+        w2c = np.linalg.inv(c2w)
+        R = np.transpose(
+            w2c[:3, :3])  # R is stored transposed due to 'glm' in CUDA code
+        T = w2c[:3, 3]
+        fx = pose[16]
+        FovX = focal2fov(fx, 1)
+        FovY = focal2fov(fx, 1)
+
+        tanfovx = math.tan(FovX * 0.5)
+        tanfovy = math.tan(FovY * 0.5)
+
+        assert tanfovx == tanfovy
+
+        trans = np.array([0.0, 0.0, 0.0])
+        scale = 1.0
+
+        world_view_transform = torch.tensor(getWorld2View2(R, T, trans,
+                                                           scale)).transpose(
+                                                               0, 1)
+        projection_matrix = getProjectionMatrix(znear=self.znear,
+                                                zfar=self.zfar,
+                                                fovX=FovX,
+                                                fovY=FovY).transpose(0, 1)
+        full_proj_transform = (world_view_transform.unsqueeze(0).bmm(
+            projection_matrix.unsqueeze(0))).squeeze(0)
+        camera_center = world_view_transform.inverse()[3, :3]
+
+        view_world_transform = torch.tensor(getView2World(R, T, trans,
+                                                          scale)).transpose(
+                                                              0, 1)
+
+        # item.update(viewpoint_cam=[viewpoint_cam])
+        c = {}
+        c["source_cv2wT_quat"] = self.get_source_cw2wT(view_world_transform)
+        c.update(
+            # projection_matrix=projection_matrix, # K
+            cam_view=world_view_transform,  # world_view_transform
+            cam_view_proj=full_proj_transform,  # full_proj_transform
+            cam_pos=camera_center,
+            tanfov=tanfovx,  # TODO, fix in the renderer
+            # orig_c2w=c2w,
+            # orig_w2c=w2c,
+            orig_pose=torch.from_numpy(pose),
+            orig_c2w=torch.from_numpy(c2w),
+            orig_w2c=torch.from_numpy(w2c),
+            # tanfovy=tanfovy,
+        )
+
+        return c  # dict for gs rendering
+
+    def __len__(self):
+        return len(self.rgb_list)
+
+    def load_bbox(self, mask):
+        # st()
+        nonzero_value = torch.nonzero(mask)
+        height, width = nonzero_value.max(dim=0)[0]
+        top, left = nonzero_value.min(dim=0)[0]
+        bbox = torch.tensor([top, left, height, width], dtype=torch.float32)
+        return bbox
+
+    def __getitem__(self, idx):
+        # try:
+
+        data = self._read_data(idx)
+        return data
+
+        # except Exception as e:
+        #     # with open('error_log_pcd.txt', 'a') as f:
+        #     with open('error_log_pcd.txt', 'a') as f:
+        #         f.write(str(e) + '\n')
+        #     with open('error_idx_pcd.txt', 'a') as f:
+        #         f.write(str(self.data_ins_list[idx]) + '\n')
+        #     print(e, flush=True)
+        #     return {}
+
+    def gen_rays(self, c2w):
+        # Generate rays
+        self.h = self.reso_encoder
+        self.w = self.reso_encoder
+        yy, xx = torch.meshgrid(
+            torch.arange(self.h, dtype=torch.float32) + 0.5,
+            torch.arange(self.w, dtype=torch.float32) + 0.5,
+            indexing='ij')
+
+        # normalize to 0-1 pixel range
+        yy = yy / self.h
+        xx = xx / self.w
+
+        # K = np.array([f_x, 0, w / 2, 0, f_y, h / 2, 0, 0, 1]).reshape(3, 3)
+        cx, cy, fx, fy = self.intrinsics[2], self.intrinsics[
+            5], self.intrinsics[0], self.intrinsics[4]
+        # cx *= self.w
+        # cy *= self.h
+
+        # f_x = f_y = fx * h / res_raw
+        c2w = torch.from_numpy(c2w).float()
+
+        xx = (xx - cx) / fx
+        yy = (yy - cy) / fy
+        zz = torch.ones_like(xx)
+        dirs = torch.stack((xx, yy, zz), dim=-1)  # OpenCV convention
+        dirs /= torch.norm(dirs, dim=-1, keepdim=True)
+        dirs = dirs.reshape(-1, 3, 1)
+        del xx, yy, zz
+        # st()
+        dirs = (c2w[None, :3, :3] @ dirs)[..., 0]
+
+        origins = c2w[None, :3, 3].expand(self.h * self.w, -1).contiguous()
+        origins = origins.view(self.h, self.w, 3)
+        dirs = dirs.view(self.h, self.w, 3)
+
+        return origins, dirs
+
+    def normalize_camera(self, c, c_frame0):
+        # assert c.shape[0] == self.chunk_size  # 8 o r10
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+        canonical_camera_poses = c_frame0[:, :16].reshape(B, 4, 4)
+
+        # if for_encoder:
+
+        # encoder_canonical_idx = [0, self.V]
+        # st()
+        cam_radius = np.linalg.norm(
+            c_frame0[:, :16].reshape(1, 4, 4)[:, :3, 3],
+            axis=-1,
+            keepdims=False)  # since g-buffer adopts dynamic radius here.
+
+        frame1_fixed_pos = np.repeat(np.eye(4)[None], 1, axis=0)
+        frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+        transform = frame1_fixed_pos @ np.linalg.inv(canonical_camera_poses)
+        # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+        # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+        new_camera_poses = np.repeat(
+            transform, 1, axis=0
+        ) @ camera_poses  # [V, 4, 4]. np.repeat() is th.repeat_interleave()
+
+        # else:
+        #     cam_radius = np.linalg.norm(
+        #         c[canonical_idx][:16].reshape(4, 4)[:3, 3],
+        #         axis=-1,
+        #         keepdims=False
+        #     )  # since g-buffer adopts dynamic radius here.
+        #     frame1_fixed_pos = np.eye(4)
+        #     frame1_fixed_pos[2, -1] = -cam_radius
+
+        #     transform = frame1_fixed_pos @ np.linalg.inv(
+        #         camera_poses[canonical_idx])  # 4,4
+        #     # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+        #     # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+        #     new_camera_poses = np.repeat(
+        #         transform[None], self.chunk_size,
+        #         axis=0) @ camera_poses  # [V, 4, 4]
+
+        # st()
+        c = np.concatenate([new_camera_poses.reshape(B, 16), c[:, 16:]],
+                           axis=-1)
+        # st()
+
+        return c
+
+    def _read_data(
+        self,
+        idx,
+    ):
+        rgb_fname = self.rgb_list[idx]
+        pose_fname = self.pose_list[idx]
+
+        raw_img = imageio.imread(rgb_fname)
+
+        # ! RGBD
+        alpha_mask = raw_img[..., -1:] / 255
+        raw_img = alpha_mask * raw_img[..., :3] + (
+            1 - alpha_mask) * np.ones_like(raw_img[..., :3]) * 255
+
+        raw_img = raw_img.astype(
+            np.uint8)  # otherwise, float64 won't call ToTensor()
+
+        # return raw_img
+        # st()
+
+        if self.preprocess is None:
+            img_to_encoder = cv2.resize(raw_img,
+                                        (self.reso_encoder, self.reso_encoder),
+                                        interpolation=cv2.INTER_LANCZOS4)
+            # interpolation=cv2.INTER_AREA)
+            img_to_encoder = img_to_encoder[
+                ..., :3]  #[3, reso_encoder, reso_encoder]
+            img_to_encoder = self.normalize(img_to_encoder)
+        else:
+            img_to_encoder = self.preprocess(Image.open(rgb_fname))  # clip
+
+        # return img_to_encoder
+
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+
+        #  interpolation=cv2.INTER_AREA)
+
+        # img_sr = cv2.resize(raw_img, (512, 512), interpolation=cv2.INTER_AREA)
+        # img_sr = cv2.resize(raw_img, (256, 256), interpolation=cv2.INTER_AREA) # just as refinement, since eg3d uses 64->128 final resolution
+        # img_sr = cv2.resize(raw_img, (128, 128), interpolation=cv2.INTER_AREA) # just as refinement, since eg3d uses 64->128 final resolution
+
+        # img_sr = cv2.resize(
+        #     raw_img, (128, 128), interpolation=cv2.INTER_LANCZOS4
+        # )  # just as refinement, since eg3d uses 64->128 final resolution
+
+        # img = torch.from_numpy(img)[..., :3].permute(
+        #     2, 0, 1) / 255.0  #[3, reso, reso]
+
+        img = torch.from_numpy(img)[..., :3].permute(
+            2, 0, 1
+        ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        # img_sr = torch.from_numpy(img_sr)[..., :3].permute(
+        #     2, 0, 1
+        # ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        c2w = read_camera_matrix_single(pose_fname)  #[1, 4, 4] -> [1, 16]
+        # c = np.concatenate([c2w, self.intrinsics], axis=0).reshape(25)  # 25, no '1' dim needed.
+
+        # return c2w
+
+        # if self.load_depth:
+        # depth, depth_mask, depth_mask_sr = read_dnormal(self.depth_list[idx],
+        # try:
+        depth, normal = read_dnormal(self.depth_list[idx], c2w[:3, 3:],
+                                     self.reso, self.reso)
+
+        # ! frame0 alignment
+        # if self.frame_0_as_canonical:
+
+        # return depth
+        # except:
+        #     # print(self.depth_list[idx])
+        #     raise NotImplementedError(self.depth_list[idx])
+        # if depth
+
+        try:
+            bbox = self.load_bbox(depth > 0)
+        except:
+            print(rgb_fname, flush=True)
+            with open('error_log.txt', 'a') as f:
+                f.write(str(rgb_fname + '\n'))
+            bbox = self.load_bbox(torch.ones_like(depth))
+
+        # plucker
+
+        # ! normalize camera
+
+        c = np.concatenate([c2w.reshape(16), self.intrinsics],
+                           axis=0).reshape(25).astype(
+                               np.float32)  # 25, no '1' dim needed.
+
+        if self.frame_0_as_canonical:  # 4 views as input per batch
+            frame0_pose_name = self.frame0_pose_list[idx]
+            c2w_frame0 = read_camera_matrix_single(
+                frame0_pose_name)  #[1, 4, 4] -> [1, 16]
+            c = self.normalize_camera(c[None], c2w_frame0[None])[0]
+            c2w = c[:16].reshape(4, 4)  # !
+            # st()
+            # pass
+
+        rays_o, rays_d = self.gen_rays(c2w)
+        rays_plucker = torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d],
+                                 dim=-1)  # [h, w, 6]
+
+        img_to_encoder = torch.cat(
+            [img_to_encoder, rays_plucker.permute(2, 0, 1)],
+            0).float()  # concat in C dim
+
+        # ! add depth as input
+
+        depth, normal = read_dnormal(self.depth_list[idx], c2w[:3, 3:],
+                                     self.reso_encoder, self.reso_encoder)
+        normalized_depth = depth.unsqueeze(0)  # min=0
+        img_to_encoder = torch.cat([img_to_encoder, normalized_depth],
+                                   0)  # concat in C dim
+
+        if self.gs_cam_format:
+            c = self.c_to_3dgs_format(c)
+        else:
+            c = torch.from_numpy(c)
+
+        ret_dict = {
+            # 'rgb_fname': rgb_fname,
+            'img_to_encoder': img_to_encoder,
+            'img': img,
+            'c': c,
+            # 'img_sr': img_sr,
+            # 'ins_name': self.data_ins_list[idx]
+        }
+
+        # ins = str(
+        #     (Path(self.data_ins_list[idx]).relative_to(self.file_path)).parent)
+
+        pcd_ins = Path(self.data_ins_list[idx]).relative_to(
+            Path(self.file_path).parent).parent
+        # load pcd
+        # fps_pcd = pcu.load_mesh_v(
+        #     str(self.pcd_path / pcd_ins / 'fps-10000.ply'))
+
+        ins = str(  # for compat
+            (Path(self.data_ins_list[idx]).relative_to(self.file_path)).parent)
+        # if self.shuffle_across_cls:
+        caption = self.caption_data['/'.join(ins.split('/')[1:])]
+        # else:
+        # caption = self.caption_data[ins]
+
+        ret_dict.update({
+            'depth': depth,
+            'normal': normal,
+            'alpha_mask': alpha_mask,
+            'depth_mask': depth > 0,
+            # 'depth_mask_sr': depth_mask_sr,
+            'bbox': bbox,
+            'caption': caption,
+            'rays_plucker': rays_plucker,  # cam embedding used in lgm
+            'ins': ins,  # placeholder
+            # 'fps_pcd': fps_pcd,
+        })
+
+        return ret_dict
+
+
+# class MultiViewObjverseDatasetChunk(MultiViewObjverseDataset):
+
+#     def __init__(self,
+#                  file_path,
+#                  reso,
+#                  reso_encoder,
+#                  preprocess=None,
+#                  classes=False,
+#                  load_depth=False,
+#                  test=False,
+#                  scene_scale=1,
+#                  overfitting=False,
+#                  imgnet_normalize=True,
+#                  dataset_size=-1,
+#                  overfitting_bs=-1,
+#                  interval=1,
+#                  plucker_embedding=False,
+#                  shuffle_across_cls=False,
+#                  wds_split=1,
+#                  four_view_for_latent=False,
+#                  single_view_for_i23d=False,
+#                  load_extra_36_view=False,
+#                  gs_cam_format=False,
+#                  **kwargs):
+#         super().__init__(file_path, reso, reso_encoder, preprocess, classes,
+#                          load_depth, test, scene_scale, overfitting,
+#                          imgnet_normalize, dataset_size, overfitting_bs,
+#                          interval, plucker_embedding, shuffle_across_cls,
+#                          wds_split, four_view_for_latent, single_view_for_i23d,
+#                          load_extra_36_view, gs_cam_format, **kwargs)
+#         # load 40 views at a time, for inferring latents.
+
+
+# TODO merge all the useful APIs together
+class ChunkObjaverseDataset(Dataset):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            wds_split_all=1,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            load_raw=False,
+            load_instance_only=False,
+            mv_latent_dir='',
+            perturb_pcd_scale=0.0,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__()
+
+        # st()
+        self.mv_latent_dir = mv_latent_dir
+
+        self.load_raw = load_raw
+        self.load_instance_only = load_instance_only
+        self.read_normal = read_normal
+        self.file_path = file_path
+        self.chunk_size = split_chunk_size
+        self.gs_cam_format = gs_cam_format
+        self.frame_0_as_canonical = frame_0_as_canonical
+        self.four_view_for_latent = four_view_for_latent  # export 0 12 30 36, 4 views for reconstruction
+        self.overfitting = overfitting
+        self.scene_scale = scene_scale
+        self.reso = reso
+        self.reso_encoder = reso_encoder
+        self.classes = False
+        self.load_depth = load_depth
+        self.preprocess = preprocess
+        self.plucker_embedding = plucker_embedding
+        self.intrinsics = get_intri(h=self.reso, w=self.reso,
+                                    normalize=True).reshape(9)
+        self.perturb_pcd_scale = perturb_pcd_scale
+
+        assert not self.classes, "Not support class condition now."
+
+        dataset_name = Path(self.file_path).stem.split('_')[0]
+        self.dataset_name = dataset_name
+        self.ray_sampler = RaySampler()
+
+        self.zfar = 100.0
+        self.znear = 0.01
+
+        # ! load all chunk paths
+        self.chunk_list = []
+
+        # if dataset_size != -1: # predefined instance
+        #     self.chunk_list = self.fetch_chunk_list(os.path.join(self.file_path, 'debug'))
+        # else:
+        #     # for shard_idx in range(1, 5): # shard_dir 1-4 by default
+        #     for shard_idx in os.listdir(self.file_path):
+        #         self.chunk_list += self.fetch_chunk_list(os.path.join(self.file_path, shard_idx))
+
+        def load_single_cls_instances(file_path):
+            ins_list = []  # the first 1 instance for evaluation reference.
+
+            for dict_dir in os.listdir(file_path)[:]: # ! for debugging
+                for ins_dir in os.listdir(os.path.join(file_path, dict_dir)):
+                    ins_list.append(
+                        os.path.join(file_path, dict_dir, ins_dir,
+                                    'campos_512_v4'))
+            return ins_list
+        
+        # st()
+
+        if self.load_raw:
+
+            with open(
+                    # '/nas/shared/V2V/yslan/aigc3d/text_captions_cap3d.json') as f:
+                    # '/nas/shared/public/yslan//data/text_captions_cap3d.json') as f:
+                    './dataset/text_captions_3dtopia.json') as f:
+                self.caption_data = json.load(f)
+
+            # with open
+            #         # '/nas/shared/V2V/yslan/aigc3d/text_captions_cap3d.json') as f:
+            #         '/nas/shared/public/yslan//data/text_captions_cap3d.json') as f:
+            #         # '/cpfs01/shared/public/yhluo/Projects/threed/3D-Enhancer/develop/text_captions_3dtopia.json') as f:
+            #     self.old_caption_data = json.load(f)
+
+            for subset in [  # ! around 17.6 W instances in total. 
+                    'Animals',
+                    # 'daily-used',
+                    # 'BuildingsOutdoor',
+                    # 'Furnitures',
+                    # 'Food',
+                    # 'Plants',
+                    # 'Electronics',
+                    # 'Transportations_tar',
+                    # 'Human-Shape',
+            ]:  # selected subset for training
+                # self.chunk_list += load_single_cls_instances(
+                #     os.path.join(self.file_path, subset))
+                with open(f'shell_scripts/raw_img_list/{subset}.txt', 'r') as f:
+                    self.chunk_list += [os.path.join(subset, item.strip()) for item in f.readlines()]
+
+            # st() # save to local
+            # with open('/cpfs01/user/lanyushi.p/Repo/diffusion-3d/shell_scripts/shards_list/chunk_list.txt', 'w') as f:
+            #     f.writelines(self.chunk_list)
+            # load raw g-objv dataset
+            # self.img_ext = 'png'  # ln3diff
+            # for k, v in dataset_json.items(): # directly load from folders instead
+            #     self.chunk_list.extend(v)
+        else:
+
+            # ! direclty load from json
+            with open(f'{self.file_path}/dataset.json', 'r') as f:
+                dataset_json = json.load(f)
+                # dataset_json = {'Animals': ['Animals/0/10017/1']} 
+
+            if self.chunk_size == 12:
+                self.img_ext = 'png'  # ln3diff
+                for k, v in dataset_json.items():
+                    self.chunk_list.extend(v)
+            else:
+                # extract latent
+                assert self.chunk_size in [16,18, 20]
+                self.img_ext = 'jpg'  # more views
+                for k, v in dataset_json.items():
+                    # if k != 'BuildingsOutdoor':  # cannot be handled by gs
+                    self.chunk_list.extend(v)
+        
+        # filter 
+        # st()
+        # root = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/final/objav/vae/gs/infer-latents/768/8x8/animals-gs-latent/latent_dir'
+        # root = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/final/objav/vae/gs/infer-latents/768/8x8/animals-gs-latent-dim=10-fullset/latent_dir'
+        # filtered_chunk_list = []
+        # for v in self.chunk_list:
+        #     if os.path.exists(os.path.join(root, v[:-2], 'gaussians.npy') ):
+        #         continue
+        #     filtered_chunk_list.append(v)
+        # self.chunk_list = filtered_chunk_list
+
+        dataset_size = len(self.chunk_list)
+        self.chunk_list = sorted(self.chunk_list)
+
+        # self.chunk_list, self.eval_list = self.chunk_list[:int(dataset_size*0.95)], self.chunk_list[int(dataset_size*0.95):]
+        # self.chunk_list = self.eval_list
+
+        # self.wds_split_all = wds_split_all # for 
+        # self.wds_split_all = 1 
+        # self.wds_split_all = 7
+        # self.wds_split_all = 4
+        self.wds_split_all = 1
+
+
+        # ! filter
+        # st()
+
+        if wds_split_all != 1:
+            # ! retrieve the right wds split
+            all_ins_size = len(self.chunk_list)
+            ratio_size = all_ins_size // self.wds_split_all + 1
+            # ratio_size = int(all_ins_size / self.wds_split_all) + 1
+            print('ratio_size: ', ratio_size, 'all_ins_size: ', all_ins_size)
+
+            self.chunk_list = self.chunk_list[ratio_size *
+                                                (wds_split):ratio_size *
+                                                (wds_split + 1)]
+    
+        # st()
+
+        # load images from raw
+        self.rgb_list = []
+
+        if self.load_instance_only:
+            for ins in tqdm(self.chunk_list):
+
+                ins_name = str(Path(ins).parent)
+                # cur_all_fname = [f'{t:05d}' for t in range(40)] # load all instances for now
+
+                self.rgb_list += ([
+                    os.path.join(self.file_path, ins, fname + '.png')
+                    for fname in [f'{t}' for t in range(2)]
+                    # for fname in [f'{t:05d}' for t in range(2)]
+                ]) # synthetic mv data
+
+            # index mapping of mvi data to objv single-view data
+            self.mvi_objv_mapping = {
+                '0': '00000',
+                '1': '00012',
+            }
+
+            # load gt mv data
+
+            self.gt_chunk_list = []
+            self.gt_mv_file_path = '/cpfs01/user/lanyushi.p/data/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+            assert self.chunk_size in [16,18, 20]
+
+            with open(f'{self.gt_mv_file_path}/dataset.json', 'r') as f:
+                dataset_json = json.load(f)
+                # dataset_json = {'Animals': dataset_json['Animals'] } #
+
+            self.img_ext = 'jpg'  # more views
+            for k, v in dataset_json.items():
+                # if k != 'BuildingsOutdoor':  # cannot be handled by gs
+                self.gt_chunk_list.extend(v)
+
+
+        elif self.load_raw:
+            for ins in tqdm(self.chunk_list):
+                # 
+                # st()
+                # ins = ins[len('/nas/shared/V2V/yslan/aigc3d/unzip4/'):]
+                # ins_name = str(Path(ins).relative_to(self.file_path).parent)
+                ins_name = str(Path(ins).parent)
+                # latent_path = os.path.join(self.mv_latent_dir, ins_name, 'latent.npz')
+                # if not os.path.exists(latent_path):
+                #     continue
+
+                cur_all_fname = [f'{t:05d}' for t in range(40)] # load all instances for now
+
+                self.rgb_list += ([
+                    os.path.join(self.file_path, ins, fname, fname + '.png')
+                    for fname in cur_all_fname
+                ])
+    
+        self.post_process = PostProcess(
+            reso,
+            reso_encoder,
+            imgnet_normalize=imgnet_normalize,
+            plucker_embedding=plucker_embedding,
+            decode_encode_img_only=False,
+            mv_input=mv_input,
+            split_chunk_input=split_chunk_size,
+            duplicate_sample=True,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            gs_cam_format=gs_cam_format,
+            orthog_duplicate=False,
+            frame_0_as_canonical=frame_0_as_canonical,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            split_chunk_size=split_chunk_size,
+        )
+        self.kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+
+        # self.no_bottom = True # avoid loading bottom vew
+
+    def fetch_chunk_list(self, file_path):
+        if os.path.isdir(file_path):
+            chunks = [
+                os.path.join(file_path, fname)
+                for fname in os.listdir(file_path) if fname.isdigit()
+            ]
+            return chunks
+        else:
+            return []
+
+    def _pre_process_chunk(self):
+        # e.g., remove bottom view
+        pass
+
+    def read_chunk(self, chunk_path):
+        # equivalent to decode_zip() in wds
+
+        # reshape chunk
+        raw_img = imageio.imread(
+            os.path.join(chunk_path, f'raw_img.{self.img_ext}'))
+        h, bw, c = raw_img.shape
+        raw_img = raw_img.reshape(h, self.chunk_size, -1, c).transpose(
+            (1, 0, 2, 3))
+        c = np.load(os.path.join(chunk_path, 'c.npy'))
+
+        with open(os.path.join(chunk_path, 'caption.txt'),
+                  'r',
+                  encoding="utf-8") as f:
+            caption = f.read()
+
+        with open(os.path.join(chunk_path, 'ins.txt'), 'r',
+                  encoding="utf-8") as f:
+            ins = f.read()
+
+        bbox = np.load(os.path.join(chunk_path, 'bbox.npy'))
+        
+        if self.chunk_size > 16:
+
+            depth_alpha = imageio.imread(
+                os.path.join(chunk_path, 'depth_alpha.jpg'))  # 2h 10w
+            depth_alpha = depth_alpha.reshape(h * 2, self.chunk_size,
+                                            -1).transpose((1, 0, 2))
+
+            depth, alpha = np.split(depth_alpha, 2, axis=1)
+
+            d_near_far = np.load(os.path.join(chunk_path, 'd_near_far.npy'))
+
+            d_near = d_near_far[0].reshape(self.chunk_size, 1, 1)
+            d_far = d_near_far[1].reshape(self.chunk_size, 1, 1)
+            # d = 1 / ( (d_normalized / 255) * (far-near) + near)
+            depth = 1 / ((depth / 255) * (d_far - d_near) + d_near)
+
+            depth[depth > 2.9] = 0.0  # background as 0, follow old tradition
+
+            # ! filter anti-alias artifacts
+
+            erode_mask = kornia.morphology.erosion(
+                torch.from_numpy(alpha == 255).float().unsqueeze(1),
+                self.kernel)  # B 1 H W
+            depth = (torch.from_numpy(depth).unsqueeze(1) * erode_mask).squeeze(
+                1)  # shrink anti-alias bug
+
+        else:
+            # load separate alpha and depth map
+
+            alpha = imageio.imread(
+                os.path.join(chunk_path, f'alpha.{self.img_ext}'))
+            alpha = alpha.reshape(h, self.chunk_size, h).transpose(
+                (1, 0, 2))            
+            depth = np.load(os.path.join(chunk_path, 'depth.npz'))['depth']
+            # depth = depth * (alpha==255) # mask out background
+
+
+
+        # depth = np.stack([depth, alpha], -1)  # rgba
+
+        # if self.no_bottom:
+        #     raw_img
+        #     pass
+
+        if self.read_normal:
+            normal = imageio.imread(os.path.join(
+                chunk_path, 'normal.png')).astype(np.float32) / 255.0
+
+            normal = (normal * 2 - 1).reshape(h, self.chunk_size, -1,
+                                              3).transpose((1, 0, 2, 3))
+            # fix g-buffer normal rendering coordinate
+            # normal = unity2blender(normal) # ! still wrong
+            normal = unity2blender_fix(normal) # ! 
+            depth = (depth, normal)  # ?
+
+        return raw_img, depth, c, alpha, bbox, caption, ins
+
+    def __len__(self):
+        return len(self.chunk_list)
+
+    def __getitem__(self, index) -> Any:
+        sample = self.read_chunk(
+            os.path.join(self.file_path, self.chunk_list[index]))
+        sample = self.post_process.paired_post_process_chunk(sample)
+
+        sample = self.post_process.create_dict_nobatch(sample)
+
+        # aug pcd
+        # st()
+        if self.perturb_pcd_scale > 0:
+            if random.random() > 0.5:
+                t = np.random.rand(sample['fps_pcd'].shape[0], 1, 1) * self.perturb_pcd_scale
+                sample['fps_pcd'] = sample['fps_pcd'] + t * np.random.randn(*sample['fps_pcd'].shape) # type: ignore
+                sample['fps_pcd'] = np.clip(sample['fps_pcd'], -0.45, 0.45) # truncate noisy augmentation
+
+        return sample
+
+
+class ChunkObjaverseDatasetDDPM(ChunkObjaverseDataset):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            load_raw=False,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=split_chunk_size,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            load_raw=load_raw,
+            mv_latent_dir=mv_latent_dir,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+
+        self.n_cond_frames = 6
+        self.perspective_transformer = v2.RandomPerspective(distortion_scale=0.4, p=0.15, fill=1, 
+            interpolation=torchvision.transforms.InterpolationMode.NEAREST)
+        self.mv_resize_cls = torchvision.transforms.Resize(320, interpolation=torchvision.transforms.InterpolationMode.BILINEAR, 
+                max_size=None, antialias=True)
+
+        # ! read img c, caption.
+
+    def get_plucker_ray(self, c):
+        rays_plucker = []
+        for idx in range(c.shape[0]):
+            rays_o, rays_d = self.gen_rays(c[idx])
+            rays_plucker.append(
+                torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d],
+                          dim=-1).permute(2, 0, 1))  # [h, w, 6] -> 6,h,w
+        rays_plucker = torch.stack(rays_plucker, 0)
+        return rays_plucker
+
+    def read_chunk(self, chunk_path):
+        # equivalent to decode_zip() in wds
+
+        # reshape chunk
+        raw_img = imageio.imread(
+            os.path.join(chunk_path, f'raw_img.{self.img_ext}')).astype(np.float32)
+        h, bw, c = raw_img.shape
+        raw_img = raw_img.reshape(h, self.chunk_size, -1, c).transpose(
+            (1, 0, 2, 3))
+
+        c = np.load(os.path.join(chunk_path, 'c.npy')).astype(np.float32)
+
+        with open(os.path.join(chunk_path, 'caption.txt'),
+                  'r',
+                  encoding="utf-8") as f:
+            caption = f.read()
+
+        with open(os.path.join(chunk_path, 'ins.txt'), 'r',
+                  encoding="utf-8") as f:
+            ins = f.read()
+
+        return raw_img, c, caption, ins
+    
+    def _load_latent(self, ins):
+        # if 'adv' in self.mv_latent_dir: # new latent codes saved have 3 augmentations
+        #     idx = random.choice([0,1,2])
+        #     latent = np.load(os.path.join(self.mv_latent_dir, ins, f'latent-{idx}.npy'))  # pre-calculated VAE latent
+        # else:
+        latent = np.load(os.path.join(self.mv_latent_dir, ins, 'latent.npy'))  # pre-calculated VAE latent
+        latent = repeat(latent, 'C H W -> B C H W', B=2)
+        # return {'latent': latent}
+        return latent
+
+    def normalize_camera(self, c, c_frame0):
+        # assert c.shape[0] == self.chunk_size  # 8 o r10
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+        canonical_camera_poses = c_frame0[:, :16].reshape(1, 4, 4)
+        inverse_canonical_pose = np.linalg.inv(canonical_camera_poses)
+        inverse_canonical_pose = np.repeat(inverse_canonical_pose, B, 0)
+
+        cam_radius = np.linalg.norm(
+            c_frame0[:, :16].reshape(1, 4, 4)[:, :3, 3],
+            axis=-1,
+            keepdims=False)  # since g-buffer adopts dynamic radius here.
+
+        frame1_fixed_pos = np.repeat(np.eye(4)[None], 1, axis=0)
+        frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+        transform = frame1_fixed_pos @ inverse_canonical_pose
+
+        new_camera_poses = np.repeat(
+            transform, 1, axis=0
+        ) @ camera_poses  # [V, 4, 4]. np.repeat() is th.repeat_interleave()
+
+        c = np.concatenate([new_camera_poses.reshape(B, 16), c[:, 16:]],
+                           axis=-1)
+
+        return c
+
+    # @autocast
+    # def plucker_embedding(self, c):
+    #     rays_o, rays_d = self.gen_rays(c)
+    #     rays_plucker = torch.cat(
+    #         [torch.cross(rays_o, rays_d, dim=-1), rays_d],
+    #         dim=-1).permute(2, 0, 1)  # [h, w, 6] -> 6,h,w
+
+    #     return rays_plucker
+
+    def __getitem__(self, index) -> Any:
+        raw_img, c, caption, ins = self.read_chunk(
+            os.path.join(self.file_path, self.chunk_list[index]))
+        # sample = self.post_process.paired_post_process_chunk(sample)
+
+        # ! random zoom in (scale augmentation)
+        # for i in range(img.shape[0]):
+        #     for v in range(img.shape[1]):
+        #         if random.random() > 0.8:
+        #             rand_bg_scale = random.randint(60,99) / 100
+        #             st()
+        #             img[i,v] = recenter(img[i,v], np.ones_like(img[i,v]), border_ratio=rand_bg_scale)
+
+        # ! process
+        raw_img = torch.from_numpy(raw_img).permute(0, 3, 1, 2) / 255.0  # [0,1]
+
+        if raw_img.shape[-1] != self.reso:
+            raw_img = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso, self.reso),
+                mode='bilinear',
+                align_corners=False,
+            )
+        img = raw_img * 2 - 1  # as gt
+
+        # ! load latent
+        latent, _ = self._load_latent(ins)
+
+        # ! shuffle
+        indices = np.random.permutation(self.chunk_size)
+        img = img[indices]
+        c = c[indices]
+
+        img = self.perspective_transformer(img) # create 3D inconsistency
+
+        # ! split along V and repeat other stuffs accordingly
+        img = rearrange(img, '(B V) ... -> B V ...', B=2)[:, :self.n_cond_frames]
+        c = rearrange(c, '(B V) ... -> B V ...', B=2)[:, :self.n_cond_frames] # 2 6 25
+
+
+        # rand perspective aug
+        caption = [caption, caption]
+        ins = [ins, ins]
+
+        # load plucker coord
+        # st()
+        # plucker_c = self.get_plucker_ray(rearrange(c[:, 1:1+self.n_cond_frames], "b t ... -> (b t) ..."))
+        # plucker_c = rearrange(c, '(B V) ... -> B V ...', B=2) # 2 6 25
+
+        # use view-space camera tradition
+        c[0] = self.normalize_camera(c[0], c[0,0:1])
+        c[1] = self.normalize_camera(c[1], c[1,0:1])
+
+        # https://github.com/TencentARC/InstantMesh/blob/7fe95627cf819748f7830b2b278f302a9d798d17/src/model.py#L70
+        # c = np.concatenate([c[..., :12], c[..., 16:17], c[..., 20:21], c[..., 18:19], c[..., 21:22]], axis=-1)
+        # c = c + np.random.randn(*c.shape) * 0.04 - 0.02
+
+
+        # ! to dict
+        # sample = self.post_process.create_dict_nobatch(sample)
+        ret_dict = {
+            'caption': caption,
+            'ins': ins,
+            'c': c,
+            'img': img, # fix inp img range to [-1,1]
+            'latent': latent,
+            # **latent
+        }
+
+        # st()
+
+        return ret_dict
+
+
+class ChunkObjaverseDatasetDDPMgs(ChunkObjaverseDatasetDDPM):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            load_raw=False,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            classes=classes,
+            load_depth=load_depth,
+            test=test,
+            scene_scale=scene_scale,
+            overfitting=overfitting,
+            imgnet_normalize=imgnet_normalize,
+            dataset_size=dataset_size,
+            overfitting_bs=overfitting_bs,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            shuffle_across_cls=shuffle_across_cls,
+            wds_split=wds_split,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=four_view_for_latent,
+            single_view_for_i23d=single_view_for_i23d,
+            load_extra_36_view=load_extra_36_view,
+            gs_cam_format=gs_cam_format,
+            frame_0_as_canonical=frame_0_as_canonical,
+            split_chunk_size=split_chunk_size,
+            mv_input=mv_input,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            read_normal=read_normal,
+            mv_latent_dir=mv_latent_dir,
+            load_raw=load_raw,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+        
+        self.avoid_loading_first = False
+
+    #     self.feat_scale_factor = torch.Tensor([0.99227685, 1.014337  , 0.20842505, 0.98727155, 0.3305389 ,
+    #    0.38729668, 1.0155401 , 0.9728264 , 1.0009694 , 0.97328585,
+    #    0.2881106 , 0.1652732 , 0.3482468 , 0.9971449 , 0.99895126,
+    #    0.18491288]).float().reshape(1,1,-1)
+
+        # stat for normalization
+        # self.xyz_mean = torch.Tensor([-0.00053714, 0.08095618, -0.01914407] ).reshape(1, 3).float()
+        # self.xyz_std = np.array([0.14593576, 0.15753542, 0.18873914] ).reshape(1,3).astype(np.float32)
+
+        # self.xyz_std = np.array([0.14593576, 0.15753542, 0.18873914] ).reshape(1,3).astype(np.float32)
+        self.xyz_std = 0.164 # a global scaler
+
+        self.kl_mean = np.array([ 0.0184,  0.0024,  0.0926,  0.0517,  0.1781,  0.7137, -0.0355,  0.0267,
+         0.0183,  0.0164, -0.5090,  0.2406,  0.2733, -0.0256, -0.0285,  0.0761]).reshape(1,16).astype(np.float32)
+
+        self.kl_std = np.array([1.0018, 1.0309, 1.3001, 1.0160, 0.8182, 0.8023, 1.0591, 0.9789, 0.9966,
+        0.9448, 0.8908, 1.4595, 0.7957, 0.9871, 1.0236, 1.2923]).reshape(1,16).astype(np.float32)
+
+
+    def normalize_pcd_act(self, x):
+        return x / self.xyz_std
+    
+    def normalize_kl_feat(self, latent):
+        # return latent / self.feat_scale_factor
+        return (latent-self.kl_mean) / self.kl_std
+
+    def _load_latent(self, ins, rand_pick_one=False, pick_both=False):
+
+        if 'adv' in self.mv_latent_dir: # new latent codes saved have 3 augmentations
+            idx = random.choice([0,1,2])
+            # idx = random.choice([0])
+            latent = np.load(os.path.join(self.mv_latent_dir, ins, f'latent-{idx}.npz'))  # pre-calculated VAE latent
+        else:
+            latent = np.load(os.path.join(self.mv_latent_dir, ins, 'latent.npz'))  # pre-calculated VAE latent
+
+        latent, fps_xyz = latent['latent_normalized'], latent['query_pcd_xyz'] # 2,768,16; 2,768,3
+
+        if not pick_both:
+            if rand_pick_one:
+                rand_idx = random.randint(0,1)
+            else:
+                rand_idx = 0
+
+            latent, fps_xyz = latent[rand_idx:rand_idx+1], fps_xyz[rand_idx:rand_idx+1]
+        
+        # per-channel normalize to std=1 & concat
+        # latent_pcd = np.concatenate([self.normalize_kl_feat(latent), self.normalize_pcd_act(fps_xyz)], -1)
+        # latent_pcd = np.concatenate([latent, self.normalize_pcd_act(fps_xyz)], -1)
+
+        # return latent_pcd, fps_xyz
+        return latent, fps_xyz
+
+
+    def __getitem__(self, index) -> Any:
+        raw_img, c, caption, ins = self.read_chunk(
+            os.path.join(self.file_path, self.chunk_list[index]))
+        # sample = self.post_process.paired_post_process_chunk(sample)
+
+        # ! random zoom in (scale augmentation)
+        # for i in range(img.shape[0]):
+        #     for v in range(img.shape[1]):
+        #         if random.random() > 0.8:
+        #             rand_bg_scale = random.randint(60,99) / 100
+        #             st()
+        #             img[i,v] = recenter(img[i,v], np.ones_like(img[i,v]), border_ratio=rand_bg_scale)
+
+        # ! process
+        raw_img = torch.from_numpy(raw_img).permute(0, 3, 1, 2) / 255.0  # [0,1]
+
+        if raw_img.shape[-1] != self.reso:
+            raw_img = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso, self.reso),
+                mode='bilinear',
+                align_corners=False,
+            )
+        img = raw_img * 2 - 1  # as gt
+
+        # ! load latent
+        # latent, _ = self._load_latent(ins)
+
+        latent, fps_xyz = self._load_latent(ins, pick_both=True) # analyzing xyz/latent disentangled diffusion
+        # latent, fps_xyz = latent[0], fps_xyz[0] # remove batch dim here
+
+        # fps_xyz = fps_xyz / self.scaling_factor # for xyz training
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz)
+
+        if self.avoid_loading_first: # for training mv model
+            index = list(range(1,6)) + list(range(7,12))
+            img = img[index]
+            c = c[index]
+        
+        # ! shuffle
+        indices = np.random.permutation(img.shape[0])
+        img = img[indices]
+        c = c[indices]
+
+        img = self.perspective_transformer(img) # create 3D inconsistency
+
+
+        # ! split along V and repeat other stuffs accordingly
+        img = rearrange(img, '(B V) ... -> B V ...', B=2)[:, :self.n_cond_frames]
+        c = rearrange(c, '(B V) ... -> B V ...', B=2)[:, :self.n_cond_frames] # 2 6 25
+
+        # rand perspective aug
+        caption = [caption, caption]
+        ins = [ins, ins]
+
+        # load plucker coord
+        # st()
+        # plucker_c = self.get_plucker_ray(rearrange(c[:, 1:1+self.n_cond_frames], "b t ... -> (b t) ..."))
+        # plucker_c = rearrange(c, '(B V) ... -> B V ...', B=2) # 2 6 25
+
+        # use view-space camera tradition
+        c[0] = self.normalize_camera(c[0], c[0,0:1])
+        c[1] = self.normalize_camera(c[1], c[1,0:1])
+
+        # ! to dict
+        # sample = self.post_process.create_dict_nobatch(sample)
+        ret_dict = {
+            'caption': caption,
+            'ins': ins,
+            'c': c,
+            'img': img, # fix inp img range to [-1,1]
+            'latent': latent,
+            'normalized-fps-xyz': normalized_fps_xyz
+            # **latent
+        }
+
+        # st()
+
+        return ret_dict
+
+
+
+
+class ChunkObjaverseDatasetDDPMgsT23D(ChunkObjaverseDatasetDDPMgs):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            classes=classes,
+            load_depth=load_depth,
+            test=test,
+            scene_scale=scene_scale,
+            overfitting=overfitting,
+            imgnet_normalize=imgnet_normalize,
+            dataset_size=dataset_size,
+            overfitting_bs=overfitting_bs,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            shuffle_across_cls=shuffle_across_cls,
+            wds_split=wds_split,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=four_view_for_latent,
+            single_view_for_i23d=single_view_for_i23d,
+            load_extra_36_view=load_extra_36_view,
+            gs_cam_format=gs_cam_format,
+            frame_0_as_canonical=frame_0_as_canonical,
+            split_chunk_size=split_chunk_size,
+            mv_input=mv_input,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            read_normal=read_normal,
+            mv_latent_dir=mv_latent_dir,
+            load_raw=True,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+
+    # def __len__(self):
+    #     return 40
+
+    def __len__(self):
+        return len(self.rgb_list) 
+
+    def __getitem__(self, index) -> Any:
+
+        
+        rgb_path = self.rgb_list[index]
+        ins = str(Path(rgb_path).relative_to(self.file_path).parent.parent.parent)
+
+        # load caption
+        caption = self.caption_data['/'.join(ins.split('/')[1:])]
+
+        # chunk_path = os.path.join(self.file_path, self.chunk_list[index])
+
+        # # load caption
+        # with open(os.path.join(chunk_path, 'caption.txt'),
+        #           'r',
+        #           encoding="utf-8") as f:
+        #     caption = f.read()
+
+        # # load latent
+        # with open(os.path.join(chunk_path, 'ins.txt'), 'r',
+        #           encoding="utf-8") as f:
+        #     ins = f.read()
+
+        latent, fps_xyz = self._load_latent(ins, True) # analyzing xyz/latent disentangled diffusion
+        latent, fps_xyz = latent[0], fps_xyz[0] # remove batch dim here
+
+        # fps_xyz = fps_xyz / self.scaling_factor # for xyz training
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz)
+
+
+        # ! to dict
+        ret_dict = {
+            # 'caption': caption,
+            'latent': latent,
+            # 'img': img,
+            'fps-xyz': fps_xyz,
+            'normalized-fps-xyz': normalized_fps_xyz,
+            'caption': caption
+        }
+
+        return ret_dict
+
+
+class ChunkObjaverseDatasetDDPMgsI23D(ChunkObjaverseDatasetDDPMgs):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            classes=classes,
+            load_depth=load_depth,
+            test=test,
+            scene_scale=scene_scale,
+            overfitting=overfitting,
+            imgnet_normalize=imgnet_normalize,
+            dataset_size=dataset_size,
+            overfitting_bs=overfitting_bs,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            shuffle_across_cls=shuffle_across_cls,
+            wds_split=wds_split,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=four_view_for_latent,
+            single_view_for_i23d=single_view_for_i23d,
+            load_extra_36_view=load_extra_36_view,
+            gs_cam_format=gs_cam_format,
+            frame_0_as_canonical=frame_0_as_canonical,
+            split_chunk_size=split_chunk_size,
+            mv_input=mv_input,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            read_normal=read_normal,
+            mv_latent_dir=mv_latent_dir,
+            load_raw=True,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+
+        assert self.load_raw
+        self.scaling_factor = np.array([0.14593576, 0.15753542, 0.18873914])
+
+    def __len__(self):
+        return len(self.rgb_list)
+
+    # def __len__(self):
+    #     return 40
+
+    def __getitem__(self, index) -> Any:
+
+        rgb_path = self.rgb_list[index]
+        ins = str(Path(rgb_path).relative_to(self.file_path).parent.parent.parent)
+
+        raw_img = imageio.imread(rgb_path).astype(np.float32)
+        alpha_mask = raw_img[..., -1:] / 255
+        raw_img = alpha_mask * raw_img[..., :3] + (
+            1 - alpha_mask) * np.ones_like(raw_img[..., :3]) * 255
+
+        raw_img = cv2.resize(raw_img, (self.reso, self.reso), interpolation=cv2.INTER_CUBIC)
+        raw_img = torch.from_numpy(raw_img).permute(2,0,1).clip(0,255)  # [0,1]
+        img = raw_img / 127.5 - 1
+
+        # with open(os.path.join(chunk_path, 'caption.txt'),
+        #           'r',
+        #           encoding="utf-8") as f:
+        #     caption = f.read()
+
+        # latent = self._load_latent(ins, True)[0]
+        latent, fps_xyz = self._load_latent(ins, True) # analyzing xyz/latent disentangled diffusion
+        latent, fps_xyz = latent[0], fps_xyz[0]
+
+        # fps_xyz = fps_xyz / self.scaling_factor # for xyz training
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz)
+
+        # load caption
+        caption = self.caption_data['/'.join(ins.split('/')[1:])]
+
+        # ! to dict
+        ret_dict = {
+            # 'caption': caption,
+            'latent': latent,
+            'img': img.numpy(), # no idea whether loading Tensor leads to 'too many files opened'
+            'fps-xyz': fps_xyz,
+            'normalized-fps-xyz': normalized_fps_xyz,
+            'caption': caption
+        }
+
+        return ret_dict
+
+class ChunkObjaverseDatasetDDPMgsMV23D(ChunkObjaverseDatasetDDPMgs):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            classes=classes,
+            load_depth=load_depth,
+            test=test,
+            scene_scale=scene_scale,
+            overfitting=overfitting,
+            imgnet_normalize=imgnet_normalize,
+            dataset_size=dataset_size,
+            overfitting_bs=overfitting_bs,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            shuffle_across_cls=shuffle_across_cls,
+            wds_split=wds_split,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=four_view_for_latent,
+            single_view_for_i23d=single_view_for_i23d,
+            load_extra_36_view=load_extra_36_view,
+            gs_cam_format=gs_cam_format,
+            frame_0_as_canonical=frame_0_as_canonical,
+            split_chunk_size=split_chunk_size,
+            mv_input=mv_input,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            read_normal=read_normal,
+            mv_latent_dir=mv_latent_dir,
+            load_raw=False,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+
+        assert not self.load_raw
+        # self.scaling_factor = np.array([0.14593576, 0.15753542, 0.18873914])
+
+        self.n_cond_frames = 4 # a easy version for now.
+        self.avoid_loading_first = True
+
+    def __getitem__(self, index) -> Any:
+        raw_img, c, caption, ins = self.read_chunk(
+            os.path.join(self.file_path, self.chunk_list[index]))
+
+        # ! process
+        raw_img = torch.from_numpy(raw_img).permute(0, 3, 1, 2) / 255.0  # [0,1]
+
+        if raw_img.shape[-1] != self.reso:
+            raw_img = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso, self.reso),
+                mode='bilinear',
+                align_corners=False,
+            )
+        img = raw_img * 2 - 1  # as gt
+
+        # ! load latent
+        # latent, _ = self._load_latent(ins)
+
+        latent, fps_xyz = self._load_latent(ins, pick_both=True) # analyzing xyz/latent disentangled diffusion
+        # latent, fps_xyz = latent[0], fps_xyz[0] # remove batch dim here
+
+        # fps_xyz = fps_xyz / self.scaling_factor # for xyz training
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz)
+
+        if self.avoid_loading_first: # for training mv model
+            index = list(range(1,self.chunk_size//2)) + list(range(self.chunk_size//2+1, self.chunk_size))
+            img = img[index]
+            c = c[index]
+        
+        # ! shuffle
+        indices = np.random.permutation(img.shape[0])
+        img = img[indices]
+        c = c[indices]
+
+        aug_img = self.perspective_transformer(img) # create 3D inconsistency
+
+        # ! split along V and repeat other stuffs accordingly
+        img = rearrange(img, '(B V) ... -> B V ...', B=2)[:, 0:1] # only return first view (randomly sampled)
+
+        aug_img = rearrange(aug_img, '(B V) ... -> B V ...', B=2)[:, 1:self.n_cond_frames+1]
+        c = rearrange(c, '(B V) ... -> B V ...', B=2)[:, 1:self.n_cond_frames+1] # 2 6 25
+
+
+        # use view-space camera tradition
+        c[0] = self.normalize_camera(c[0], c[0,0:1])
+        c[1] = self.normalize_camera(c[1], c[1,0:1])
+
+        caption = [caption, caption]
+        ins = [ins, ins]
+
+        # ! to dict
+        # sample = self.post_process.create_dict_nobatch(sample)
+        ret_dict = {
+            'caption': caption,
+            'ins': ins,
+            'c': c,
+            'img': img, # fix inp img range to [-1,1]
+            'mv_img': aug_img,
+            'latent': latent,
+            'normalized-fps-xyz': normalized_fps_xyz
+            # **latent
+        }
+
+        # st()
+
+        return ret_dict
+
+
+class ChunkObjaverseDatasetDDPMgsMV23DSynthetic(ChunkObjaverseDatasetDDPMgs):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            classes=classes,
+            load_depth=load_depth,
+            test=test,
+            scene_scale=scene_scale,
+            overfitting=overfitting,
+            imgnet_normalize=imgnet_normalize,
+            dataset_size=dataset_size,
+            overfitting_bs=overfitting_bs,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            shuffle_across_cls=shuffle_across_cls,
+            wds_split=wds_split,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=four_view_for_latent,
+            single_view_for_i23d=single_view_for_i23d,
+            load_extra_36_view=load_extra_36_view,
+            gs_cam_format=gs_cam_format,
+            frame_0_as_canonical=frame_0_as_canonical,
+            split_chunk_size=split_chunk_size,
+            mv_input=mv_input,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            read_normal=read_normal,
+            mv_latent_dir=mv_latent_dir,
+            load_raw=True,
+            load_instance_only=True,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+
+        # assert not self.load_raw
+        # self.scaling_factor = np.array([0.14593576, 0.15753542, 0.18873914])
+
+        self.n_cond_frames = 6 # a easy version for now.
+        self.avoid_loading_first = True
+        self.indices = np.array([0,1,2,3,4,5])
+        self.img_root_dir = '/cpfs01/user/lanyushi.p/data/unzip4_img'
+
+
+        azimuths = np.array([30, 90, 150, 210, 270, 330]).astype(float)
+        elevations = np.array([20, -10, 20, -10, 20, -10]).astype(float)
+
+        zero123pp_pose, _ = generate_input_camera(1.8, [[elevations[i], azimuths[i]] for i in range(6)], fov=30)
+        K = torch.Tensor([1.3889, 0.0000, 0.5000, 0.0000, 1.3889, 0.5000, 0.0000, 0.0000, 0.0039]).to(zero123pp_pose) # keeps the same
+        zero123pp_pose = torch.cat([zero123pp_pose.reshape(6,-1), K.unsqueeze(0).repeat(6,1)], dim=-1)
+
+
+        eval_camera = zero123pp_pose[self.indices].float().cpu().numpy() # for normalization
+        self.eval_camera = self.normalize_camera(eval_camera, eval_camera[0:1]) # the first img is not used.
+
+        # self.load_synthetic_only = False
+        self.load_synthetic_only = True
+
+    def __len__(self):
+        return len(self.rgb_list)
+    
+    def _getitem_synthetic(self, index) -> Any:
+
+        rgb_fname = Path(self.rgb_list[index])
+        # ins = self.mvi_objv_mapping(rgb_fname.parent.parent.stem)
+
+        # ins = str(Path(rgb_fname).parent.parent.stem)
+
+        ins = str((Path(rgb_fname).relative_to(self.file_path)).parent.parent)
+
+        mv_img = imageio.imread(rgb_fname)
+        # st()
+        mv_img = rearrange(mv_img, '(n h) (m w) c -> (n m) h w c', n=3, m=2)[self.indices]        # (6, 3, 320, 320)
+        mv_img = np.stack([recenter(img, np.ones_like(img), border_ratio=0.1) for img in mv_img], axis=0)
+        mv_img = rearrange(mv_img, 'b h w c -> b c h w') # to torch tradition
+        mv_img = torch.from_numpy(mv_img) / 127.5 - 1
+
+        # ! load single-view image here
+        img_idx = self.mvi_objv_mapping[rgb_fname.stem]
+        img_path = os.path.join(self.img_root_dir, rgb_fname.parent.relative_to(self.file_path), img_idx, f'{img_idx}.png')
+
+        raw_img = imageio.imread(img_path).astype(np.float32)
+        alpha_mask = raw_img[..., -1:] / 255
+        raw_img = alpha_mask * raw_img[..., :3] + (
+            1 - alpha_mask) * np.ones_like(raw_img[..., :3]) * 255
+
+        raw_img = cv2.resize(raw_img, (self.reso, self.reso), interpolation=cv2.INTER_CUBIC)
+        raw_img = torch.from_numpy(raw_img).permute(2,0,1).clip(0,255)  # [0,1]
+        img = raw_img / 127.5 - 1
+
+        latent, fps_xyz = self._load_latent(ins, pick_both=False) # analyzing xyz/latent disentangled diffusion
+        latent, fps_xyz = latent[0], fps_xyz[0]
+
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz) # for stage-1
+
+        # use view-space camera tradition
+        # ins = [ins, ins]
+        # st()
+        caption = self.caption_data['/'.join(ins.split('/')[1:])]
+
+        # ! to dict
+        # sample = self.post_process.create_dict_nobatch(sample)
+        ret_dict = {
+            'caption': caption,
+            # 'ins': ins,
+            'c': self.eval_camera,
+            'img': img, # fix inp img range to [-1,1]
+            'mv_img': mv_img,
+            'latent': latent,
+            'normalized-fps-xyz': normalized_fps_xyz,
+            'fps-xyz': fps_xyz,
+        }
+
+        return ret_dict
+
+
+    def _getitem_gt(self, index) -> Any:
+        raw_img, c, caption, ins = self.read_chunk(
+            os.path.join(self.gt_mv_file_path, self.gt_chunk_list[index]))
+
+        # ! process
+        raw_img = torch.from_numpy(raw_img).permute(0, 3, 1, 2) / 255.0  # [0,1]
+
+        if raw_img.shape[-1] != self.reso:
+            raw_img = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso, self.reso),
+                mode='bilinear',
+                align_corners=False,
+            )
+        img = raw_img * 2 - 1  # as gt
+
+        # ! load latent
+        # latent, _ = self._load_latent(ins)
+
+        latent, fps_xyz = self._load_latent(ins, pick_both=True) # analyzing xyz/latent disentangled diffusion
+        # latent, fps_xyz = latent[0], fps_xyz[0] # remove batch dim here
+
+        # fps_xyz = fps_xyz / self.scaling_factor # for xyz training
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz)
+
+        if self.avoid_loading_first: # for training mv model
+            index = list(range(1,self.chunk_size//2)) + list(range(self.chunk_size//2+1, self.chunk_size))
+            img = img[index]
+            c = c[index]
+        
+        # ! shuffle
+        indices = np.random.permutation(img.shape[0])
+        img = img[indices]
+        c = c[indices]
+
+        # st()
+        aug_img = self.mv_resize_cls(img)
+        aug_img = self.perspective_transformer(aug_img) # create 3D inconsistency
+
+        # ! split along V and repeat other stuffs accordingly
+        img = rearrange(img, '(B V) ... -> B V ...', B=2)[:, 0:1] # only return first view (randomly sampled)
+
+        aug_img = rearrange(aug_img, '(B V) ... -> B V ...', B=2)[:, 1:self.n_cond_frames+1]
+        c = rearrange(c, '(B V) ... -> B V ...', B=2)[:, 1:self.n_cond_frames+1] # 2 6 25
+
+
+        # use view-space camera tradition
+        c[0] = self.normalize_camera(c[0], c[0,0:1])
+        c[1] = self.normalize_camera(c[1], c[1,0:1])
+
+        caption = [caption, caption]
+        ins = [ins, ins]
+
+        # ! to dict
+        # sample = self.post_process.create_dict_nobatch(sample)
+        ret_dict = {
+            'caption': caption,
+            'ins': ins,
+            'c': c,
+            'img': img, # fix inp img range to [-1,1]
+            'mv_img': aug_img,
+            'latent': latent,
+            'normalized-fps-xyz': normalized_fps_xyz,
+            'fps-xyz': fps_xyz,
+        }
+
+        return ret_dict
+
+
+    def __getitem__(self, index) -> Any:
+        # load synthetic version
+
+        try:
+            synthetic_mv = self._getitem_synthetic(index)
+        except Exception as e:
+            # logger.log(Path(self.rgb_list[index]), 'missing')
+            synthetic_mv = self._getitem_synthetic(random.randint(0, len(self.rgb_list)//2))
+
+        if self.load_synthetic_only:
+            return synthetic_mv
+
+        else:
+            # load gt mv chunk
+            gt_chunk_index = random.randint(0, len(self.gt_chunk_list)-1)
+            gt_mv = self._getitem_gt(gt_chunk_index)
+
+            # merge them together along batch dim
+            merged_mv = {}
+            for k, v in synthetic_mv.items(): # merge, synthetic - gt order
+                if k not in ['caption', 'ins']:
+                    if k == 'img':
+                        merged_mv[k] = np.concatenate([v[None], gt_mv[k][:, 0]], axis=0).astype(np.float32)
+                    else:
+                        merged_mv[k] = np.concatenate([v[None], gt_mv[k]], axis=0).astype(np.float32)
+                else:
+                    merged_mv[k] = [v] + gt_mv[k] # list
+
+            return merged_mv
+
+
+
+
+
+class ChunkObjaverseDatasetDDPMgsI23D_loadMV(ChunkObjaverseDatasetDDPMgs):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=False,
+            single_view_for_i23d=False,
+            load_extra_36_view=False,
+            gs_cam_format=False,
+            frame_0_as_canonical=True,
+            split_chunk_size=10,
+            mv_input=True,
+            append_depth=False,
+            append_xyz=False,
+            pcd_path=None,
+            load_pcd=False,
+            read_normal=False,
+            mv_latent_dir='',
+            canonicalize_pcd=False,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs):
+
+        super().__init__(
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=preprocess,
+            classes=classes,
+            load_depth=load_depth,
+            test=test,
+            scene_scale=scene_scale,
+            overfitting=overfitting,
+            imgnet_normalize=imgnet_normalize,
+            dataset_size=dataset_size,
+            overfitting_bs=overfitting_bs,
+            interval=interval,
+            plucker_embedding=plucker_embedding,
+            shuffle_across_cls=shuffle_across_cls,
+            wds_split=wds_split,  # 4 splits to accelerate preprocessing
+            four_view_for_latent=four_view_for_latent,
+            single_view_for_i23d=single_view_for_i23d,
+            load_extra_36_view=load_extra_36_view,
+            gs_cam_format=gs_cam_format,
+            frame_0_as_canonical=frame_0_as_canonical,
+            split_chunk_size=split_chunk_size,
+            mv_input=mv_input,
+            append_depth=append_depth,
+            append_xyz=append_xyz,
+            pcd_path=pcd_path,
+            load_pcd=load_pcd,
+            read_normal=read_normal,
+            mv_latent_dir=mv_latent_dir,
+            load_raw=False,
+            # shards_folder_num=4,
+            # eval=False,
+            **kwargs)
+
+        assert not self.load_raw
+        # self.scaling_factor = np.array([0.14593576, 0.15753542, 0.18873914])
+
+        self.n_cond_frames = 5 # a easy version for now.
+        self.avoid_loading_first = True
+
+        # self.canonicalize_pcd = canonicalize_pcd
+        # self.canonicalize_pcd = True
+        self.canonicalize_pcd = False
+
+    def canonicalize_xyz(self, c, pcd):
+
+        B = c.shape[0]
+        camera_poses_rot = c[:, :16].reshape(B, 4, 4)[:, :3, :3]
+
+        R_inv = np.transpose(camera_poses_rot, (0,2,1)) # w2c rotation
+
+        new_pcd = (R_inv @ np.transpose(pcd, (0,2,1))) # B 3 3 @ B 3 N
+        new_pcd = np.transpose(new_pcd, (0,2,1))
+
+        return new_pcd
+
+
+    def __getitem__(self, index) -> Any:
+        raw_img, c, caption, ins = self.read_chunk(
+            os.path.join(self.file_path, self.chunk_list[index]))
+
+        # ! process
+        raw_img = torch.from_numpy(raw_img).permute(0, 3, 1, 2) / 255.0  # [0,1]
+
+        if raw_img.shape[-1] != self.reso:
+            raw_img = torch.nn.functional.interpolate(
+                input=raw_img,
+                size=(self.reso, self.reso),
+                mode='bilinear',
+                align_corners=False,
+            )
+        img = raw_img * 2 - 1  # as gt
+
+        # ! load latent
+        # latent, _ = self._load_latent(ins)
+
+        if self.avoid_loading_first: # for training mv model
+            index = list(range(1,self.chunk_size//2)) + list(range(self.chunk_size//2+1, self.chunk_size))
+            img = img[index]
+            c = c[index]
+        
+        # ! shuffle
+        indices = np.random.permutation(img.shape[0])[:self.n_cond_frames*2]
+        img = img[indices]
+        c = c[indices]
+
+        latent, fps_xyz = self._load_latent(ins, pick_both=True) # analyzing xyz/latent disentangled diffusion
+        # latent, fps_xyz = latent[0], fps_xyz[0] # remove batch dim here
+
+        fps_xyz = np.repeat(fps_xyz, self.n_cond_frames, 0)
+        latent = np.repeat(latent, self.n_cond_frames, 0)
+        normalized_fps_xyz = self.normalize_pcd_act(fps_xyz)
+
+        if self.canonicalize_pcd:
+            normalized_fps_xyz = self.canonicalize_xyz(c, normalized_fps_xyz)
+
+        # repeat
+        caption = [caption] * self.n_cond_frames * 2
+        ins = [ins] * self.n_cond_frames * 2
+
+        ret_dict = {
+            'caption': caption,
+            'ins': ins,
+            'c': c,
+            'img': img, # fix inp img range to [-1,1]
+            'latent': latent,
+            'normalized-fps-xyz': normalized_fps_xyz,
+            'fps-xyz': fps_xyz,
+            # **latent
+        }
+
+        return ret_dict
+
+
+class RealDataset(Dataset):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+    ) -> None:
+        super().__init__()
+
+        self.file_path = file_path
+        self.overfitting = overfitting
+        self.scene_scale = scene_scale
+        self.reso = reso
+        self.reso_encoder = reso_encoder
+        self.classes = False
+        self.load_depth = load_depth
+        self.preprocess = preprocess
+        self.plucker_embedding = plucker_embedding
+
+        self.rgb_list = []
+
+        all_fname = [
+            t for t in os.listdir(self.file_path)
+            if t.split('.')[1] in ['png', 'jpg']
+        ]
+
+        all_fname = [name for name in all_fname if '-input' in name ]
+
+        self.rgb_list += ([
+            os.path.join(self.file_path, fname) for fname in all_fname
+        ])
+
+        # st()
+
+        # if len(self.rgb_list) == 1:
+        #     # placeholder
+        #     self.rgb_list = self.rgb_list * 40
+
+        # ! setup normalizataion
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+
+        assert imgnet_normalize
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+        # camera = torch.load('eval_pose.pt', map_location='cpu')
+        # self.eval_camera = camera
+
+        # pre-cache
+        # self.calc_rays_plucker()
+
+    def __len__(self):
+        return len(self.rgb_list)
+
+    def __getitem__(self, index) -> Any:
+        # return super().__getitem__(index)
+
+        rgb_fname = self.rgb_list[index]
+        # ! preprocess, normalize
+
+        raw_img = imageio.imread(rgb_fname)
+
+        # interpolation=cv2.INTER_AREA)
+        if raw_img.shape[-1] == 4:
+            alpha_mask = raw_img[..., 3:4] / 255.0
+            bg_white = np.ones_like(alpha_mask) * 255.0
+            raw_img = raw_img[..., :3] * alpha_mask + (
+                1 - alpha_mask) * bg_white  #[3, reso_encoder, reso_encoder]
+            raw_img = raw_img.astype(np.uint8)
+
+        # raw_img = recenter(raw_img, np.ones_like(raw_img), border_ratio=0.2)
+
+        # log gt
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+
+        img = torch.from_numpy(img)[..., :3].permute(
+            2, 0, 1
+        ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        ret_dict = {
+            # 'rgb_fname': rgb_fname,
+            # 'img_to_encoder':
+            # img_to_encoder.unsqueeze(0).repeat_interleave(40, 0),
+            'img': img,
+            # 'c': self.eval_camera,  # TODO, get pre-calculated samples
+            # 'ins': 'placeholder',
+            # 'bbox': 'placeholder',
+            # 'caption': 'placeholder',
+        }
+
+        # ! repeat as a intance
+
+        return ret_dict
+
+
+
+
+class RealDataset_GSO(Dataset):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+    ) -> None:
+        super().__init__()
+
+        self.file_path = file_path
+        self.overfitting = overfitting
+        self.scene_scale = scene_scale
+        self.reso = reso
+        self.reso_encoder = reso_encoder
+        self.classes = False
+        self.load_depth = load_depth
+        self.preprocess = preprocess
+        self.plucker_embedding = plucker_embedding
+
+        self.rgb_list = []
+
+
+
+        # ! for gso-rendering
+        all_objs = os.listdir(self.file_path)
+        all_objs.sort()
+
+        if True: # instant-mesh picked images
+        # if False:
+            all_instances = os.listdir(self.file_path) 
+            # all_fname = [
+            #     t for t in all_instances
+            #     if t.split('.')[1] in ['png', 'jpg']
+            # ]
+
+            # all_fname = [name for name in all_fname if '-input' in name ]
+
+            # all_fname = ['house2-input.png', 'plant-input.png']
+            all_fname = ['house2-input.png']
+
+            self.rgb_list = [os.path.join(self.file_path, name) for name in all_fname]
+
+        if False:
+            for obj_folder in tqdm(all_objs[515:]):
+            # for obj_folder in tqdm(all_objs[:515]):
+            # for obj_folder in tqdm(all_objs[:]):
+            # for obj_folder in tqdm(sorted(os.listdir(self.file_path))[515:]):
+                # for idx in range(0,25,5):
+                for idx in [0]: # only query frontal view is enough
+                    self.rgb_list.append(os.path.join(self.file_path, obj_folder, 'rgba', f'{idx:03d}.png')) 
+
+
+        # for free-3d rendering
+        if False:
+        # if True:
+            # all_instances = sorted(os.listdir(self.file_path))
+
+            all_instances = ['BAGEL_WITH_CHEESE',
+                'BALANCING_CACTUS',
+                'Baby_Elements_Stacking_Cups',
+                'Breyer_Horse_Of_The_Year_2015',
+                'COAST_GUARD_BOAT',
+                'CONE_SORTING',
+                'CREATIVE_BLOCKS_35_MM',
+                'Cole_Hardware_Mini_Honey_Dipper',
+                'FAIRY_TALE_BLOCKS',
+                'FIRE_ENGINE',
+                'FOOD_BEVERAGE_SET',
+                'GEOMETRIC_PEG_BOARD',
+                'Great_Dinos_Triceratops_Toy',
+                'JUICER_SET',
+                'STACKING_BEAR',
+                'STACKING_RING',
+                'Schleich_African_Black_Rhino']
+
+            for instance in all_instances:
+                self.rgb_list += ([
+                    # os.path.join(self.file_path, instance, 'rgb', f'{fname:06d}.png') for fname in range(0,250,50)
+                    # os.path.join(self.file_path, instance, 'rgb', f'{fname:06d}.png') for fname in range(0,250,100)
+                    # os.path.join(self.file_path, instance, f'{fname:03d}.png') for fname in range(0,25,5)
+                    os.path.join(self.file_path, instance, 'render_mvs_25', 'model', f'{fname:03d}.png') for fname in range(0,25,4)
+                ])
+
+        # if True: # g-objv animals images for i23d eval
+        if False:
+        # if True:
+            objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+            dataset_json = os.path.join(objv_dataset, 'dataset.json')
+            with open(dataset_json, 'r') as f:
+                dataset_json = json.load(f)
+
+            # all_objs = dataset_json['Animals'][::3][:6250]
+            all_objs = dataset_json['Animals'][::3][1100:2200][:600]
+
+            for obj_folder in tqdm(all_objs[:]):
+                for idx in [0]: # only query frontal view is enough
+                    self.rgb_list.append(os.path.join(self.file_path, obj_folder, f'{idx}.jpg')) 
+                
+        
+        # ! setup normalizataion
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+
+        assert imgnet_normalize
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+        # camera = torch.load('eval_pose.pt', map_location='cpu')
+        # self.eval_camera = camera
+
+        # pre-cache
+        # self.calc_rays_plucker()
+
+    def __len__(self):
+        return len(self.rgb_list)
+
+    def __getitem__(self, index) -> Any:
+        # return super().__getitem__(index)
+
+        rgb_fname = self.rgb_list[index]
+        # ! preprocess, normalize
+
+        raw_img = imageio.imread(rgb_fname)
+
+        # interpolation=cv2.INTER_AREA)
+        if raw_img.shape[-1] == 4:
+            alpha_mask = raw_img[..., 3:4] / 255.0
+            bg_white = np.ones_like(alpha_mask) * 255.0
+            raw_img = raw_img[..., :3] * alpha_mask + (
+                1 - alpha_mask) * bg_white  #[3, reso_encoder, reso_encoder]
+            raw_img = raw_img.astype(np.uint8)
+
+        # raw_img = recenter(raw_img, np.ones_like(raw_img), border_ratio=0.2)
+
+        # log gt
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+
+        img = torch.from_numpy(img)[..., :3].permute(
+            2, 0, 1
+        ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        ret_dict = {
+            'img': img,
+            # 'ins': str(Path(rgb_fname).parent.parent.stem), # for gso-rendering
+            'ins': str(Path(rgb_fname).relative_to(self.file_path)), # for gso-rendering
+            # 'ins': rgb_fname,
+        }
+
+        return ret_dict
+
+
+
+class RealMVDataset(Dataset):
+
+    def __init__(
+            self,
+            file_path,
+            reso,
+            reso_encoder,
+            preprocess=None,
+            classes=False,
+            load_depth=False,
+            test=False,
+            scene_scale=1,
+            overfitting=False,
+            imgnet_normalize=True,
+            dataset_size=-1,
+            overfitting_bs=-1,
+            interval=1,
+            plucker_embedding=False,
+            shuffle_across_cls=False,
+            wds_split=1,  # 4 splits to accelerate preprocessing
+    ) -> None:
+        super().__init__()
+
+        self.file_path = file_path
+        self.overfitting = overfitting
+        self.scene_scale = scene_scale
+        self.reso = reso
+        self.reso_encoder = reso_encoder
+        self.classes = False
+        self.load_depth = load_depth
+        self.preprocess = preprocess
+        self.plucker_embedding = plucker_embedding
+
+        self.rgb_list = []
+
+        all_fname = [
+            t for t in os.listdir(self.file_path)
+            if t.split('.')[1] in ['png', 'jpg']
+        ]
+        all_fname = [name for name in all_fname if '-input' in name ]
+        # all_fname = [name for name in all_fname if 'sorting_board-input' in name ]
+        # all_fname = [name for name in all_fname if 'teasure_chest-input' in name ]
+        # all_fname = [name for name in all_fname if 'bubble_mart_blue-input' in name ]
+        # all_fname = [name for name in all_fname if 'chair_comfort-input' in name ]
+        self.rgb_list += ([
+            os.path.join(self.file_path, fname) for fname in all_fname
+        ])
+        # if len(self.rgb_list) == 1:
+        #     # placeholder
+        #     self.rgb_list = self.rgb_list * 40
+
+        # ! setup normalizataion
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+
+        azimuths = np.array([30, 90, 150, 210, 270, 330]).astype(float)
+        elevations = np.array([20, -10, 20, -10, 20, -10]).astype(float)
+
+        # zero123pp_pose, _ = generate_input_camera(1.6, [[elevations[i], azimuths[i]] for i in range(6)], fov=30)
+        zero123pp_pose, _ = generate_input_camera(1.8, [[elevations[i], azimuths[i]] for i in range(6)], fov=30)
+        K = torch.Tensor([1.3889, 0.0000, 0.5000, 0.0000, 1.3889, 0.5000, 0.0000, 0.0000, 0.0039]).to(zero123pp_pose) # keeps the same
+        # st()
+        zero123pp_pose = torch.cat([zero123pp_pose.reshape(6,-1), K.unsqueeze(0).repeat(6,1)], dim=-1)
+
+        # ! directly adopt gt input 
+        # self.indices = np.array([0,2,4,5])
+        # eval_camera = zero123pp_pose[self.indices]
+        # self.eval_camera = torch.cat([torch.zeros_like(eval_camera[0:1]),eval_camera], 0) # first c not used as condition here, just placeholder
+
+        # ! adopt mv-diffusion output as input.
+        # self.indices = np.array([1,0,2,4,5])
+        self.indices = np.array([0,1,2,3,4,5])
+        eval_camera = zero123pp_pose[self.indices].float().cpu().numpy() # for normalization
+
+        # eval_camera = zero123pp_pose[self.indices]
+        # self.eval_camera = eval_camera
+        # self.eval_camera = torch.cat([torch.zeros_like(eval_camera[0:1]),eval_camera], 0) # first c not used as condition here, just placeholder
+
+        # # * normalize here
+        self.eval_camera = self.normalize_camera(eval_camera, eval_camera[0:1]) # the first img is not used. 
+
+        # self.mv_resize_cls = torchvision.transforms.Resize(320, interpolation=torchvision.transforms.InterpolationMode.BILINEAR, 
+        #         max_size=None, antialias=True)
+
+    def normalize_camera(self, c, c_frame0):
+        # assert c.shape[0] == self.chunk_size  # 8 o r10
+
+        B = c.shape[0]
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+        canonical_camera_poses = c_frame0[:, :16].reshape(1, 4, 4)
+        inverse_canonical_pose = np.linalg.inv(canonical_camera_poses)
+        inverse_canonical_pose = np.repeat(inverse_canonical_pose, B, 0)
+
+        cam_radius = np.linalg.norm(
+            c_frame0[:, :16].reshape(1, 4, 4)[:, :3, 3],
+            axis=-1,
+            keepdims=False)  # since g-buffer adopts dynamic radius here.
+
+        frame1_fixed_pos = np.repeat(np.eye(4)[None], 1, axis=0)
+        frame1_fixed_pos[:, 2, -1] = -cam_radius
+
+        transform = frame1_fixed_pos @ inverse_canonical_pose
+
+        new_camera_poses = np.repeat(
+            transform, 1, axis=0
+        ) @ camera_poses  # [V, 4, 4]. np.repeat() is th.repeat_interleave()
+
+        c = np.concatenate([new_camera_poses.reshape(B, 16), c[:, 16:]],
+                           axis=-1)
+
+        return c
+
+    def __len__(self):
+        return len(self.rgb_list)
+
+    def __getitem__(self, index) -> Any:
+        # return super().__getitem__(index)
+
+        rgb_fname = self.rgb_list[index]
+
+        raw_img = imageio.imread(rgb_fname)[..., :3]
+        raw_img = cv2.resize(raw_img, (self.reso, self.reso), interpolation=cv2.INTER_CUBIC)
+        raw_img = torch.from_numpy(raw_img).permute(2,0,1).clip(0,255)  # [0,1]
+        img = raw_img / 127.5 - 1
+
+
+        # ! if loading mv-diff output views
+        mv_img = imageio.imread(rgb_fname.replace('-input', ''))
+        mv_img = rearrange(mv_img, '(n h) (m w) c -> (n m) h w c', n=3, m=2)[self.indices]        # (6, 3, 320, 320)
+        mv_img = np.stack([recenter(img, np.ones_like(img), border_ratio=0.1) for img in mv_img], axis=0)
+        mv_img = rearrange(mv_img, 'b h w c -> b c h w') # to torch tradition
+        mv_img = torch.from_numpy(mv_img) / 127.5 - 1
+
+        ret_dict = {
+            'img': img,
+            'mv_img': mv_img,
+            'c': self.eval_camera,
+            'caption': 'null',
+        }
+
+        return ret_dict
+
+
+
+
+
+class NovelViewObjverseDataset(MultiViewObjverseDataset):
+    """novel view prediction version.
+    """
+
+    def __init__(self,
+                 file_path,
+                 reso,
+                 reso_encoder,
+                 preprocess=None,
+                 classes=False,
+                 load_depth=False,
+                 test=False,
+                 scene_scale=1,
+                 overfitting=False,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 overfitting_bs=-1,
+                 **kwargs):
+        super().__init__(file_path, reso, reso_encoder, preprocess, classes,
+                         load_depth, test, scene_scale, overfitting,
+                         imgnet_normalize, dataset_size, overfitting_bs,
+                         **kwargs)
+
+    def __getitem__(self, idx):
+        input_view = super().__getitem__(
+            idx)  # get previous input view results
+
+        # get novel view of the same instance
+        novel_view = super().__getitem__(
+            (idx // self.instance_data_length) * self.instance_data_length +
+            random.randint(0, self.instance_data_length - 1))
+
+        # assert input_view['ins_name'] == novel_view['ins_name'], 'should sample novel view from the same instance'
+
+        input_view.update({f'nv_{k}': v for k, v in novel_view.items()})
+        return input_view
+
+
+class MultiViewObjverseDatasetforLMDB(MultiViewObjverseDataset):
+
+    def __init__(
+        self,
+        file_path,
+        reso,
+        reso_encoder,
+        preprocess=None,
+        classes=False,
+        load_depth=False,
+        test=False,
+        scene_scale=1,
+        overfitting=False,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        overfitting_bs=-1,
+        shuffle_across_cls=False,
+        wds_split=1,
+        four_view_for_latent=False,
+    ):
+        super().__init__(file_path,
+                         reso,
+                         reso_encoder,
+                         preprocess,
+                         classes,
+                         load_depth,
+                         test,
+                         scene_scale,
+                         overfitting,
+                         imgnet_normalize,
+                         dataset_size,
+                         overfitting_bs,
+                         shuffle_across_cls=shuffle_across_cls,
+                         wds_split=wds_split,
+                         four_view_for_latent=four_view_for_latent)
+
+        # assert self.reso == 256
+        self.load_caption = True
+
+        with open(
+                # '/cpfs01/shared/V2V/V2V_hdd/yslan/aigc3d/text_captions_cap3d.json'
+                '/nas/shared/public/yslan/data/text_captions_cap3d.json') as f:
+                # '/nas/shared/V2V/yslan/aigc3d/text_captions_cap3d.json') as f:
+            self.caption_data = json.load(f)
+        # lmdb_path = '/cpfs01/user/yangpeiqing.p/yslan/data/Furnitures_uncompressed/'
+
+        # with open(os.path.join(lmdb_path, 'idx_to_ins_mapping.json')) as f:
+        #     self.idx_to_ins_mapping = json.load(f)
+
+    def __len__(self):
+        return super().__len__()
+        # return 100 # for speed debug
+
+    def quantize_depth(self, depth):
+        # https://developers.google.com/depthmap-metadata/encoding
+        # RangeInverse encoding
+        bg = depth == 0
+        depth[bg] = 3  # no need to allocate capacity to it
+        disparity = 1 / depth
+
+        far = disparity.max().item()  # np array here
+        near = disparity.min().item()
+
+        # d_normalized = (far * (depth-near) / (depth * far - near)) # [0,1] range
+        d_normalized = (disparity - near) / (far - near)  # [0,1] range
+        # imageio.imwrite('depth_negative.jpeg', (((depth - near) / (far - near) * 255)<0).numpy().astype(np.uint8))
+        # imageio.imwrite('depth_negative.jpeg', ((depth <0)*255).numpy().astype(np.uint8))
+        d_normalized = np.nan_to_num(d_normalized.cpu().numpy())
+        d_normalized = (np.clip(d_normalized, 0, 1) * 255).astype(np.uint8)
+        # imageio.imwrite('depth.png', d_normalized)
+
+        # d = 1 / ( (d_normalized / 255) * (far-near) + near)
+        # diff = (d[~bg.numpy()] - depth[~bg].numpy()).sum()
+
+        return d_normalized, near, far  # return disp
+
+    def __getitem__(self, idx):
+        # ret_dict = super().__getitem__(idx)
+        rgb_fname = self.rgb_list[idx]
+        pose_fname = self.pose_list[idx]
+        raw_img = imageio.imread(rgb_fname)  # [..., :3]
+
+        assert raw_img.shape[-1] == 4
+
+        # st() # cv2.imwrite('img_CV2_90.jpg', a, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
+        # if raw_img.shape[-1] == 4:  # ! set bg to white
+
+        alpha_mask = raw_img[..., -1:] / 255  # [0,1]
+
+        raw_img = alpha_mask * raw_img[..., :3] + (
+            1 - alpha_mask) * np.ones_like(raw_img[..., :3]) * 255
+
+        raw_img = np.concatenate([raw_img, alpha_mask * 255], -1)
+        raw_img = raw_img.astype(np.uint8)
+
+        raw_img = cv2.resize(raw_img, (self.reso, self.reso),
+                             interpolation=cv2.INTER_LANCZOS4)
+        alpha_mask = raw_img[..., -1] / 255
+        raw_img = raw_img[..., :3]
+
+        # alpha_mask = cv2.resize(alpha_mask, (self.reso, self.reso),
+        #                         interpolation=cv2.INTER_LANCZOS4)
+
+        c2w = read_camera_matrix_single(pose_fname)  #[1, 4, 4] -> [1, 16]
+        c = np.concatenate([c2w.reshape(16), self.intrinsics],
+                           axis=0).reshape(25).astype(
+                               np.float32)  # 25, no '1' dim needed.
+        c = torch.from_numpy(c)
+        # c = np.concatenate([c2w, self.intrinsics], axis=0).reshape(25)  # 25, no '1' dim needed.
+
+        # if self.load_depth:
+        # depth, depth_mask, depth_mask_sr = read_dnormal(self.depth_list[idx],
+        # try:
+        depth, normal = read_dnormal(self.depth_list[idx], c2w[:3, 3:],
+                                     self.reso, self.reso)
+
+        # ! quantize depth for fast decoding
+        # d_normalized, d_near, d_far = self.quantize_depth(depth)
+
+        # ! add frame_0 alignment
+
+        # try:
+
+        ins = str(
+            (Path(self.data_ins_list[idx]).relative_to(self.file_path)).parent)
+        # if self.shuffle_across_cls:
+        if self.load_caption:
+            caption = self.caption_data['/'.join(ins.split('/')[1:])]
+            bbox = self.load_bbox(torch.from_numpy(alpha_mask) > 0)
+        else:
+            caption = '' # since in g-alignment-xl, some instances will fail.
+            bbox = self.load_bbox(torch.from_numpy(np.ones_like(alpha_mask)) > 0)
+
+        # else:
+        #     caption = self.caption_data[ins]
+
+        ret_dict = {
+            'normal': normal,
+            'raw_img': raw_img,
+            'c': c,
+            # 'depth_mask': depth_mask, # 64x64 here?
+            'bbox': bbox,
+            'ins': ins,
+            'caption': caption,
+            'alpha_mask': alpha_mask,
+            'depth': depth,  # return for pcd creation
+            # 'd_normalized': d_normalized,
+            # 'd_near': d_near,
+            # 'd_far': d_far,
+            # 'fname': rgb_fname,
+        }
+        return ret_dict
+
+
+class MultiViewObjverseDatasetforLMDB_nocaption(MultiViewObjverseDatasetforLMDB):
+
+    def __init__(
+        self,
+        file_path,
+        reso,
+        reso_encoder,
+        preprocess=None,
+        classes=False,
+        load_depth=False,
+        test=False,
+        scene_scale=1,
+        overfitting=False,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        overfitting_bs=-1,
+        shuffle_across_cls=False,
+        wds_split=1,
+        four_view_for_latent=False,
+    ):
+        super().__init__(file_path,
+                         reso,
+                         reso_encoder,
+                         preprocess,
+                         classes,
+                         load_depth,
+                         test,
+                         scene_scale,
+                         overfitting,
+                         imgnet_normalize,
+                         dataset_size,
+                         overfitting_bs,
+                         shuffle_across_cls=shuffle_across_cls,
+                         wds_split=wds_split,
+                         four_view_for_latent=four_view_for_latent)
+
+        self.load_caption = False
+
+
+class Objv_LMDBDataset_MV_Compressed(LMDBDataset_MV_Compressed):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 test=False,
+                 **kwargs):
+        super().__init__(lmdb_path,
+                         reso,
+                         reso_encoder,
+                         imgnet_normalize,
+                         dataset_size=dataset_size,
+                         **kwargs)
+        self.instance_data_length = 40  # ! could save some key attributes in LMDB
+        if test:
+            self.length = self.instance_data_length
+        elif dataset_size > 0:
+            self.length = dataset_size * self.instance_data_length
+
+        # load caption data, and idx-to-ins mapping
+        with open(
+                '/cpfs01/shared/V2V/V2V_hdd/yslan/aigc3d/text_captions_cap3d.json'
+        ) as f:
+            self.caption_data = json.load(f)
+        with open(os.path.join(lmdb_path, 'idx_to_ins_mapping.json')) as f:
+            self.idx_to_ins_mapping = json.load(f)
+
+    def _load_data(self, idx):
+        # '''
+        raw_img, depth, c, bbox = self._load_lmdb_data(idx)
+        # raw_img, depth, c, bbox  = self._load_lmdb_data_no_decompress(idx)
+
+        # resize depth and bbox
+        caption = self.caption_data[self.idx_to_ins_mapping[str(idx)]]
+
+        return {
+            **self._post_process_sample(raw_img, depth),
+            'c': c,
+            'bbox': (bbox * (self.reso / 512.0)).astype(np.uint8),
+            # 'bbox': (bbox*(self.reso/256.0)).astype(np.uint8), # TODO, double check 512 in wds?
+            'caption': caption
+        }
+        # '''
+        # raw_img, depth, c, bbox  = self._load_lmdb_data_no_decompress(idx)
+        # st()
+        # return {}
+
+    def __getitem__(self, idx):
+        return self._load_data(idx)
+
+
+class Objv_LMDBDataset_MV_NoCompressed(Objv_LMDBDataset_MV_Compressed):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 test=False,
+                 **kwargs):
+        super().__init__(lmdb_path, reso, reso_encoder, imgnet_normalize,
+                         dataset_size, test, **kwargs)
+
+    def _load_data(self, idx):
+        # '''
+        raw_img, depth, c, bbox = self._load_lmdb_data_no_decompress(idx)
+
+        # resize depth and bbox
+        caption = self.caption_data[self.idx_to_ins_mapping[str(idx)]]
+
+        return {
+            **self._post_process_sample(raw_img, depth), 'c': c,
+            'bbox': (bbox * (self.reso / 512.0)).astype(np.uint8),
+            'caption': caption
+        }
+        return {}
+
+
+class Objv_LMDBDataset_NV_NoCompressed(Objv_LMDBDataset_MV_NoCompressed):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 test=False,
+                 **kwargs):
+        super().__init__(lmdb_path, reso, reso_encoder, imgnet_normalize,
+                         dataset_size, test, **kwargs)
+
+    def __getitem__(self, idx):
+        input_view = self._load_data(idx)  # get previous input view results
+
+        # get novel view of the same instance
+        try:
+            novel_view = self._load_data(
+                (idx // self.instance_data_length) *
+                self.instance_data_length +
+                random.randint(0, self.instance_data_length - 1))
+        except Exception as e:
+            raise NotImplementedError(idx)
+
+        # assert input_view['ins_name'] == novel_view['ins_name'], 'should sample novel view from the same instance'
+
+        input_view.update({f'nv_{k}': v for k, v in novel_view.items()})
+        return input_view
+
+
+class Objv_LMDBDataset_MV_Compressed_for_lmdb(LMDBDataset_MV_Compressed):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 test=False,
+                 **kwargs):
+        super().__init__(lmdb_path,
+                         reso,
+                         reso_encoder,
+                         imgnet_normalize,
+                         dataset_size=dataset_size,
+                         **kwargs)
+        self.instance_data_length = 40  # ! could save some key attributes in LMDB
+        if test:
+            self.length = self.instance_data_length
+        elif dataset_size > 0:
+            self.length = dataset_size * self.instance_data_length
+
+        # load caption data, and idx-to-ins mapping
+        with open(
+                '/cpfs01/shared/V2V/V2V_hdd/yslan/aigc3d/text_captions_cap3d.json'
+        ) as f:
+            self.caption_data = json.load(f)
+        with open(os.path.join(lmdb_path, 'idx_to_ins_mapping.json')) as f:
+            self.idx_to_ins_mapping = json.load(f)
+
+    # def _load_data(self, idx):
+    #     # '''
+    #     raw_img, depth, c, bbox  = self._load_lmdb_data(idx)
+
+    #     # resize depth and bbox
+    #     caption = self.caption_data[self.idx_to_ins_mapping[str(idx)]]
+
+    #     # st()
+
+    #     return {
+    #         **self._post_process_sample(raw_img, depth), 'c': c,
+    #         'bbox': (bbox*(self.reso/512.0)).astype(np.uint8),
+    #         'caption': caption
+    #     }
+    #     # '''
+    #     # raw_img, depth, c, bbox  = self._load_lmdb_data_no_decompress(idx)
+    #     # st()
+    #     # return {}
+
+    def load_bbox(self, mask):
+        nonzero_value = torch.nonzero(mask)
+        height, width = nonzero_value.max(dim=0)[0]
+        top, left = nonzero_value.min(dim=0)[0]
+        bbox = torch.tensor([top, left, height, width], dtype=torch.float32)
+        return bbox
+
+    def __getitem__(self, idx):
+        raw_img, depth, c, bbox = self._load_lmdb_data(idx)
+        return {'raw_img': raw_img, 'depth': depth, 'c': c, 'bbox': bbox}
+
+
+class Objv_LMDBDataset_NV_Compressed(Objv_LMDBDataset_MV_Compressed):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 **kwargs):
+        super().__init__(lmdb_path, reso, reso_encoder, imgnet_normalize,
+                         dataset_size, **kwargs)
+
+    def __getitem__(self, idx):
+        input_view = self._load_data(idx)  # get previous input view results
+
+        # get novel view of the same instance
+        try:
+            novel_view = self._load_data(
+                (idx // self.instance_data_length) *
+                self.instance_data_length +
+                random.randint(0, self.instance_data_length - 1))
+        except Exception as e:
+            raise NotImplementedError(idx)
+
+        # assert input_view['ins_name'] == novel_view['ins_name'], 'should sample novel view from the same instance'
+
+        input_view.update({f'nv_{k}': v for k, v in novel_view.items()})
+        return input_view
+
+
+#
+
+
+# test tar loading
+def load_wds_ResampledShard(file_path,
+                            batch_size,
+                            num_workers,
+                            reso,
+                            reso_encoder,
+                            test=False,
+                            preprocess=None,
+                            imgnet_normalize=True,
+                            plucker_embedding=False,
+                            decode_encode_img_only=False,
+                            load_instance=False,
+                            mv_input=False,
+                            split_chunk_input=False,
+                            duplicate_sample=True,
+                            append_depth=False,
+                            append_normal=False,
+                            gs_cam_format=False,
+                            orthog_duplicate=False,
+                            **kwargs):
+
+    #     return raw_img, depth, c, bbox, sample_pyd['ins.pyd'], sample_pyd['fname.pyd']
+
+    post_process_cls = PostProcess(
+        reso,
+        reso_encoder,
+        imgnet_normalize=imgnet_normalize,
+        plucker_embedding=plucker_embedding,
+        decode_encode_img_only=decode_encode_img_only,
+        mv_input=mv_input,
+        split_chunk_input=split_chunk_input,
+        duplicate_sample=duplicate_sample,
+        append_depth=append_depth,
+        gs_cam_format=gs_cam_format,
+        orthog_duplicate=orthog_duplicate,
+        append_normal=append_normal,
+    )
+
+    # ! add shuffling
+
+    if isinstance(file_path, list):  # lst of shard urls
+        all_shards = []
+        for url_path in file_path:
+            all_shards.extend(wds.shardlists.expand_source(url_path))
+        logger.log('all_shards', all_shards)
+    else:
+        all_shards = file_path  # to be expanded
+
+    if not load_instance:  # during reconstruction training, load pair
+        if not split_chunk_input:
+            dataset = wds.DataPipeline(
+                wds.ResampledShards(all_shards),  # url_shard
+                # at this point we have an iterator over all the shards
+                wds.shuffle(50),
+                wds.split_by_worker,  # if multi-node
+                wds.tarfile_to_samples(),
+                # add wds.split_by_node here if you are using multiple nodes
+                wds.shuffle(
+                    1000
+                ),  # shuffles in the memory, leverage large RAM for more efficient loading
+                wds.decode(wds.autodecode.basichandlers),  # TODO
+                wds.to_tuple(
+                    "sample.pyd"),  # extract the pyd from top level dict
+                wds.map(post_process_cls.decode_zip),
+                wds.map(post_process_cls.paired_post_process
+                        ),  # create input-novelview paired samples
+                # wds.map(post_process_cls._post_process_sample),
+                # wds.detshuffle(1000),  # shuffles in the memory, leverage large RAM for more efficient loading
+                wds.batched(
+                    16,
+                    partial=True,
+                    # collation_fn=collate
+                )  # streaming more data at once, and rebatch later
+            )
+
+        elif load_gzip:  # deprecated, no performance improve
+
+            dataset = wds.DataPipeline(
+                wds.ResampledShards(all_shards),  # url_shard
+                # at this point we have an iterator over all the shards
+                wds.shuffle(10),
+                wds.split_by_worker,  # if multi-node
+                wds.tarfile_to_samples(),
+                # add wds.split_by_node here if you are using multiple nodes
+                # wds.shuffle(
+                #     100
+                # ),  # shuffles in the memory, leverage large RAM for more efficient loading
+                wds.decode('rgb8'),  # TODO
+                # wds.decode(wds.autodecode.basichandlers),  # TODO
+                # wds.to_tuple('raw_img.jpeg', 'depth.jpeg', 'alpha_mask.jpeg',
+                #              'd_near.npy', 'd_far.npy', "c.npy", 'bbox.npy',
+                #              'ins.txt', 'caption.txt'),
+                wds.to_tuple('raw_img.png', 'depth_alpha.png'),
+                # wds.to_tuple('raw_img.jpg', "c.npy", 'bbox.npy', 'depth.pyd', 'ins.txt', 'caption.txt'),
+                # wds.to_tuple('raw_img.jpg', "c.npy", 'bbox.npy', 'ins.txt', 'caption.txt'),
+                wds.map(post_process_cls.decode_gzip),
+                # wds.map(post_process_cls.paired_post_process_chunk
+                #         ),  # create input-novelview paired samples
+                wds.batched(
+                    20,
+                    partial=True,
+                    # collation_fn=collate
+                )  # streaming more data at once, and rebatch later
+            )
+
+        else:
+            dataset = wds.DataPipeline(
+                wds.ResampledShards(all_shards),  # url_shard
+                # at this point we have an iterator over all the shards
+                wds.shuffle(100),
+                wds.split_by_worker,  # if multi-node
+                wds.tarfile_to_samples(),
+                # add wds.split_by_node here if you are using multiple nodes
+                wds.shuffle(
+                    4000 // split_chunk_size
+                ),  # shuffles in the memory, leverage large RAM for more efficient loading
+                wds.decode(wds.autodecode.basichandlers),  # TODO
+                wds.to_tuple(
+                    "sample.pyd"),  # extract the pyd from top level dict
+                wds.map(post_process_cls.decode_zip),
+                wds.map(post_process_cls.paired_post_process_chunk
+                        ),  # create input-novelview paired samples
+                # wds.map(post_process_cls._post_process_sample),
+                # wds.detshuffle(1000),  # shuffles in the memory, leverage large RAM for more efficient loading
+                wds.batched(
+                    120 // split_chunk_size,
+                    partial=True,
+                    # collation_fn=collate
+                )  # streaming more data at once, and rebatch later
+            )
+
+        loader_shard = wds.WebLoader(
+            dataset,
+            num_workers=num_workers,
+            drop_last=False,
+            batch_size=None,
+            shuffle=False,
+            persistent_workers=num_workers > 0).unbatched().shuffle(
+                1000 // split_chunk_size).batched(batch_size).map(
+                    post_process_cls.create_dict)
+
+        if mv_input:
+            loader_shard = loader_shard.map(post_process_cls.prepare_mv_input)
+
+    else:  # load single instance during test/eval
+        assert batch_size == 1
+
+        dataset = wds.DataPipeline(
+            wds.ResampledShards(all_shards),  # url_shard
+            # at this point we have an iterator over all the shards
+            wds.shuffle(50),
+            wds.split_by_worker,  # if multi-node
+            wds.tarfile_to_samples(),
+            # add wds.split_by_node here if you are using multiple nodes
+            wds.detshuffle(
+                100
+            ),  # shuffles in the memory, leverage large RAM for more efficient loading
+            wds.decode(wds.autodecode.basichandlers),  # TODO
+            wds.to_tuple("sample.pyd"),  # extract the pyd from top level dict
+            wds.map(post_process_cls.decode_zip),
+            # wds.map(post_process_cls.paired_post_process), # create input-novelview paired samples
+            wds.map(post_process_cls._post_process_batch_sample),
+            # wds.detshuffle(1000),  # shuffles in the memory, leverage large RAM for more efficient loading
+            wds.batched(
+                2,
+                partial=True,
+                # collation_fn=collate
+            )  # streaming more data at once, and rebatch later
+        )
+
+        loader_shard = wds.WebLoader(
+            dataset,
+            num_workers=num_workers,
+            drop_last=False,
+            batch_size=None,
+            shuffle=False,
+            persistent_workers=num_workers
+            > 0).unbatched().shuffle(200).batched(batch_size).map(
+                post_process_cls.single_instance_sample_create_dict)
+
+        # persistent_workers=num_workers > 0).unbatched().batched(batch_size).map(post_process_cls.create_dict)
+        # 1000).batched(batch_size).map(post_process_cls.create_dict)
+    # .map(collate)
+    # .map(collate)
+
+    # .batched(batch_size)
+    #
+
+    # .unbatched().shuffle(1000).batched(batch_size).map(post_process)
+    #     # https://github.com/webdataset/webdataset/issues/187
+
+    # return next(iter(loader_shard))
+    #return dataset
+    return loader_shard
+
+
+class PostProcessForDiff:
+
+    def __init__(
+        self,
+        reso,
+        reso_encoder,
+        imgnet_normalize,
+        plucker_embedding,
+        decode_encode_img_only,
+        mv_latent_dir,
+    ) -> None:
+        self.plucker_embedding = plucker_embedding
+
+        self.mv_latent_dir = mv_latent_dir
+        self.decode_encode_img_only = decode_encode_img_only
+
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+
+        self.reso_encoder = reso_encoder
+        self.reso = reso
+        self.instance_data_length = 40
+        # self.pair_per_instance = 1 # compat
+        self.pair_per_instance = 2  # check whether improves IO
+        # self.pair_per_instance = 3 # check whether improves IO
+        # self.pair_per_instance = 4 # check whether improves IO
+        self.camera = torch.load('eval_pose.pt', map_location='cpu').numpy()
+        self.canonical_frame = self.camera[25:26]  # 1, 25 # inverse this
+        self.canonical_frame_pos = self.canonical_frame[:, :16].reshape(4, 4)
+
+    def get_rays_kiui(self, c, opengl=True):
+        h, w = self.reso_encoder, self.reso_encoder
+        intrinsics, pose = c[16:], c[:16].reshape(4, 4)
+        # cx, cy, fx, fy = intrinsics[2], intrinsics[5]
+        fx = fy = 525  # pixel space
+        cx = cy = 256  # rendering default K
+        factor = self.reso / (cx * 2)  # 128 / 512
+        fx = fx * factor
+        fy = fy * factor
+
+        x, y = torch.meshgrid(
+            torch.arange(w, device=pose.device),
+            torch.arange(h, device=pose.device),
+            indexing="xy",
+        )
+        x = x.flatten()
+        y = y.flatten()
+
+        cx = w * 0.5
+        cy = h * 0.5
+
+        # focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy))
+
+        camera_dirs = F.pad(
+            torch.stack(
+                [
+                    (x - cx + 0.5) / fx,
+                    (y - cy + 0.5) / fy * (-1.0 if opengl else 1.0),
+                ],
+                dim=-1,
+            ),
+            (0, 1),
+            value=(-1.0 if opengl else 1.0),
+        )  # [hw, 3]
+
+        rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1)  # [hw, 3]
+        rays_o = pose[:3, 3].unsqueeze(0).expand_as(rays_d)  # [hw, 3]
+
+        rays_o = rays_o.view(h, w, 3)
+        rays_d = safe_normalize(rays_d).view(h, w, 3)
+
+        return rays_o, rays_d
+
+    def gen_rays(self, c):
+        # Generate rays
+        intrinsics, c2w = c[16:], c[:16].reshape(4, 4)
+        self.h = self.reso_encoder
+        self.w = self.reso_encoder
+        yy, xx = torch.meshgrid(
+            torch.arange(self.h, dtype=torch.float32) + 0.5,
+            torch.arange(self.w, dtype=torch.float32) + 0.5,
+            indexing='ij')
+
+        # normalize to 0-1 pixel range
+        yy = yy / self.h
+        xx = xx / self.w
+
+        # K = np.array([f_x, 0, w / 2, 0, f_y, h / 2, 0, 0, 1]).reshape(3, 3)
+        cx, cy, fx, fy = intrinsics[2], intrinsics[5], intrinsics[
+            0], intrinsics[4]
+        # cx *= self.w
+        # cy *= self.h
+
+        # f_x = f_y = fx * h / res_raw
+        c2w = torch.from_numpy(c2w).float()
+
+        xx = (xx - cx) / fx
+        yy = (yy - cy) / fy
+        zz = torch.ones_like(xx)
+        dirs = torch.stack((xx, yy, zz), dim=-1)  # OpenCV convention
+        dirs /= torch.norm(dirs, dim=-1, keepdim=True)
+        dirs = dirs.reshape(-1, 3, 1)
+        del xx, yy, zz
+        # st()
+        dirs = (c2w[None, :3, :3] @ dirs)[..., 0]
+
+        origins = c2w[None, :3, 3].expand(self.h * self.w, -1).contiguous()
+        origins = origins.view(self.h, self.w, 3)
+        dirs = dirs.view(self.h, self.w, 3)
+
+        return origins, dirs
+
+    def normalize_camera(self, c):
+        # assert c.shape[0] == self.chunk_size  # 8 o r10
+
+        c = c[None]  # api compat
+        B = c.shape[0]
+
+        camera_poses = c[:, :16].reshape(B, 4, 4)  # 3x4
+
+        cam_radius = np.linalg.norm(
+            self.canonical_frame_pos.reshape(4, 4)[:3, 3],
+            axis=-1,
+            keepdims=False)  # since g-buffer adopts dynamic radius here.
+        frame1_fixed_pos = np.eye(4)
+        frame1_fixed_pos[2, -1] = -cam_radius
+
+        transform = frame1_fixed_pos @ np.linalg.inv(
+            self.canonical_frame_pos)  # 4,4
+        # from LGM, https://github.com/3DTopia/LGM/blob/fe8d12cff8c827df7bb77a3c8e8b37408cb6fe4c/core/provider_objaverse.py#L127
+        # transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(c[[0,4]])
+
+        new_camera_poses = transform[None] @ camera_poses  # [V, 4, 4]
+
+        c = np.concatenate([new_camera_poses.reshape(B, 16), c[:, 16:]],
+                           axis=-1)
+
+        return c[0]
+
+    def _post_process_sample(self, data_sample):
+        # raw_img, depth, c, bbox, caption, ins = data_sample
+        raw_img, c, caption, ins = data_sample
+
+        # c = self.normalize_camera(c) @ if relative pose.
+
+        img = raw_img  # 256x256
+
+        img = torch.from_numpy(img).permute(2, 0, 1) / 127.5 - 1
+
+        # load latent.
+        # latent_path = Path(self.mv_latent_dir, ins, 'latent.npy') # ! a converged version, before adding augmentation
+
+        # if random.random() > 0.5:
+        #     latent_path = Path(self.mv_latent_dir, ins, 'latent.npy')
+        # else: # augmentation, double the dataset
+        latent_path = Path(
+            self.mv_latent_dir.replace('v=4-final', 'v=4-rotate'), ins,
+            'latent.npy')
+
+        latent = np.load(latent_path)
+
+        # return (img_to_encoder, img, c, caption, ins)
+        return (latent, img, c, caption, ins)
+
+    def rand_sample_idx(self):
+        return random.randint(0, self.instance_data_length - 1)
+
+    def rand_pair(self):
+        return (self.rand_sample_idx() for _ in range(2))
+
+    def paired_post_process(self, sample):
+        # repeat n times?
+        all_inp_list = []
+        all_nv_list = []
+        caption, ins = sample[-2:]
+        # expanded_return = []
+        for _ in range(self.pair_per_instance):
+            cano_idx, nv_idx = self.rand_pair()
+            cano_sample = self._post_process_sample(item[cano_idx]
+                                                    for item in sample[:-2])
+            nv_sample = self._post_process_sample(item[nv_idx]
+                                                  for item in sample[:-2])
+            all_inp_list.extend(cano_sample)
+            all_nv_list.extend(nv_sample)
+        return (*all_inp_list, *all_nv_list, caption, ins)
+        # return [cano_sample, nv_sample, caption, ins]
+        # return (*cano_sample, *nv_sample, caption, ins)
+
+    # def single_sample_create_dict(self, sample, prefix=''):
+    #     # if len(sample) == 1:
+    #     #     sample = sample[0]
+    #     # assert len(sample) == 6
+    #     img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox = sample
+    #     return {
+    #         # **sample,
+    #         f'{prefix}img_to_encoder': img_to_encoder,
+    #         f'{prefix}img': img,
+    #         f'{prefix}depth_mask': fg_mask_reso,
+    #         f'{prefix}depth': depth_reso,
+    #         f'{prefix}c': c,
+    #         f'{prefix}bbox': bbox,
+    #     }
+
+    def single_sample_create_dict(self, sample, prefix=''):
+        # if len(sample) == 1:
+        #     sample = sample[0]
+        # assert len(sample) == 6
+        # img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox = sample
+        # img_to_encoder, img, c, caption, ins = sample
+        # img, c, caption, ins = sample
+        latent, img, c, caption, ins = sample
+        # load latent
+        return {
+            # **sample,
+            # 'img_to_encoder': img_to_encoder,
+            'latent': latent,
+            'img': img,
+            'c': c,
+            'caption': caption,
+            'ins': ins
+        }
+
+    def decode_zip(self, sample_pyd, shape=(256, 256)):
+        if isinstance(sample_pyd, tuple):
+            sample_pyd = sample_pyd[0]
+        assert isinstance(sample_pyd, dict)
+
+        raw_img = decompress_and_open_image_gzip(
+            sample_pyd['raw_img'],
+            is_img=True,
+            decompress=True,
+            decompress_fn=lz4.frame.decompress)
+
+        caption = sample_pyd['caption'].decode('utf-8')
+        ins = sample_pyd['ins'].decode('utf-8')
+
+        c = decompress_array(sample_pyd['c'], (25, ),
+                             np.float32,
+                             decompress=True,
+                             decompress_fn=lz4.frame.decompress)
+
+        # bbox = decompress_array(
+        #     sample_pyd['bbox'],
+        #     (
+        #         40,
+        #         4,
+        #     ),
+        #     np.float32,
+        #     # decompress=False)
+        #     decompress=True,
+        #     decompress_fn=lz4.frame.decompress)
+
+        # if self.decode_encode_img_only:
+        #     depth = np.zeros(shape=(40, *shape)) # save loading time
+        # else:
+        #     depth = decompress_array(sample_pyd['depth'], (40, *shape),
+        #                             np.float32,
+        #                             decompress=True,
+        #                             decompress_fn=lz4.frame.decompress)
+
+        # return {'raw_img': raw_img, 'depth': depth, 'bbox': bbox, 'caption': caption, 'ins': ins, 'c': c}
+        # return raw_img, depth, c, bbox, caption, ins
+        # return raw_img, bbox, caption, ins
+        # return bbox, caption, ins
+        return raw_img, c, caption, ins
+        # ! run single-instance pipeline first
+        # return raw_img[0], depth[0], c[0], bbox[0], caption, ins
+
+    def create_dict(self, sample):
+        # sample = [item[0] for item in sample] # wds wrap items in []
+        # cano_sample_list = [[] for _ in range(6)]
+        # nv_sample_list = [[] for _ in range(6)]
+        # for idx in range(0, self.pair_per_instance):
+        #     cano_sample = sample[6*idx:6*(idx+1)]
+        #     nv_sample = sample[6*self.pair_per_instance+6*idx:6*self.pair_per_instance+6*(idx+1)]
+
+        #     for item_idx in range(6):
+        #         cano_sample_list[item_idx].append(cano_sample[item_idx])
+        #         nv_sample_list[item_idx].append(nv_sample[item_idx])
+
+        #         # ! cycle input/output view for more pairs
+        #         cano_sample_list[item_idx].append(nv_sample[item_idx])
+        #         nv_sample_list[item_idx].append(cano_sample[item_idx])
+
+        cano_sample = self.single_sample_create_dict(sample, prefix='')
+        # nv_sample = self.single_sample_create_dict((torch.cat(item_list) for item_list in nv_sample_list) , prefix='nv_')
+
+        return cano_sample
+        # return {
+        #     **cano_sample,
+        #     # **nv_sample,
+        #     'caption': sample[-2],
+        #     'ins': sample[-1]
+        # }
+
+
+# test tar loading
+def load_wds_diff_ResampledShard(file_path,
+                                 batch_size,
+                                 num_workers,
+                                 reso,
+                                 reso_encoder,
+                                 test=False,
+                                 preprocess=None,
+                                 imgnet_normalize=True,
+                                 plucker_embedding=False,
+                                 decode_encode_img_only=False,
+                                 mv_latent_dir='',
+                                 **kwargs):
+
+    #     return raw_img, depth, c, bbox, sample_pyd['ins.pyd'], sample_pyd['fname.pyd']
+
+    post_process_cls = PostProcessForDiff(
+        reso,
+        reso_encoder,
+        imgnet_normalize=imgnet_normalize,
+        plucker_embedding=plucker_embedding,
+        decode_encode_img_only=decode_encode_img_only,
+        mv_latent_dir=mv_latent_dir,
+    )
+
+    if isinstance(file_path, list):  # lst of shard urls
+        all_shards = []
+        for url_path in file_path:
+            all_shards.extend(wds.shardlists.expand_source(url_path))
+        logger.log('all_shards', all_shards)
+    else:
+        all_shards = file_path  # to be expanded
+
+    dataset = wds.DataPipeline(
+        wds.ResampledShards(all_shards),  # url_shard
+        # at this point we have an iterator over all the shards
+        wds.shuffle(100),
+        wds.split_by_worker,  # if multi-node
+        wds.tarfile_to_samples(),
+        # add wds.split_by_node here if you are using multiple nodes
+        wds.shuffle(
+            20000
+        ),  # shuffles in the memory, leverage large RAM for more efficient loading
+        wds.decode(wds.autodecode.basichandlers),  # TODO
+        wds.to_tuple("sample.pyd"),  # extract the pyd from top level dict
+        wds.map(post_process_cls.decode_zip),
+        # wds.map(post_process_cls.paired_post_process), # create input-novelview paired samples
+        wds.map(post_process_cls._post_process_sample),
+        # wds.detshuffle(1000),  # shuffles in the memory, leverage large RAM for more efficient loading
+        wds.batched(
+            100,
+            partial=True,
+            # collation_fn=collate
+        )  # streaming more data at once, and rebatch later
+    )
+
+    loader_shard = wds.WebLoader(
+        dataset,
+        num_workers=num_workers,
+        drop_last=False,
+        batch_size=None,
+        shuffle=False,
+        persistent_workers=num_workers
+        > 0).unbatched().shuffle(2500).batched(batch_size).map(
+            post_process_cls.create_dict)
+
+    # persistent_workers=num_workers > 0).unbatched().batched(batch_size).map(post_process_cls.create_dict)
+    # 1000).batched(batch_size).map(post_process_cls.create_dict)
+    # .map(collate)
+    # .map(collate)
+
+    # .batched(batch_size)
+    #
+
+    # .unbatched().shuffle(1000).batched(batch_size).map(post_process)
+    #     # https://github.com/webdataset/webdataset/issues/187
+
+    # return next(iter(loader_shard))
+    #return dataset
+    return loader_shard
+
+
+def load_wds_data(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        num_workers=6,
+        plucker_embedding=False,
+        decode_encode_img_only=False,
+        load_wds_diff=False,
+        load_wds_latent=False,
+        load_instance=False,  # for evaluation
+        mv_input=False,
+        split_chunk_input=False,
+        duplicate_sample=True,
+        mv_latent_dir='',
+        append_depth=False,
+        gs_cam_format=False,
+        orthog_duplicate=False,
+        **args):
+
+    if load_wds_diff:
+        # assert num_workers == 0  # on aliyun, worker=0 performs much much faster
+        wds_loader = load_wds_diff_ResampledShard(
+            file_path,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            reso=reso,
+            reso_encoder=reso_encoder,
+            plucker_embedding=plucker_embedding,
+            decode_encode_img_only=decode_encode_img_only,
+            mv_input=mv_input,
+            split_chunk_input=split_chunk_input,
+            append_depth=append_depth,
+            mv_latent_dir=mv_latent_dir,
+            gs_cam_format=gs_cam_format,
+            orthog_duplicate=orthog_duplicate,
+        )
+    elif load_wds_latent:
+        # for diffusion training, cache latent
+        wds_loader = load_wds_latent_ResampledShard(
+            file_path,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            reso=reso,
+            reso_encoder=reso_encoder,
+            plucker_embedding=plucker_embedding,
+            decode_encode_img_only=decode_encode_img_only,
+            mv_input=mv_input,
+            split_chunk_input=split_chunk_input,
+        )
+
+    # elif load_instance:
+    #     wds_loader = load_wds_instance_ResampledShard(
+    #         file_path,
+    #         batch_size=batch_size,
+    #         num_workers=num_workers,
+    #         reso=reso,
+    #         reso_encoder=reso_encoder,
+    #         plucker_embedding=plucker_embedding,
+    #         decode_encode_img_only=decode_encode_img_only
+    #     )
+
+    else:
+        wds_loader = load_wds_ResampledShard(
+            file_path,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            reso=reso,
+            reso_encoder=reso_encoder,
+            plucker_embedding=plucker_embedding,
+            decode_encode_img_only=decode_encode_img_only,
+            load_instance=load_instance,
+            mv_input=mv_input,
+            split_chunk_input=split_chunk_input,
+            duplicate_sample=duplicate_sample,
+            append_depth=append_depth,
+            gs_cam_format=gs_cam_format,
+            orthog_duplicate=orthog_duplicate,
+        )
+
+    while True:
+        yield from wds_loader
+        # yield from wds_loader
+
+
+class PostProcess_forlatent:
+
+    def __init__(
+        self,
+        reso,
+        reso_encoder,
+        imgnet_normalize,
+        plucker_embedding,
+        decode_encode_img_only,
+    ) -> None:
+        self.plucker_embedding = plucker_embedding
+        self.decode_encode_img_only = decode_encode_img_only
+
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+
+        self.reso_encoder = reso_encoder
+        self.reso = reso
+        self.instance_data_length = 40
+        # self.pair_per_instance = 1 # compat
+        self.pair_per_instance = 2  # check whether improves IO
+        # self.pair_per_instance = 3 # check whether improves IO
+        # self.pair_per_instance = 4 # check whether improves IO
+
+    def _post_process_sample(self, data_sample):
+        # raw_img, depth, c, bbox, caption, ins = data_sample
+        raw_img, c, caption, ins = data_sample
+
+        # bbox = (bbox*(self.reso/256)).astype(np.uint8) # normalize bbox to the reso range
+
+        if raw_img.shape[-2] != self.reso_encoder:
+            img_to_encoder = cv2.resize(raw_img,
+                                        (self.reso_encoder, self.reso_encoder),
+                                        interpolation=cv2.INTER_LANCZOS4)
+        else:
+            img_to_encoder = raw_img
+
+        img_to_encoder = self.normalize(img_to_encoder)
+        if self.plucker_embedding:
+            rays_o, rays_d = self.gen_rays(c)
+            rays_plucker = torch.cat(
+                [torch.cross(rays_o, rays_d, dim=-1), rays_d],
+                dim=-1).permute(2, 0, 1)  # [h, w, 6] -> 6,h,w
+            img_to_encoder = torch.cat([img_to_encoder, rays_plucker], 0)
+
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+
+        img = torch.from_numpy(img).permute(2, 0, 1) / 127.5 - 1
+
+        return (img_to_encoder, img, c, caption, ins)
+
+    def rand_sample_idx(self):
+        return random.randint(0, self.instance_data_length - 1)
+
+    def rand_pair(self):
+        return (self.rand_sample_idx() for _ in range(2))
+
+    def paired_post_process(self, sample):
+        # repeat n times?
+        all_inp_list = []
+        all_nv_list = []
+        caption, ins = sample[-2:]
+        # expanded_return = []
+        for _ in range(self.pair_per_instance):
+            cano_idx, nv_idx = self.rand_pair()
+            cano_sample = self._post_process_sample(item[cano_idx]
+                                                    for item in sample[:-2])
+            nv_sample = self._post_process_sample(item[nv_idx]
+                                                  for item in sample[:-2])
+            all_inp_list.extend(cano_sample)
+            all_nv_list.extend(nv_sample)
+        return (*all_inp_list, *all_nv_list, caption, ins)
+        # return [cano_sample, nv_sample, caption, ins]
+        # return (*cano_sample, *nv_sample, caption, ins)
+    def paired_post_process(self, sample):
+        # repeat n times?
+        all_inp_list = []
+        all_nv_list = []
+        caption, ins = sample[-2:]
+        # expanded_return = []
+        for _ in range(self.pair_per_instance):
+            cano_idx, nv_idx = self.rand_pair()
+            cano_sample = self._post_process_sample(item[cano_idx]
+                                                    for item in sample[:-2])
+            nv_sample = self._post_process_sample(item[nv_idx]
+                                                  for item in sample[:-2])
+            all_inp_list.extend(cano_sample)
+            all_nv_list.extend(nv_sample)
+        return (*all_inp_list, *all_nv_list, caption, ins)
+        # return [cano_sample, nv_sample, caption, ins]
+        # return (*cano_sample, *nv_sample, caption, ins)
+
+    # def single_sample_create_dict(self, sample, prefix=''):
+    #     # if len(sample) == 1:
+    #     #     sample = sample[0]
+    #     # assert len(sample) == 6
+    #     img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox = sample
+    #     return {
+    #         # **sample,
+    #         f'{prefix}img_to_encoder': img_to_encoder,
+    #         f'{prefix}img': img,
+    #         f'{prefix}depth_mask': fg_mask_reso,
+    #         f'{prefix}depth': depth_reso,
+    #         f'{prefix}c': c,
+    #         f'{prefix}bbox': bbox,
+    #     }
+
+    def single_sample_create_dict(self, sample, prefix=''):
+        # if len(sample) == 1:
+        #     sample = sample[0]
+        # assert len(sample) == 6
+        # img_to_encoder, img, fg_mask_reso, depth_reso, c, bbox = sample
+        img_to_encoder, img, c, caption, ins = sample
+        return {
+            # **sample,
+            'img_to_encoder': img_to_encoder,
+            'img': img,
+            'c': c,
+            'caption': caption,
+            'ins': ins
+        }
+
+    def decode_zip(self, sample_pyd, shape=(256, 256)):
+        if isinstance(sample_pyd, tuple):
+            sample_pyd = sample_pyd[0]
+        assert isinstance(sample_pyd, dict)
+
+        latent = sample_pyd['latent']
+        caption = sample_pyd['caption'].decode('utf-8')
+        c = sample_pyd['c']
+        # img = sample_pyd['img']
+        # st()
+
+        return latent, caption, c
+
+    def create_dict(self, sample):
+
+        return {
+            # **sample,
+            'latent': sample[0],
+            'caption': sample[1],
+            'c': sample[2],
+        }
+
+
+# test tar loading
+def load_wds_latent_ResampledShard(file_path,
+                                   batch_size,
+                                   num_workers,
+                                   reso,
+                                   reso_encoder,
+                                   test=False,
+                                   preprocess=None,
+                                   imgnet_normalize=True,
+                                   plucker_embedding=False,
+                                   decode_encode_img_only=False,
+                                   **kwargs):
+
+    #     return raw_img, depth, c, bbox, sample_pyd['ins.pyd'], sample_pyd['fname.pyd']
+
+    post_process_cls = PostProcess_forlatent(
+        reso,
+        reso_encoder,
+        imgnet_normalize=imgnet_normalize,
+        plucker_embedding=plucker_embedding,
+        decode_encode_img_only=decode_encode_img_only,
+    )
+
+    if isinstance(file_path, list):  # lst of shard urls
+        all_shards = []
+        for url_path in file_path:
+            all_shards.extend(wds.shardlists.expand_source(url_path))
+        logger.log('all_shards', all_shards)
+    else:
+        all_shards = file_path  # to be expanded
+
+    dataset = wds.DataPipeline(
+        wds.ResampledShards(all_shards),  # url_shard
+        # at this point we have an iterator over all the shards
+        wds.shuffle(50),
+        wds.split_by_worker,  # if multi-node
+        wds.tarfile_to_samples(),
+        # add wds.split_by_node here if you are using multiple nodes
+        wds.detshuffle(
+            2500
+        ),  # shuffles in the memory, leverage large RAM for more efficient loading
+        wds.decode(wds.autodecode.basichandlers),  # TODO
+        wds.to_tuple("sample.pyd"),  # extract the pyd from top level dict
+        wds.map(post_process_cls.decode_zip),
+        # wds.map(post_process_cls._post_process_sample),
+        # wds.detshuffle(1000),  # shuffles in the memory, leverage large RAM for more efficient loading
+        wds.batched(
+            150,
+            partial=True,
+            # collation_fn=collate
+        )  # streaming more data at once, and rebatch later
+    )
+
+    loader_shard = wds.WebLoader(
+        dataset,
+        num_workers=num_workers,
+        drop_last=False,
+        batch_size=None,
+        shuffle=False,
+        persistent_workers=num_workers
+        > 0).unbatched().shuffle(1000).batched(batch_size).map(
+            post_process_cls.create_dict)
+
+    # persistent_workers=num_workers > 0).unbatched().batched(batch_size).map(post_process_cls.create_dict)
+    # 1000).batched(batch_size).map(post_process_cls.create_dict)
+    # .map(collate)
+    # .map(collate)
+
+    # .batched(batch_size)
+    #
+
+    # .unbatched().shuffle(1000).batched(batch_size).map(post_process)
+    #     # https://github.com/webdataset/webdataset/issues/187
+
+    # return next(iter(loader_shard))
+    #return dataset
+    return loader_shard
diff --git a/datasets/lsun_bedroom.py b/datasets/lsun_bedroom.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a5be22eef8c7434331a76ef5ed7332a98a446ef
--- /dev/null
+++ b/datasets/lsun_bedroom.py
@@ -0,0 +1,54 @@
+"""
+Convert an LSUN lmdb database into a directory of images.
+"""
+
+import argparse
+import io
+import os
+
+from PIL import Image
+import lmdb
+import numpy as np
+
+
+def read_images(lmdb_path, image_size):
+    env = lmdb.open(lmdb_path, map_size=1099511627776, max_readers=100, readonly=True)
+    with env.begin(write=False) as transaction:
+        cursor = transaction.cursor()
+        for _, webp_data in cursor:
+            img = Image.open(io.BytesIO(webp_data))
+            width, height = img.size
+            scale = image_size / min(width, height)
+            img = img.resize(
+                (int(round(scale * width)), int(round(scale * height))),
+                resample=Image.BOX,
+            )
+            arr = np.array(img)
+            h, w, _ = arr.shape
+            h_off = (h - image_size) // 2
+            w_off = (w - image_size) // 2
+            arr = arr[h_off : h_off + image_size, w_off : w_off + image_size]
+            yield arr
+
+
+def dump_images(out_dir, images, prefix):
+    if not os.path.exists(out_dir):
+        os.mkdir(out_dir)
+    for i, img in enumerate(images):
+        Image.fromarray(img).save(os.path.join(out_dir, f"{prefix}_{i:07d}.png"))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image-size", help="new image size", type=int, default=256)
+    parser.add_argument("--prefix", help="class name", type=str, default="bedroom")
+    parser.add_argument("lmdb_path", help="path to an LSUN lmdb database")
+    parser.add_argument("out_dir", help="path to output directory")
+    args = parser.parse_args()
+
+    images = read_images(args.lmdb_path, args.image_size)
+    dump_images(args.out_dir, images, args.prefix)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/datasets/shapenet.py b/datasets/shapenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c82b095ab9f3a8f24e87e57fe574f8683ba6ff5
--- /dev/null
+++ b/datasets/shapenet.py
@@ -0,0 +1,1022 @@
+import os
+import torchvision
+import pickle
+from typing import Any
+import lmdb
+import cv2
+import imageio
+import numpy as np
+from PIL import Image
+import Imath
+import OpenEXR
+from pdb import set_trace as st
+from pathlib import Path
+
+from functools import partial
+import io
+import gzip
+import random
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+from torch.utils.data.distributed import DistributedSampler
+from pathlib import Path
+
+from guided_diffusion import logger
+
+def load_dataset(
+    file_path="",
+    reso=64,
+    reso_encoder=224,
+    batch_size=1,
+    #   shuffle=True,
+    num_workers=6,
+    load_depth=False,
+    preprocess=None,
+    imgnet_normalize=True,
+    dataset_size=-1,
+    trainer_name='input_rec',
+    use_lmdb=False,
+    infi_sampler=True
+):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # st()
+    if use_lmdb:
+        logger.log('using LMDB dataset')
+        # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+        if 'nv' in trainer_name:
+            dataset_cls = LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        else:
+            dataset_cls = LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        # dataset = dataset_cls(file_path)
+    else:
+        if 'nv' in trainer_name:
+            dataset_cls = NovelViewDataset  # 1.5-2iter/s
+        else:
+            dataset_cls = MultiViewDataset
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size)
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+    
+    loader = DataLoader(dataset,
+                        batch_size=batch_size,
+                        num_workers=num_workers,
+                        drop_last=False,
+                        pin_memory=True,
+                        persistent_workers=num_workers > 0, 
+                        shuffle=False)
+    return loader
+
+
+def load_data(
+    file_path="",
+    reso=64,
+    reso_encoder=224,
+    batch_size=1,
+    #   shuffle=True,
+    num_workers=6,
+    load_depth=False,
+    preprocess=None,
+    imgnet_normalize=True,
+    dataset_size=-1,
+    trainer_name='input_rec',
+    use_lmdb=False,
+    infi_sampler=True
+):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # st()
+    if use_lmdb:
+        logger.log('using LMDB dataset')
+        # dataset_cls = LMDBDataset_MV #  2.5-3iter/s, but unstable, drops to 1 later.
+        if 'nv' in trainer_name:
+            dataset_cls = LMDBDataset_NV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        else:
+            dataset_cls = LMDBDataset_MV_Compressed  #  2.5-3iter/s, but unstable, drops to 1 later.
+        # dataset = dataset_cls(file_path)
+    else:
+        if 'nv' in trainer_name:
+            dataset_cls = NovelViewDataset  # 1.5-2iter/s
+        else:
+            dataset_cls = MultiViewDataset
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size)
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+    
+    # st()
+
+    if infi_sampler:
+        train_sampler = DistributedSampler(dataset=dataset,
+                                        shuffle=True,
+                                        drop_last=True)
+
+        loader = DataLoader(dataset,
+                            batch_size=batch_size,
+                            num_workers=num_workers,
+                            drop_last=True,
+                            pin_memory=True,
+                            persistent_workers=num_workers > 0,
+                            sampler=train_sampler)
+
+        while True:
+            yield from loader
+
+    else:
+        # loader = DataLoader(dataset,
+        #                     batch_size=batch_size,
+        #                     num_workers=num_workers,
+        #                     drop_last=False,
+        #                     pin_memory=True,
+        #                     persistent_workers=num_workers > 0, 
+        #                     shuffle=False)
+        st()
+        return dataset
+
+
+def load_eval_rays(file_path="",
+                   reso=64,
+                   reso_encoder=224,
+                   imgnet_normalize=True):
+    dataset = MultiViewDataset(file_path,
+                               reso,
+                               reso_encoder,
+                               imgnet_normalize=imgnet_normalize)
+    pose_list = dataset.single_pose_list
+    ray_list = []
+    for pose_fname in pose_list:
+        # c2w = dataset.get_c2w(pose_fname).reshape(1,4,4)  #[1, 4, 4]
+        # rays_o, rays_d = dataset.gen_rays(c2w)
+        # ray_list.append(
+        #     [rays_o.unsqueeze(0),
+        #      rays_d.unsqueeze(0),
+        #      c2w.reshape(-1, 16)])
+
+        c2w = dataset.get_c2w(pose_fname).reshape(16)  #[1, 4, 4]
+
+        c = torch.cat([c2w, dataset.intrinsics],
+                      dim=0).reshape(25)  # 25, no '1' dim needed.
+        ray_list.append(c)
+
+    return ray_list
+
+
+def load_eval_data(file_path="",
+                   reso=64,
+                   reso_encoder=224,
+                   batch_size=1,
+                   num_workers=1,
+                   load_depth=False,
+                   preprocess=None,
+                   imgnet_normalize=True, 
+                   interval=1,
+                   **kwargs
+                   ):
+
+    dataset = MultiViewDataset(file_path,
+                               reso,
+                               reso_encoder,
+                               preprocess=preprocess,
+                               load_depth=load_depth,
+                               test=True,
+                               imgnet_normalize=imgnet_normalize,
+                               interval=interval, **kwargs)
+    print('eval dataset size: {}'.format(len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset)
+    loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+        shuffle=False,
+    )
+    # sampler=train_sampler)
+    return loader
+
+
+def load_memory_data(file_path="",
+                     reso=64,
+                     reso_encoder=224,
+                     batch_size=1,
+                     num_workers=1,
+                     load_depth=True,
+                     preprocess=None,
+                     imgnet_normalize=True):
+    # load a single-instance into the memory to speed up training IO
+    dataset = MultiViewDataset(file_path,
+                               reso,
+                               reso_encoder,
+                               preprocess=preprocess,
+                               load_depth=True,
+                               test=False,
+                               overfitting=True,
+                               imgnet_normalize=imgnet_normalize,
+                               overfitting_bs=batch_size)
+    logger.log('!!!!!!! memory dataset size: {} !!!!!!'.format(len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset)
+    loader = DataLoader(
+        dataset,
+        batch_size=len(dataset),
+        num_workers=num_workers,
+        drop_last=False,
+        shuffle=False,
+    )
+
+    all_data: dict = next(iter(loader))
+    while True:
+        start_idx = np.random.randint(0, len(dataset) - batch_size + 1)
+        yield {
+            k: v[start_idx:start_idx + batch_size]
+            for k, v in all_data.items()
+        }
+
+
+class MultiViewDataset(Dataset):
+
+    def __init__(self,
+                 file_path,
+                 reso,
+                 reso_encoder,
+                 preprocess=None,
+                 classes=False,
+                 load_depth=False,
+                 test=False,
+                 scene_scale=1,
+                 overfitting=False,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 overfitting_bs=-1, 
+                 interval=1):
+        self.file_path = file_path
+        self.overfitting = overfitting
+        self.scene_scale = scene_scale
+        self.reso = reso
+        self.reso_encoder = reso_encoder
+        self.classes = False
+        self.load_depth = load_depth
+        self.preprocess = preprocess
+        assert not self.classes, "Not support class condition now."
+
+        # self.ins_list = os.listdir(self.file_path)
+        # if test: # TODO
+
+        dataset_name = Path(self.file_path).stem.split('_')[0]
+        self.dataset_name = dataset_name
+
+        if test:
+            # ins_list_file = Path(self.file_path).parent / f'{dataset_name}_test_list.txt' # ? in domain
+            if dataset_name == 'chair':
+                self.ins_list = sorted(os.listdir(
+                    self.file_path))[1:2]  # more diversity
+            else:
+                self.ins_list = sorted(os.listdir(self.file_path))[
+                    0:1]  # the first 1 instance for evaluation reference.
+        else:
+            # self.ins_list = sorted(Path(self.file_path).glob('[0-8]*'))
+            # self.ins_list = Path(self.file_path).glob('*')
+            # self.ins_list = list(Path(self.file_path).glob('*'))[:dataset_size]
+
+            # ins_list_file = Path(
+            #     self.file_path).parent / f'{dataset_name}s_train_list.txt'
+            # assert ins_list_file.exists(), 'add training list for ShapeNet'
+            # with open(ins_list_file, 'r') as f:
+            #     self.ins_list = [name.strip() for name in f.readlines()]
+
+            # if dataset_name == 'chair':
+            ins_list_file = Path(
+                self.file_path).parent / f'{dataset_name}_train_list.txt'
+            # st()
+            assert ins_list_file.exists(), 'add training list for ShapeNet'
+            with open(ins_list_file, 'r') as f:
+                self.ins_list = [name.strip()
+                                 for name in f.readlines()][:dataset_size]
+            # else:
+            #     self.ins_list = Path(self.file_path).glob('*')
+
+        if overfitting:
+            self.ins_list = self.ins_list[:1]
+
+        self.rgb_list = []
+        self.pose_list = []
+        self.depth_list = []
+        self.data_ins_list = []
+        self.instance_data_length = -1
+        for ins in self.ins_list:
+            cur_rgb_path = os.path.join(self.file_path, ins, 'rgb')
+            cur_pose_path = os.path.join(self.file_path, ins, 'pose')
+
+            cur_all_fname = sorted([
+                t.split('.')[0] for t in os.listdir(cur_rgb_path)
+                if 'depth' not in t
+            ][::interval])
+            if self.instance_data_length == -1:
+                self.instance_data_length = len(cur_all_fname)
+            else:
+                assert len(cur_all_fname) == self.instance_data_length
+
+            # ! check filtered data
+            # for idx in range(len(cur_all_fname)):
+            #     fname = cur_all_fname[idx]
+            #     if not Path(os.path.join(cur_rgb_path, fname + '.png') ).exists():
+            #         cur_all_fname.remove(fname)
+
+            # del cur_all_fname[idx]
+
+            if test:
+                mid_index = len(cur_all_fname) // 3 * 2
+                cur_all_fname.insert(0, cur_all_fname[mid_index])
+
+            self.pose_list += ([
+                os.path.join(cur_pose_path, fname + '.txt')
+                for fname in cur_all_fname
+            ])
+            self.rgb_list += ([
+                os.path.join(cur_rgb_path, fname + '.png')
+                for fname in cur_all_fname
+            ])
+
+            self.depth_list += ([
+                os.path.join(cur_rgb_path, fname + '_depth0001.exr')
+                for fname in cur_all_fname
+            ])
+            self.data_ins_list += ([ins] * len(cur_all_fname))
+
+        # validate overfitting on images
+        if overfitting:
+            # bs=9
+            # self.pose_list = self.pose_list[::50//9+1]
+            # self.rgb_list = self.rgb_list[::50//9+1]
+            # self.depth_list = self.depth_list[::50//9+1]
+            # bs=6
+            # self.pose_list = self.pose_list[::50//6+1]
+            # self.rgb_list = self.rgb_list[::50//6+1]
+            # self.depth_list = self.depth_list[::50//6+1]
+            # bs=3
+            assert overfitting_bs != -1
+            # bs=1
+            # self.pose_list = self.pose_list[25:26]
+            # self.rgb_list = self.rgb_list[25:26]
+            # self.depth_list = self.depth_list[25:26]
+
+            # uniform pose sampling
+            self.pose_list = self.pose_list[::50//overfitting_bs+1]
+            self.rgb_list = self.rgb_list[::50//overfitting_bs+1]
+            self.depth_list = self.depth_list[::50//overfitting_bs+1]
+
+            # sequentially sampling pose
+            # self.pose_list = self.pose_list[25:25+overfitting_bs]
+            # self.rgb_list = self.rgb_list[25:25+overfitting_bs]
+            # self.depth_list = self.depth_list[25:25+overfitting_bs]
+
+            # duplicate the same pose
+            # self.pose_list = [self.pose_list[25]] * overfitting_bs
+            # self.rgb_list = [self.rgb_list[25]] * overfitting_bs
+            # self.depth_list = [self.depth_list[25]] * overfitting_bs
+            # self.pose_list = [self.pose_list[28]] * overfitting_bs
+            # self.rgb_list = [self.rgb_list[28]] * overfitting_bs
+            # self.depth_list = [self.depth_list[28]] * overfitting_bs
+
+        self.single_pose_list = [
+            os.path.join(cur_pose_path, fname + '.txt')
+            for fname in cur_all_fname
+        ]
+
+        # st()
+
+        # if imgnet_normalize:
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+
+        # self.normalize_normalrange = transforms.Compose([
+        #     transforms.ToTensor(),# [0,1] range
+        #     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        # ])
+
+        fx = fy = 525
+        cx = cy = 256  # rendering default K
+        factor = self.reso / (cx * 2)  # 128 / 512
+        self.fx = fx * factor
+        self.fy = fy * factor
+        self.cx = cx * factor
+        self.cy = cy * factor
+
+        # ! fix scale for triplane ray_sampler(), here we adopt [0,1] uv range, not [0, w] img space range.
+        self.cx /= self.reso  # 0.5
+        self.cy /= self.reso  # 0.5
+        self.fx /= self.reso
+        self.fy /= self.reso
+
+        intrinsics = np.array([[self.fx, 0, self.cx], [0, self.fy, self.cy],
+                               [0, 0, 1]]).reshape(9)
+        # self.intrinsics = torch.from_numpy(intrinsics).float()
+        self.intrinsics = intrinsics
+
+    def __len__(self):
+        return len(self.rgb_list)
+
+    def get_c2w(self, pose_fname):
+        with open(pose_fname, 'r') as f:
+            cam2world = f.readline().strip()
+            cam2world = [float(t) for t in cam2world.split(' ')]
+        c2w = torch.tensor(cam2world, dtype=torch.float32).reshape(4, 4)
+        return c2w
+
+    def gen_rays(self, c2w):
+        # Generate rays
+        self.h = self.reso
+        self.w = self.reso
+        yy, xx = torch.meshgrid(
+            torch.arange(self.h, dtype=torch.float32) + 0.5,
+            torch.arange(self.w, dtype=torch.float32) + 0.5,
+            indexing='ij')
+        xx = (xx - self.cx) / self.fx
+        yy = (yy - self.cy) / self.fy
+        zz = torch.ones_like(xx)
+        dirs = torch.stack((xx, yy, zz), dim=-1)  # OpenCV convention
+        dirs /= torch.norm(dirs, dim=-1, keepdim=True)
+        dirs = dirs.reshape(1, -1, 3, 1)
+        del xx, yy, zz
+        dirs = (c2w[:, None, :3, :3] @ dirs)[..., 0]
+
+        origins = c2w[:, None, :3, 3].expand(-1, self.h * self.w,
+                                             -1).contiguous()
+        origins = origins.view(-1, 3)
+        dirs = dirs.view(-1, 3)
+
+        return origins, dirs
+
+    def read_depth(self, idx):
+        depth_path = self.depth_list[idx]
+        # image_path = os.path.join(depth_fname, self.image_names[index])
+        exr = OpenEXR.InputFile(depth_path)
+        header = exr.header()
+        size = (header['dataWindow'].max.x - header['dataWindow'].min.x + 1,
+                header['dataWindow'].max.y - header['dataWindow'].min.y + 1)
+        FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
+        depth_str = exr.channel('B', FLOAT)
+        depth = np.frombuffer(depth_str,
+                              dtype=np.float32).reshape(size[1],
+                                                        size[0])  # H W
+        depth = np.nan_to_num(depth, posinf=0, neginf=0)
+        depth = depth.reshape(size)
+
+        def resize_depth_mask(depth_to_resize, resolution):
+            depth_resized = cv2.resize(depth_to_resize,
+                                       (resolution, resolution),
+                                       interpolation=cv2.INTER_LANCZOS4)
+            #    interpolation=cv2.INTER_AREA)
+            return depth_resized > 0  # type: ignore
+
+        fg_mask_reso = resize_depth_mask(depth, self.reso)
+        fg_mask_sr = resize_depth_mask(depth, 128)
+
+        # depth = cv2.resize(depth, (self.reso, self.reso),
+        #                    interpolation=cv2.INTER_LANCZOS4)
+        #    interpolation=cv2.INTER_AREA)
+        # depth_mask = depth > 0
+        # depth = np.expand_dims(depth, axis=0).reshape(size)
+        # return torch.from_numpy(depth)
+        return torch.from_numpy(depth), torch.from_numpy(
+            fg_mask_reso), torch.from_numpy(fg_mask_sr)
+
+    def load_bbox(self, mask):
+        nonzero_value = torch.nonzero(mask)
+        height, width = nonzero_value.max(dim=0)[0]
+        top, left = nonzero_value.min(dim=0)[0]
+        bbox = torch.tensor([top, left, height, width], dtype=torch.float32)
+        return bbox
+
+    def __getitem__(self, idx):
+        rgb_fname = self.rgb_list[idx]
+        pose_fname = self.pose_list[idx]
+
+        raw_img = imageio.imread(rgb_fname)
+
+        if self.preprocess is None:
+            img_to_encoder = cv2.resize(raw_img,
+                                        (self.reso_encoder, self.reso_encoder),
+                                        interpolation=cv2.INTER_LANCZOS4)
+            # interpolation=cv2.INTER_AREA)
+            img_to_encoder = img_to_encoder[
+                ..., :3]  #[3, reso_encoder, reso_encoder]
+            img_to_encoder = self.normalize(img_to_encoder)
+        else:
+            img_to_encoder = self.preprocess(Image.open(rgb_fname))  # clip
+
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+        #  interpolation=cv2.INTER_AREA)
+
+        # img_sr = cv2.resize(raw_img, (512, 512), interpolation=cv2.INTER_AREA)
+        # img_sr = cv2.resize(raw_img, (256, 256), interpolation=cv2.INTER_AREA) # just as refinement, since eg3d uses 64->128 final resolution
+        # img_sr = cv2.resize(raw_img, (128, 128), interpolation=cv2.INTER_AREA) # just as refinement, since eg3d uses 64->128 final resolution
+        img_sr = cv2.resize(
+            raw_img, (128, 128), interpolation=cv2.INTER_LANCZOS4
+        )  # just as refinement, since eg3d uses 64->128 final resolution
+
+        # img = torch.from_numpy(img)[..., :3].permute(
+        #     2, 0, 1) / 255.0  #[3, reso, reso]
+
+        img = torch.from_numpy(img)[..., :3].permute(
+            2, 0, 1
+        ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        img_sr = torch.from_numpy(img_sr)[..., :3].permute(
+            2, 0, 1
+        ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        # c2w = self.get_c2w(pose_fname).reshape(1, 4, 4) #[1, 4, 4]
+        # rays_o, rays_d = self.gen_rays(c2w)
+        # return img_to_encoder, img, rays_o, rays_d, c2w.reshape(-1)
+
+        c2w = self.get_c2w(pose_fname).reshape(16)  #[1, 4, 4] -> [1, 16]
+        # c = np.concatenate([c2w, self.intrinsics], axis=0).reshape(25)  # 25, no '1' dim needed.
+        c = torch.cat([c2w, torch.from_numpy(self.intrinsics)],
+                      dim=0).reshape(25)  # 25, no '1' dim needed.
+        ret_dict = {
+            # 'rgb_fname': rgb_fname,
+            'img_to_encoder': img_to_encoder,
+            'img': img,
+            'c': c,
+            'img_sr': img_sr,
+            # 'ins_name': self.data_ins_list[idx]
+        }
+        if self.load_depth:
+            depth, depth_mask, depth_mask_sr = self.read_depth(idx)
+            bbox = self.load_bbox(depth_mask)
+            ret_dict.update({
+                'depth': depth,
+                'depth_mask': depth_mask,
+                'depth_mask_sr': depth_mask_sr,
+                'bbox': bbox
+            })
+        # rays_o, rays_d = self.gen_rays(c2w)
+        # return img_to_encoder, img, c
+        return ret_dict
+
+
+class MultiViewDatasetforLMDB(MultiViewDataset):
+
+    def __init__(self,
+                 file_path,
+                 reso,
+                 reso_encoder,
+                 preprocess=None,
+                 classes=False,
+                 load_depth=False,
+                 test=False,
+                 scene_scale=1,
+                 overfitting=False,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 overfitting_bs=-1):
+        super().__init__(file_path, reso, reso_encoder, preprocess, classes,
+                         load_depth, test, scene_scale, overfitting,
+                         imgnet_normalize, dataset_size, overfitting_bs)
+
+    def __len__(self):
+        return super().__len__()
+        # return 100 # for speed debug
+
+    def __getitem__(self, idx):
+        # ret_dict = super().__getitem__(idx)
+        rgb_fname = self.rgb_list[idx]
+        pose_fname = self.pose_list[idx]
+        raw_img = imageio.imread(rgb_fname)[..., :3]
+
+        if raw_img.shape[-1] == 4: # ! set bg to white
+            alpha_mask = raw_img[..., -1:] / 255
+            raw_img = alpha_mask * raw_img[..., :3] + (1-alpha_mask) * np.ones_like(raw_img[..., :3]) * 255
+            raw_img = raw_img.astype(np.uint8)
+
+        raw_img = cv2.resize(raw_img,
+                                    (self.reso, self.reso),
+                                    interpolation=cv2.INTER_LANCZOS4)
+
+        c2w = self.get_c2w(pose_fname).reshape(16)  #[1, 4, 4] -> [1, 16]
+        # c = np.concatenate([c2w, self.intrinsics], axis=0).reshape(25)  # 25, no '1' dim needed.
+        c = torch.cat([c2w, torch.from_numpy(self.intrinsics)],
+                      dim=0).reshape(25)  # 25, no '1' dim needed.
+
+        depth, depth_mask, depth_mask_sr = self.read_depth(idx)
+        bbox = self.load_bbox(depth_mask)
+        ret_dict = {
+            'raw_img': raw_img,
+            'c': c,
+            'depth': depth,
+            # 'depth_mask': depth_mask, # 64x64 here?
+            'bbox': bbox
+        }
+        return ret_dict
+
+
+def load_data_dryrun(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True):
+    # st()
+    dataset = MultiViewDataset(file_path,
+                               reso,
+                               reso_encoder,
+                               test=False,
+                               preprocess=preprocess,
+                               load_depth=load_depth,
+                               imgnet_normalize=imgnet_normalize)
+    print('dataset size: {}'.format(len(dataset)))
+    # st()
+    # train_sampler = DistributedSampler(dataset=dataset)
+    loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        # shuffle=shuffle,
+        drop_last=False,
+    )
+    # sampler=train_sampler)
+
+    return loader
+
+
+class NovelViewDataset(MultiViewDataset):
+    """novel view prediction version.
+    """
+
+    def __init__(self,
+                 file_path,
+                 reso,
+                 reso_encoder,
+                 preprocess=None,
+                 classes=False,
+                 load_depth=False,
+                 test=False,
+                 scene_scale=1,
+                 overfitting=False,
+                 imgnet_normalize=True,
+                 dataset_size=-1,
+                 overfitting_bs=-1):
+        super().__init__(file_path, reso, reso_encoder, preprocess, classes,
+                         load_depth, test, scene_scale, overfitting,
+                         imgnet_normalize, dataset_size, overfitting_bs)
+
+    def __getitem__(self, idx):
+        input_view = super().__getitem__(
+            idx)  # get previous input view results
+
+        # get novel view of the same instance
+        novel_view = super().__getitem__(
+            (idx // self.instance_data_length) * self.instance_data_length +
+            random.randint(0, self.instance_data_length - 1))
+
+        # assert input_view['ins_name'] == novel_view['ins_name'], 'should sample novel view from the same instance'
+
+        input_view.update({f'nv_{k}': v for k, v in novel_view.items()})
+        return input_view
+
+
+def load_data_for_lmdb(
+        file_path="",
+        reso=64,
+        reso_encoder=224,
+        batch_size=1,
+        #   shuffle=True,
+        num_workers=6,
+        load_depth=False,
+        preprocess=None,
+        imgnet_normalize=True,
+        dataset_size=-1,
+        trainer_name='input_rec'):
+    # st()
+    # dataset_cls = {
+    #     'input_rec': MultiViewDataset,
+    #     'nv': NovelViewDataset,
+    # }[trainer_name]
+    # if 'nv' in trainer_name:
+    #     dataset_cls = NovelViewDataset
+    # else:
+    # dataset_cls = MultiViewDataset
+    dataset_cls = MultiViewDatasetforLMDB
+
+    dataset = dataset_cls(file_path,
+                          reso,
+                          reso_encoder,
+                          test=False,
+                          preprocess=preprocess,
+                          load_depth=load_depth,
+                          imgnet_normalize=imgnet_normalize,
+                          dataset_size=dataset_size)
+
+    logger.log('dataset_cls: {}, dataset size: {}'.format(
+        trainer_name, len(dataset)))
+    # train_sampler = DistributedSampler(dataset=dataset, shuffle=True, drop_last=True)
+    loader = DataLoader(
+        dataset,
+        shuffle=False,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+        prefetch_factor=2,
+        # prefetch_factor=3,
+        pin_memory=True,
+        persistent_workers=True,
+    )
+    # sampler=train_sampler)
+
+    # while True:
+    #     yield from loader
+    return loader, dataset.dataset_name, len(dataset)
+
+
+class LMDBDataset(Dataset):
+
+    def __init__(self, lmdb_path):
+        self.env = lmdb.open(
+            lmdb_path,
+            readonly=True,
+            max_readers=32,
+            lock=False,
+            readahead=False,
+            meminit=False,
+        )
+        self.num_samples = self.env.stat()['entries']
+        # self.start_idx = self.env.stat()['start_idx']
+        # self.end_idx = self.env.stat()['end_idx']
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, idx):
+        with self.env.begin(write=False) as txn:
+            key = str(idx).encode('utf-8')
+            value = txn.get(key)
+
+        sample = pickle.loads(value)
+        return sample
+
+
+def resize_depth_mask(depth_to_resize, resolution):
+    depth_resized = cv2.resize(depth_to_resize, (resolution, resolution),
+                               interpolation=cv2.INTER_LANCZOS4)
+    #    interpolation=cv2.INTER_AREA)
+    return depth_resized, depth_resized > 0  # type: ignore
+
+
+class LMDBDataset_MV(LMDBDataset):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 **kwargs):
+        super().__init__(lmdb_path)
+
+        self.reso_encoder = reso_encoder
+        self.reso = reso
+
+        transformations = [
+            transforms.ToTensor(),  # [0,1] range
+        ]
+        if imgnet_normalize:
+            transformations.append(
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225))  # type: ignore
+            )
+        else:
+            transformations.append(
+                transforms.Normalize((0.5, 0.5, 0.5),
+                                     (0.5, 0.5, 0.5)))  # type: ignore
+
+        self.normalize = transforms.Compose(transformations)
+
+    def _post_process_sample(self, raw_img, depth):
+        
+        if raw_img.shape[-1] == 4: # ! set bg to white
+            alpha_mask = raw_img[..., -1:] / 255
+            raw_img = alpha_mask * raw_img[..., :3] + (1-alpha_mask) * np.ones_like(raw_img[..., :3]) * 255
+            raw_img = raw_img.astype(np.uint8)
+
+        # if raw_img.shape[-1] == 4: # ! set bg to white
+        #     raw_img = cv2.cvtColor(raw_img, cv2.COLOR_RGBA2RGB)
+
+        # img_to_encoder = cv2.resize(sample.pop('raw_img'),
+        if raw_img.shape[0] != self.reso_encoder:
+            img_to_encoder = cv2.resize(raw_img,
+                                        (self.reso_encoder, self.reso_encoder),
+                                        interpolation=cv2.INTER_LANCZOS4)
+        else:
+            img_to_encoder = raw_img
+
+        # interpolation=cv2.INTER_AREA)
+        # img_to_encoder = img_to_encoder[..., :
+        #                                 3]  #[3, reso_encoder, reso_encoder]
+        img_to_encoder = self.normalize(img_to_encoder)
+
+        img = cv2.resize(raw_img, (self.reso, self.reso),
+                         interpolation=cv2.INTER_LANCZOS4)
+
+        # if img.shape[-1] == 4:
+        #     alpha_mask = img[..., -1:] > 0
+        #     img = alpha_mask * img[..., :3] + (1-alpha_mask) * np.ones_like(img[..., :3]) * 255
+
+        img = torch.from_numpy(img)[..., :3].permute(
+            2, 0, 1
+        ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        # img_sr = torch.from_numpy(raw_img)[..., :3].permute(
+        #     2, 0, 1
+        # ) / 127.5 - 1  #[3, reso, reso], normalize to [-1,1], follow triplane range
+
+        # depth
+        # fg_mask_reso = resize_depth_mask(sample['depth'], self.reso)
+        depth_reso, fg_mask_reso = resize_depth_mask(depth, self.reso)
+
+        return {
+            # **sample,
+            'img_to_encoder': img_to_encoder,
+            'img': img,
+            'depth_mask': fg_mask_reso,
+            # 'img_sr': img_sr, 
+            'depth': depth_reso,
+            # ! no need to load img_sr for now
+        }
+
+    def __getitem__(self, idx):
+        sample = super().__getitem__(idx)
+        # do transformations online
+
+        return self._post_process_sample(sample['raw_img'], sample['depth'])
+        # return sample
+
+def load_bytes(inp_bytes, dtype, shape):
+    return np.frombuffer(inp_bytes, dtype=dtype).reshape(shape).copy()
+
+# Function to decompress an image using gzip and open with imageio
+def decompress_and_open_image_gzip(compressed_data, is_img=False, decompress=True, decompress_fn=gzip.decompress):
+    # Decompress the image data using gzip
+    if decompress:
+        compressed_data = decompress_fn(compressed_data)
+
+    # Read the decompressed image using imageio
+    if is_img:
+        compressed_data = imageio.v3.imread(io.BytesIO(compressed_data)).copy()
+        # return image
+    return compressed_data
+
+
+# Function to decompress an array using gzip
+def decompress_array(compressed_data, shape, dtype, decompress=True, decompress_fn=gzip.decompress):
+    # Decompress the array data using gzip
+    if decompress:
+        # compressed_data = gzip.decompress(compressed_data)
+        compressed_data = decompress_fn(compressed_data)
+
+    # Convert the decompressed data to a NumPy array
+    # arr = np.frombuffer(decompressed_data, dtype=dtype).reshape(shape)
+
+    return load_bytes(compressed_data, dtype, shape)
+
+
+class LMDBDataset_MV_Compressed(LMDBDataset_MV):
+
+    def __init__(self,
+                 lmdb_path,
+                 reso,
+                 reso_encoder,
+                 imgnet_normalize=True,
+                 **kwargs):
+        super().__init__(lmdb_path, reso, reso_encoder, imgnet_normalize,
+                         **kwargs)
+        with self.env.begin(write=False) as txn:
+            self.length = int(
+                txn.get('length'.encode('utf-8')).decode('utf-8')) - 40
+
+        self.load_image_fn = partial(decompress_and_open_image_gzip,
+                                     is_img=True)
+
+    def __len__(self):
+        return self.length
+    
+    def _load_lmdb_data(self, idx):
+
+        with self.env.begin(write=False) as txn:
+            raw_img_key = f'{idx}-raw_img'.encode('utf-8')
+            raw_img = self.load_image_fn(txn.get(raw_img_key))
+
+            depth_key = f'{idx}-depth'.encode('utf-8')
+            depth = decompress_array(txn.get(depth_key), (512,512), np.float32)
+
+            c_key = f'{idx}-c'.encode('utf-8')
+            c = decompress_array(txn.get(c_key), (25, ), np.float32)
+
+            bbox_key = f'{idx}-bbox'.encode('utf-8')
+            bbox = decompress_array(txn.get(bbox_key), (4, ), np.float32)
+
+        return raw_img, depth, c, bbox
+
+    def _load_lmdb_data_no_decompress(self, idx):
+
+        with self.env.begin(write=False) as txn:
+            raw_img_key = f'{idx}-raw_img'.encode('utf-8')
+            # raw_img = txn.get(raw_img_key)
+            raw_img = self.load_image_fn(txn.get(raw_img_key), decompress=False)
+
+            depth_key = f'{idx}-depth'.encode('utf-8')
+            depth = decompress_array(txn.get(depth_key), (512,512), np.float32, decompress=False)
+            # depth = txn.get(depth_key), (512,512)
+
+            # c_key = f'{idx}-c'.encode('utf-8')
+            # c = txn.get(c_key), (25, ), np.float32
+
+            # bbox_key = f'{idx}-bbox'.encode('utf-8')
+            # bbox = txn.get(bbox_key)
+
+            c_key = f'{idx}-c'.encode('utf-8')
+            c = decompress_array(txn.get(c_key), (25, ), np.float32, decompress=False)
+
+            bbox_key = f'{idx}-bbox'.encode('utf-8')
+            bbox = decompress_array(txn.get(bbox_key), (4, ), np.float32, decompress=False)
+
+        return raw_img, depth, c, bbox
+
+    def __getitem__(self, idx):
+        # sample = super(LMDBDataset).__getitem__(idx)
+
+        # do gzip uncompress online
+        '''
+        raw_img, depth, c, bbox  = self._load_lmdb_data(idx)
+
+        return {
+            **self._post_process_sample(raw_img, depth), 'c': c,
+            'bbox': bbox*(self.reso/64.0),
+            # 'depth': depth,
+        }
+        '''
+        raw_img, depth, c, bbox  = self._load_lmdb_data_no_decompress(idx)
+        return None
+
+
+class LMDBDataset_NV_Compressed(LMDBDataset_MV_Compressed):
+    def __init__(self, lmdb_path, reso, reso_encoder, imgnet_normalize=True, **kwargs):
+        super().__init__(lmdb_path, reso, reso_encoder, imgnet_normalize, **kwargs)
+        self.instance_data_length = 50 # 
+
+    def __getitem__(self, idx):
+        input_view = super().__getitem__(
+            idx)  # get previous input view results
+
+        # get novel view of the same instance
+        try:
+            novel_view = super().__getitem__(
+                (idx // self.instance_data_length) * self.instance_data_length +
+                random.randint(0, self.instance_data_length - 1))
+        except Exception as e:
+            raise NotImplementedError(idx)
+
+        assert input_view['ins_name'] == novel_view['ins_name'], 'should sample novel view from the same instance'
+
+        input_view.update({f'nv_{k}': v for k, v in novel_view.items()})
+        return input_view
\ No newline at end of file
diff --git a/datasets/text_captions_cap3d.json b/datasets/text_captions_cap3d.json
new file mode 120000
index 0000000000000000000000000000000000000000..221de3ead4ff1bcccb023c53eb985dcbb3143637
--- /dev/null
+++ b/datasets/text_captions_cap3d.json
@@ -0,0 +1 @@
+/home/yslan/Repo/open-source/LN3Diff/datasets/text_captions_cap3d.json
\ No newline at end of file
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diff --git a/dit/dit_3d.py b/dit/dit_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..324099b19f4f83dcfadafd06b5faf20f716252cf
--- /dev/null
+++ b/dit/dit_3d.py
@@ -0,0 +1,212 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+
+from pdb import set_trace as st
+
+from .dit_models import DiT, DiTBlock, DiT_models, get_2d_sincos_pos_embed
+
+
+class DiT_Triplane_V1(DiT):
+    """
+    1. merge the 3*H*W as L, and 8 as C only
+    2. pachify, flat into 224*(224*3) with 8 channels for pachify
+    3. unpachify accordingly
+    """
+
+    def __init__(self,
+                 input_size=32,
+                 patch_size=2,
+                 in_channels=4,
+                 hidden_size=1152,
+                 depth=28,
+                 num_heads=16,
+                 mlp_ratio=4,
+                 class_dropout_prob=0.1,
+                 num_classes=1000,
+                 learn_sigma=False):
+
+        input_size = (input_size, input_size*3)
+        super().__init__(input_size, patch_size, in_channels//3, hidden_size, # type: ignore
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma)
+    
+    def initialize_weights(self):
+        """all the same except the PE part
+        """
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        pos_embed = get_2d_sincos_pos_embed(
+            self.pos_embed.shape[-1], self.x_embedder.grid_size)
+        # st()
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        # ! untouched below
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        # Initialize label embedding table:
+        if self.y_embedder is not None:
+            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x):
+        # TODO
+        """
+        x: (N, L, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0] # type: ignore
+        h = w = int((x.shape[1]//3)**0.5)
+        assert h * w * 3 == x.shape[1] # merge triplane 3 dims with hw
+
+        x = x.reshape(shape=(x.shape[0], h, w, 3, p, p, c))
+        x = torch.einsum('nhwzpqc->nczhpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c*3, h * p, h * p)) # type: ignore
+        return imgs # B 8*3 H W
+
+    def forward(self, x, t, y=None):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+
+        # ! merge tri-channel into w chanenl for 3D-aware TX
+        x = x.reshape(x.shape[0], -1, 3, x.shape[2], x.shape[3]) # B 8 3 H W
+        x = x.permute(0,1,3,4,2).reshape(x.shape[0], -1, x.shape[-2], x.shape[-1]*3) # B 8 H W83
+
+        x = self.x_embedder(
+            x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(t)  # (N, D)
+
+        if self.y_embedder is not None:
+            assert y is not None
+            y = self.y_embedder(y, self.training)  # (N, D)
+            c = t + y  # (N, D)
+        else:
+            c = t
+
+        for block in self.blocks:
+            x = block(x, c)  # (N, T, D)
+
+        x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+        
+        return x
+
+
+
+
+class DiT_Triplane_V1_learnedPE(DiT_Triplane_V1):
+    """
+    1. learned PE, default cos/sin wave
+    """
+
+    def __init__(self,
+                 input_size=32,
+                 patch_size=2,
+                 in_channels=4,
+                 hidden_size=1152,
+                 depth=28,
+                 num_heads=16,
+                 mlp_ratio=4,
+                 class_dropout_prob=0.1,
+                 num_classes=1000,
+                 learn_sigma=True):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma)
+
+
+class DiT_Triplane_V1_fixed3DPE(DiT_Triplane_V1):
+    """
+    1. 3D aware PE, fixed
+    """
+
+    def __init__(self,
+                 input_size=32,
+                 patch_size=2,
+                 in_channels=4,
+                 hidden_size=1152,
+                 depth=28,
+                 num_heads=16,
+                 mlp_ratio=4,
+                 class_dropout_prob=0.1,
+                 num_classes=1000,
+                 learn_sigma=True):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma)
+
+
+class DiT_Triplane_V1_learned3DPE(DiT_Triplane_V1):
+    """
+    1. init with 3D aware PE, learnable
+    """
+
+    def __init__(self,
+                 input_size=32,
+                 patch_size=2,
+                 in_channels=4,
+                 hidden_size=1152,
+                 depth=28,
+                 num_heads=16,
+                 mlp_ratio=4,
+                 class_dropout_prob=0.1,
+                 num_classes=1000,
+                 learn_sigma=True):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma)
+
+def V1_Triplane_DiT_S_2(**kwargs):
+    return DiT_Triplane_V1(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
+
+def V1_Triplane_DiT_S_4(**kwargs):
+    return DiT_Triplane_V1(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
+
+def V1_Triplane_DiT_S_8(**kwargs):
+    return DiT_Triplane_V1(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
+
+def V1_Triplane_DiT_B_8(**kwargs):
+    return DiT_Triplane_V1(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
+
+def V1_Triplane_DiT_B_16(**kwargs): # ours cfg
+    return DiT_Triplane_V1(depth=12, hidden_size=768, patch_size=16, num_heads=12, **kwargs)
+
+DiT_models.update({
+    'v1-T-DiT-S/2': V1_Triplane_DiT_S_2,
+    'v1-T-DiT-S/4': V1_Triplane_DiT_S_4,
+    'v1-T-DiT-S/8': V1_Triplane_DiT_S_8,
+    'v1-T-DiT-B/8': V1_Triplane_DiT_B_8,
+    'v1-T-DiT-B/16': V1_Triplane_DiT_B_16,
+})
\ No newline at end of file
diff --git a/dit/dit_decoder.py b/dit/dit_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1179bb93cbb572c8007b818040aec1d238407d0b
--- /dev/null
+++ b/dit/dit_decoder.py
@@ -0,0 +1,345 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+
+from einops import rearrange
+from pdb import set_trace as st
+
+# from .dit_models import DiT, DiTBlock, DiT_models, get_2d_sincos_pos_embed, modulate, FinalLayer
+
+from .dit_models_xformers import DiT, DiTBlock, DiT_models, get_2d_sincos_pos_embed, modulate, FinalLayer
+# from .dit_models import DiT, DiTBlock, DiT_models, get_2d_sincos_pos_embed, modulate, FinalLayer
+
+
+def modulate2(x, shift, scale):
+    return x * (1 + scale) + shift
+
+
+class DiTBlock2(DiTBlock):
+    """
+    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4, **block_kwargs):
+        super().__init__(hidden_size, num_heads, mlp_ratio, **block_kwargs)
+
+    def forward(self, x, c):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            c).chunk(6, dim=-1)
+        # st()
+        x = x + gate_msa * self.attn(
+            modulate2(self.norm1(x), shift_msa, scale_msa))
+        x = x + gate_mlp * self.mlp(
+            modulate2(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+
+
+class FinalLayer2(FinalLayer):
+    """
+    The final layer of DiT, basically the decoder_pred in MAE with adaLN.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__(hidden_size, patch_size, out_channels)
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
+        x = modulate2(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class DiT2(DiT):
+    # a conditional ViT
+    def __init__(self,
+                 input_size=32,
+                 patch_size=2,
+                 in_channels=4,
+                 hidden_size=1152,
+                 depth=28,
+                 num_heads=16,
+                 mlp_ratio=4,
+                 class_dropout_prob=0.1,
+                 num_classes=1000,
+                 learn_sigma=True,
+                 mixing_logit_init=-3,
+                 mixed_prediction=True,
+                 context_dim=False,
+                 roll_out=False,
+                 plane_n=3,
+                 return_all_layers=False,
+                 in_plane_attention=True,
+                 vit_blk=...):
+        super().__init__(input_size,
+                         patch_size,
+                         in_channels,
+                         hidden_size,
+                         depth,
+                         num_heads,
+                         mlp_ratio,
+                         class_dropout_prob,
+                         num_classes,
+                         learn_sigma,
+                         mixing_logit_init,
+                         mixed_prediction,
+                         context_dim,
+                         roll_out,
+                         vit_blk=DiTBlock2,
+                         final_layer_blk=FinalLayer2)
+        # st()
+
+        # no t and x embedder
+        del self.x_embedder
+        del self.t_embedder
+        del self.final_layer
+        torch.cuda.empty_cache()
+        self.clip_text_proj = None
+        self.plane_n = plane_n
+        self.return_all_layers = return_all_layers
+        self.in_plane_attention = in_plane_attention
+
+    def forward(self, c, *args, **kwargs):
+        # return super().forward(x, timesteps, context, y, get_attr, **kwargs)
+        """
+        Forward pass of DiT.
+        c: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        """
+
+        x = self.pos_embed.repeat(
+            c.shape[0], 1, 1).to(c.dtype)  # (N, T, D), where T = H * W / patch_size ** 2
+
+        if self.return_all_layers:
+            all_layers = []
+
+        # if context is not None:
+        # c = context # B 3HW C
+
+        for blk_idx, block in enumerate(self.blocks):
+            if self.roll_out:
+                if self.in_plane_attention: # plane-wise output
+                    if blk_idx % 2 == 0:  # with-in plane self attention
+                        x = rearrange(x, 'b (n l) c -> (b n) l c ', n=self.plane_n)
+                        x = block(x,
+                                rearrange(c,
+                                            'b (n l) c -> (b n) l c ',
+                                            n=self.plane_n))  # (N, T, D)
+                        # st()
+                        if self.return_all_layers:
+
+                            all_layers.append(
+                                rearrange(x,
+                                        '(b n) l c -> b (n l) c',
+                                        n=self.plane_n))
+
+                            # all_layers.append(x)
+                    else:  # global attention
+                        x = rearrange(x, '(b n) l c -> b (n l) c ', n=self.plane_n)
+                        x = block(x, c)  # (N, T, D)
+                        # st()
+                        if self.return_all_layers:
+                            #  all merged into B dim
+                            all_layers.append(x)
+                            # all_layers.append(
+                            #     rearrange(x,
+                            #               'b (n l) c -> (b n) l c',
+                            #               n=self.plane_n))
+                else:
+
+                    # ! already b (n l) c
+                    # if blk_idx == 0: # rearrange once
+                    #     x = rearrange(x, '(b n) l c -> b (n l) c ', n=self.plane_n)
+                    x = block(x, c)  # (N, T, D)
+                    if self.return_all_layers:
+                        #  all merged into B dim
+                        all_layers.append(x)
+
+            else:
+                x = block(x, c)  # (N, T, D)
+
+        # x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
+
+        # if self.roll_out:  # move n from L to B axis
+        # x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        # x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        # if self.roll_out:  # move n from L to B axis
+        #     x = rearrange(x, '(b n) c h w -> b (n c) h w', n=3)
+        # st()
+
+        if self.return_all_layers:
+            return all_layers
+        else:
+            # return x.to(torch.float32)
+            return x
+
+
+# class DiT2_DPT(DiT2):
+#     def __init__(self, input_size=32, patch_size=2, in_channels=4, hidden_size=1152, depth=28, num_heads=16, mlp_ratio=4, class_dropout_prob=0.1, num_classes=1000, learn_sigma=True, mixing_logit_init=-3, mixed_prediction=True, context_dim=False, roll_out=False, plane_n=3, vit_blk=...):
+#         super().__init__(input_size, patch_size, in_channels, hidden_size, depth, num_heads, mlp_ratio, class_dropout_prob, num_classes, learn_sigma, mixing_logit_init, mixed_prediction, context_dim, roll_out, plane_n, vit_blk)
+#         self.return_all_layers = True
+
+#################################################################################
+#                                   DiT2 Configs                                  #
+#################################################################################
+
+
+def DiT2_XL_2(**kwargs):
+    return DiT2(depth=28,
+                hidden_size=1152,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_XL_2_half(**kwargs):
+    return DiT2(depth=28 // 2,
+                hidden_size=1152,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_XL_4(**kwargs):
+    return DiT2(depth=28,
+                hidden_size=1152,
+                patch_size=4,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_XL_8(**kwargs):
+    return DiT2(depth=28,
+                hidden_size=1152,
+                patch_size=8,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_L_2(**kwargs):
+    return DiT2(depth=24,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_L_2_stage1(**kwargs):
+    return DiT2(depth=24-6,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+def DiT2_L_2_stage1(**kwargs):
+    return DiT2(depth=24-6,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+def DiT2_L_2_half(**kwargs):
+    return DiT2(depth=24//2,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+def DiT2_L_2_half_ninelayer(**kwargs):
+    return DiT2(depth=9,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+
+def DiT2_L_4(**kwargs):
+    return DiT2(depth=24,
+                hidden_size=1024,
+                patch_size=4,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_L_8(**kwargs):
+    return DiT2(depth=24,
+                hidden_size=1024,
+                patch_size=8,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT2_B_2(**kwargs):
+    return DiT2(depth=12,
+                hidden_size=768,
+                patch_size=2,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT2_B_2_stage1(**kwargs):
+    return DiT2(depth=12, # ! just 12, stage-2 3 layers afterwards.
+                hidden_size=768,
+                patch_size=2,
+                num_heads=12,
+                **kwargs)
+
+def DiT2_B_4(**kwargs):
+    return DiT2(depth=12,
+                hidden_size=768,
+                patch_size=4,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT2_B_8(**kwargs):
+    return DiT2(depth=12,
+                hidden_size=768,
+                patch_size=8,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT2_B_16(**kwargs):  # ours cfg
+    return DiT2(depth=12,
+                hidden_size=768,
+                patch_size=16,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT2_S_2(**kwargs):
+    return DiT2(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
+
+
+def DiT2_S_4(**kwargs):
+    return DiT2(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
+
+
+def DiT2_S_8(**kwargs):
+    return DiT2(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
+
+
+DiT2_models = {
+    'DiT2-XL/2': DiT2_XL_2,
+    'DiT2-XL/2/half': DiT2_XL_2_half,
+    'DiT2-XL/4': DiT2_XL_4,
+    'DiT2-XL/8': DiT2_XL_8,
+    'DiT2-L/2': DiT2_L_2,
+    'DiT2-L/2/S1': DiT2_L_2_stage1,
+    'DiT2-L/2/S1-v2': DiT2_L_2_stage1,
+    'DiT2-B/2/S1': DiT2_B_2_stage1,
+    'DiT2-L/4': DiT2_L_4,
+    'DiT2-L/2-half': DiT2_L_2_half,
+    'DiT2-L/2-ninelayer': DiT2_L_2_half_ninelayer,
+    'DiT2-L/8': DiT2_L_8,
+    'DiT2-B/2': DiT2_B_2,
+    'DiT2-B/4': DiT2_B_4,
+    'DiT2-B/8': DiT2_B_8,
+    'DiT2-B/16': DiT2_B_16,
+    'DiT2-S/2': DiT2_S_2,
+    'DiT2-S/4': DiT2_S_4,
+    'DiT2-S/8': DiT2_S_8,
+}
diff --git a/dit/dit_decoder_3d.py b/dit/dit_decoder_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ce511098c4e0d098ba5b18190b161af6fae0ad2
--- /dev/null
+++ b/dit/dit_decoder_3d.py
@@ -0,0 +1,172 @@
+import torch.nn as nn
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from pdb import set_trace as st
+
+from ldm.modules.attention import MemoryEfficientCrossAttention
+from .dit_decoder import DiT2
+
+class DiT3D(DiT2):
+    def __init__(self, input_size=32, patch_size=2, in_channels=4, hidden_size=1152, depth=28, num_heads=16, mlp_ratio=4, class_dropout_prob=0.1, num_classes=1000, learn_sigma=True, mixing_logit_init=-3, mixed_prediction=True, context_dim=False, roll_out=False, plane_n=3, return_all_layers=False, in_plane_attention=True, vit_blk=...):
+        super().__init__(input_size, patch_size, in_channels, hidden_size, depth, num_heads, mlp_ratio, class_dropout_prob, num_classes, learn_sigma, mixing_logit_init, mixed_prediction, context_dim, roll_out, plane_n, return_all_layers, in_plane_attention, vit_blk)
+        # follow point infinity, add "write" CA block per 6 blocks
+
+        # 25/4/2024, cascade a "read&write" block after the DiT base model.
+        self.read_ca = MemoryEfficientCrossAttention(hidden_size, context_dim)
+        self.point_infinity_blocks = nn.ModuleList([
+            vit_blk(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+            for _ in range(2)
+        ])
+    
+    def initialize_weights(self):
+        super().initialize_weights()
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        # ! no final layer anymore
+        for block in self.point_infinity_blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+                                                      
+        
+    
+    def forward(self, c, *args, **kwargs):
+        x_base = super().forward(c, *args, **kwargs) # base latent
+        # add read&write block
+
+
+
+#################################################################################
+#                                   DiT3D Configs                                  #
+#################################################################################
+
+
+def DiT3DXL_2(**kwargs):
+    return DiT3D(depth=28,
+                hidden_size=1152,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DXL_2_half(**kwargs):
+    return DiT3D(depth=28 // 2,
+                hidden_size=1152,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DXL_4(**kwargs):
+    return DiT3D(depth=28,
+                hidden_size=1152,
+                patch_size=4,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DXL_8(**kwargs):
+    return DiT3D(depth=28,
+                hidden_size=1152,
+                patch_size=8,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DL_2(**kwargs):
+    return DiT3D(depth=24,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DL_2_half(**kwargs):
+    return DiT3D(depth=24 // 2,
+                hidden_size=1024,
+                patch_size=2,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DL_4(**kwargs):
+    return DiT3D(depth=24,
+                hidden_size=1024,
+                patch_size=4,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DL_8(**kwargs):
+    return DiT3D(depth=24,
+                hidden_size=1024,
+                patch_size=8,
+                num_heads=16,
+                **kwargs)
+
+
+def DiT3DB_2(**kwargs):
+    return DiT3D(depth=12,
+                hidden_size=768,
+                patch_size=2,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT3DB_4(**kwargs):
+    return DiT3D(depth=12,
+                hidden_size=768,
+                patch_size=4,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT3DB_8(**kwargs):
+    return DiT3D(depth=12,
+                hidden_size=768,
+                patch_size=8,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT3DB_16(**kwargs):  # ours cfg
+    return DiT3D(depth=12,
+                hidden_size=768,
+                patch_size=16,
+                num_heads=12,
+                **kwargs)
+
+
+def DiT3DS_2(**kwargs):
+    return DiT3D(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
+
+
+def DiT3DS_4(**kwargs):
+    return DiT3D(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
+
+
+def DiT3DS_8(**kwargs):
+    return DiT3D(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
+
+
+DiT3Dmodels = {
+    'DiT3D-XL/2': DiT3DXL_2,
+    'DiT3D-XL/2/half': DiT3DXL_2_half,
+    'DiT3D-XL/4': DiT3DXL_4,
+    'DiT3D-XL/8': DiT3DXL_8,
+    'DiT3D-L/2': DiT3DL_2,
+    'DiT3D-L/2/half': DiT3DL_2_half,
+    'DiT3D-L/4': DiT3DL_4,
+    'DiT3D-L/8': DiT3DL_8,
+    'DiT3D-B/2': DiT3DB_2,
+    'DiT3D-B/4': DiT3DB_4,
+    'DiT3D-B/8': DiT3DB_8,
+    'DiT3D-B/16': DiT3DB_16,
+    'DiT3D-S/2': DiT3DS_2,
+    'DiT3D-S/4': DiT3DS_4,
+    'DiT3D-S/8': DiT3DS_8,
+}
+
diff --git a/dit/dit_i23d.py b/dit/dit_i23d.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee3b62ab2d38952f9d33b39b5e21a0e9ba7a378f
--- /dev/null
+++ b/dit/dit_i23d.py
@@ -0,0 +1,1697 @@
+import torch.nn as nn
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from pdb import set_trace as st
+
+from ldm.modules.attention import MemoryEfficientCrossAttention
+from .dit_models_xformers import *
+# from apex.normalization import FusedLayerNorm as LayerNorm
+from torch.nn import LayerNorm
+# from apex.normalization import FusedRMSNorm as RMSNorm
+
+try:
+    from apex.normalization import FusedRMSNorm as RMSNorm
+except:
+    from dit.norm import RMSNorm
+
+from timm.models.vision_transformer import Mlp
+
+from vit.vit_triplane import XYZPosEmbed
+
+from .dit_trilatent import DiT_PCD_PixelArt
+
+
+class DiT_I23D(DiT):
+    # DiT with 3D_aware operations
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlock,
+        final_layer_blk=T2IFinalLayer,
+        enable_rope=False,
+    ):
+        # st()
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim, roll_out, vit_blk,
+                         T2IFinalLayer, enable_rope=enable_rope)
+
+        assert self.roll_out
+
+        # if context_dim is not None:
+        # self.dino_proj = CaptionEmbedder(context_dim,
+        self.clip_ctx_dim = 1024 # vit-l
+        # self.dino_proj = CaptionEmbedder(self.clip_ctx_dim, # ! dino-vitl/14 here, for img-cond
+        self.dino_proj = CaptionEmbedder(context_dim, # ! dino-vitb/14 here, for MV-cond. hard coded for now...
+        # self.dino_proj = CaptionEmbedder(1024, # ! dino-vitb/14 here, for MV-cond. hard coded for now...
+                                                hidden_size,
+                                                act_layer=approx_gelu)
+
+        self.clip_spatial_proj = CaptionEmbedder(1024, # clip_I-L
+                                                hidden_size,
+                                                act_layer=approx_gelu)
+
+    def init_PE_3D_aware(self):
+
+        self.pos_embed = nn.Parameter(torch.zeros(
+            1, self.plane_n * self.x_embedder.num_patches, self.embed_dim),
+                                      requires_grad=False)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        p = int(self.x_embedder.num_patches**0.5)
+        D = self.pos_embed.shape[-1]
+        grid_size = (self.plane_n, p * p)  # B n HW C
+
+        pos_embed = get_2d_sincos_pos_embed(D, grid_size).reshape(
+            self.plane_n * p * p, D)  # H*W, D
+
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+    def initialize_weights(self):
+        super().initialize_weights()
+
+        # ! add 3d-aware PE
+        self.init_PE_3D_aware()
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+        # context = self.clip_text_proj(context)
+        clip_cls_token = self.clip_text_proj(context['vector'])
+        clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        # ! todo, return spatial clip features.
+
+        # if self.roll_out:  # !
+        x = rearrange(x, 'b (c n) h w->(b n) c h w',
+                      n=3)  # downsample with same conv
+        x = self.x_embedder(x)  # (b n) c h/f w/f
+
+        x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
+        x = x + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        # if self.roll_out:  # ! roll-out in the L dim, not B dim. add condition to all tokens.
+        # x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
+
+        # assert context.ndim == 2
+        # if isinstance(context, dict):
+        #     context = context['crossattn']  # sgm conditioner compat
+
+
+        # c = t + context
+        # else:
+        # c = t  # BS 1024
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+            # x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+    # ! compat issue
+    def forward_with_cfg(self, x, t, context, cfg_scale):
+        """
+        Forward pass of SiT, but also batches the unconSiTional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        # half = x[: len(x) // 2]
+        # combined = torch.cat([half, half], dim=0)
+        eps = self.forward(x, t, context)
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        # eps, rest = model_out[:, :3], model_out[:, 3:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0)
+        return eps
+
+
+
+
+class DiT_I23D_PixelArt(DiT_I23D):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArtRMSNorm,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=True,
+        enable_rope=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim, pooling_ctx_dim, roll_out, vit_blk,
+                         final_layer_blk, 
+                         enable_rope=enable_rope)
+
+        # ! a shared one
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+
+        # ! single
+        nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
+
+        del self.clip_text_proj
+        if create_cap_embedder:
+            self.cap_embedder = nn.Sequential( # TODO, init with zero here.
+                LayerNorm(pooling_ctx_dim),
+                nn.Linear(
+                    pooling_ctx_dim,
+                    hidden_size,
+                ),
+            )
+
+            nn.init.constant_(self.cap_embedder[-1].weight, 0)
+            nn.init.constant_(self.cap_embedder[-1].bias, 0)
+        else:
+            self.cap_embedder = nn.Identity() # placeholder
+
+        print(self) # check model arch
+
+        self.attention_y_norm = RMSNorm(
+            1024, eps=1e-5
+        )  # https://github.com/Alpha-VLLM/Lumina-T2X/blob/0c8dd6a07a3b7c18da3d91f37b1e00e7ae661293/lumina_t2i/models/model.py#L570C9-L570C61
+
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+        # context = self.clip_text_proj(context)
+        clip_cls_token = self.cap_embedder(context['vector'])
+        clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+        clip_spatial_token = self.attention_y_norm(clip_spatial_token) # avoid re-normalization in each blk
+
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        # if self.roll_out:  # !
+        x = rearrange(x, 'b (c n) h w->(b n) c h w',
+                      n=3)  # downsample with same conv
+        x = self.x_embedder(x)  # (b n) c h/f w/f
+
+        x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
+        x = x + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        # if self.roll_out:  # ! roll-out in the L dim, not B dim. add condition to all tokens.
+        # x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
+
+        # assert context.ndim == 2
+        # if isinstance(context, dict):
+        #     context = context['crossattn']  # sgm conditioner compat
+
+
+        # c = t + context
+        # else:
+        # c = t  # BS 1024
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+            # x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+class DiT_I23D_PCD_PixelArt(DiT_I23D_PixelArt):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArtRMSNorm,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=True,
+        use_clay_ca=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim, pooling_ctx_dim, roll_out, vit_blk,
+                         final_layer_blk)
+
+        self.x_embedder = Mlp(in_features=in_channels,
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+        del self.pos_embed
+        self.use_clay_ca = use_clay_ca
+        if use_clay_ca:
+            del self.dino_proj # no prepending required.
+
+        # add ln_pred and ln_post, as in point-e. (does not help, worse performance)
+        # self.ln_pre = LayerNorm(hidden_size)
+        # self.ln_post = LayerNorm(hidden_size)
+    @staticmethod
+    def precompute_freqs_cis(
+        dim: int,
+        end: int,
+        theta: float = 10000.0,
+        rope_scaling_factor: float = 1.0,
+        ntk_factor: float = 1.0,
+    ):
+        """
+        Precompute the frequency tensor for complex exponentials (cis) with
+        given dimensions.
+
+        This function calculates a frequency tensor with complex exponentials
+        using the given dimension 'dim' and the end index 'end'. The 'theta'
+        parameter scales the frequencies. The returned tensor contains complex
+        values in complex64 data type.
+
+        Args:
+            dim (int): Dimension of the frequency tensor.
+            end (int): End index for precomputing frequencies.
+            theta (float, optional): Scaling factor for frequency computation.
+                Defaults to 10000.0.
+
+        Returns:
+            torch.Tensor: Precomputed frequency tensor with complex
+                exponentials.
+        """
+
+        theta = theta * ntk_factor
+
+        print(f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}")
+        freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float().cuda() / dim))
+        t = torch.arange(end, device=freqs.device, dtype=torch.float)  # type: ignore
+        t = t / rope_scaling_factor
+        freqs = torch.outer(t, freqs).float()  # type: ignore
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+        return freqs_cis
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        # global condition
+        if 'caption_vector' in context:
+            clip_cls_token = self.cap_embedder(context['caption_vector'])
+        elif 'img_vector' in context:
+            clip_cls_token = self.cap_embedder(context['img_vector'])
+        else:
+            clip_cls_token = 0
+
+        # spatial condition
+        clip_spatial_token, dino_spatial_token = context['img_crossattn'][..., :self.clip_ctx_dim], context['img_crossattn'][..., self.clip_ctx_dim:]
+        if not self.use_clay_ca:
+            dino_spatial_token=self.dino_proj(dino_spatial_token)
+
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token, clip_caption_token=context.get('caption_crossattn'))
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+# dino only version
+class DiT_I23D_PCD_PixelArt_noclip(DiT_I23D_PixelArt):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArtRMSNormNoClip,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=True,
+        use_clay_ca=False,
+        has_caption=False,
+        # has_rope=False,
+        rope_scaling_factor: float = 1.0,
+        ntk_factor: float = 1.0,
+        enable_rope=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim, pooling_ctx_dim, roll_out, vit_blk,
+                         final_layer_blk, enable_rope=enable_rope)
+
+        self.x_embedder = Mlp(in_features=in_channels,
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+        del self.pos_embed
+        del self.dino_proj
+
+        self.enable_rope = enable_rope
+        if self.enable_rope: # implementation copied from Lumina-T2X code base
+            self.freqs_cis = DiT_I23D_PCD_PixelArt.precompute_freqs_cis(
+            hidden_size // num_heads,
+            40000,
+            rope_scaling_factor=rope_scaling_factor,
+            ntk_factor=ntk_factor,
+        )
+        else:
+            self.freqs_cis = None
+
+        self.rope_scaling_factor = rope_scaling_factor
+        self.ntk_factor = ntk_factor
+
+        self.use_clay_ca = use_clay_ca
+
+        self.has_caption = has_caption
+        pooled_vector_dim = context_dim
+        if has_caption:
+            pooled_vector_dim += 768
+
+        self.pooled_vec_embedder = nn.Sequential( # TODO, init with zero here.
+            LayerNorm(pooled_vector_dim),
+            nn.Linear(
+                pooled_vector_dim,
+                hidden_size,
+            ),
+        )
+        nn.init.constant_(self.pooled_vec_embedder[-1].weight, 0)
+        nn.init.constant_(self.pooled_vec_embedder[-1].bias, 0)
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        # clip_cls_token = self.cap_embedder(context['vector'])
+        # clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+        # dino_spatial_token = context['crossattn']
+        dino_spatial_token = context['img_crossattn']
+        dino_pooled_vector = context['img_vector']
+        if self.has_caption:
+            clip_caption_token = context.get('caption_crossattn')
+            pooled_vector = torch.cat([dino_pooled_vector, context.get('caption_vector')], -1) # concat dino_vector
+        else:
+            clip_caption_token = None
+            pooled_vector = dino_pooled_vector
+        
+
+        t = self.t_embedder(timesteps) + self.pooled_vec_embedder(pooled_vector)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        freqs_cis = None
+        if self.enable_rope:
+            freqs_cis=self.freqs_cis[: x.size(1)]
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_caption_token=clip_caption_token, freqs_cis=freqs_cis)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+# xyz-diff
+
+
+# xyz-cond tex diff
+class DiT_I23D_PCD_PixelArt_xyz_cond_kl_diff(DiT_I23D_PCD_PixelArt):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArtRMSNorm,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=True,
+        use_pe_cond=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim, pooling_ctx_dim, roll_out, vit_blk,
+                         final_layer_blk)
+
+        self.use_pe_cond = use_pe_cond
+        self.x_embedder = Mlp(in_features=in_channels+3*(1-use_pe_cond),
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+
+        if use_pe_cond:
+            self.xyz_pos_embed = XYZPosEmbed(hidden_size)
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        clip_cls_token = self.cap_embedder(context['vector'])
+        clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+
+        fps_xyz = context['fps-xyz']
+
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        if self.use_pe_cond:
+            x = self.x_embedder(x) + self.xyz_pos_embed(fps_xyz) # point-wise addition
+        else: # use concat to add info
+            x = torch.cat([fps_xyz, x], dim=-1)
+            x = self.x_embedder(x)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        x = self.final_layer(x, t) # no loss on the xyz side 
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+# xyz-cond tex diff, but clay
+class DiT_I23D_PCD_PixelArt_noclip_clay_stage2(DiT_I23D_PCD_PixelArt_noclip):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArtRMSNorm,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=True,
+        use_pe_cond=False,
+        has_caption=False,
+        use_clay_ca=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim, pooling_ctx_dim, roll_out, vit_blk,
+                         final_layer_blk, use_clay_ca=use_clay_ca, has_caption=has_caption)
+
+        self.has_caption = False
+        self.use_pe_cond = use_pe_cond
+        self.x_embedder = Mlp(in_features=in_channels+3*(1-use_pe_cond),
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+
+        if use_pe_cond:
+            self.xyz_pos_embed = XYZPosEmbed(hidden_size)
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        dino_spatial_token = context['img_crossattn']
+        dino_pooled_vector = context['img_vector']
+        if self.has_caption:
+            clip_caption_token = context.get('caption_crossattn')
+            pooled_vector = torch.cat([dino_pooled_vector, context.get('caption_vector')], -1) # concat dino_vector
+        else:
+            clip_caption_token = None
+            pooled_vector = dino_pooled_vector
+        
+        t = self.t_embedder(timesteps) + self.pooled_vec_embedder(pooled_vector)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        fps_xyz = context['fps-xyz']
+        if self.use_pe_cond:
+            x = self.x_embedder(x) + self.xyz_pos_embed(fps_xyz) # point-wise addition
+        else: # use concat to add info
+            x = torch.cat([fps_xyz, x], dim=-1)
+            x = self.x_embedder(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_caption_token=clip_caption_token)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+class DiT_I23D_PixelArt_MVCond(DiT_I23D_PixelArt):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim,
+                         pooling_ctx_dim, roll_out, ImageCondDiTBlockPixelArtRMSNorm,
+                         final_layer_blk, create_cap_embedder=create_cap_embedder)
+
+
+        # support multi-view img condition
+        # DINO handles global pooling here; clip takes care of camera-cond with ModLN
+        # Input DINO concat also + global pool. InstantMesh adopts DINO (but CA).
+        # expected: support dynamic numbers of frames? since CA, shall be capable of. Any number of context window size.
+        del self.dino_proj
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # st()
+        # (Pdb) p context.keys()
+        # dict_keys(['crossattn', 'vector', 'concat'])
+        # (Pdb) p context['vector'].shape
+        # torch.Size([2, 768])
+        # (Pdb) p context['crossattn'].shape
+        # torch.Size([2, 256, 1024])
+        # (Pdb) p context['concat'].shape
+        # torch.Size([2, 4, 256, 768]) # mv dino spatial features
+
+        # ! clip spatial tokens for append self-attn, thus add a projection layer (self.dino_proj)
+        # DINO features sent via crossattn, thus no proj required (already KV linear layers in crossattn blk)
+        clip_cls_token, clip_spatial_token = self.cap_embedder(context['vector']), self.clip_spatial_proj(context['crossattn']) # no norm here required? QK norm is enough, since self.ln_post(x) in vit
+        dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        # if self.roll_out:  # !
+        x = rearrange(x, 'b (c n) h w->(b n) c h w',
+                      n=3)  # downsample with same conv
+        x = self.x_embedder(x)  # (b n) c h/f w/f
+
+        x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
+        x = x + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        for blk_idx, block in enumerate(self.blocks):
+            # x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token)  # (N, T, D)
+            # ! DINO tokens for CA, CLIP tokens for append here.
+            x = block(x, t0, dino_spatial_token=clip_spatial_token, clip_spatial_token=dino_spatial_token)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+class DiT_I23D_PixelArt_MVCond_noClip(DiT_I23D_PixelArt):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim,
+                         pooling_ctx_dim, roll_out, 
+                         ImageCondDiTBlockPixelArtRMSNormNoClip,
+                         final_layer_blk, 
+                         create_cap_embedder=create_cap_embedder)
+
+
+        # support multi-view img condition
+        # DINO handles global pooling here; clip takes care of camera-cond with ModLN
+        # Input DINO concat also + global pool. InstantMesh adopts DINO (but CA).
+        # expected: support dynamic numbers of frames? since CA, shall be capable of. Any number of context window size.
+
+        del self.dino_proj
+        del self.clip_spatial_proj, self.cap_embedder # no clip required
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # st()
+        # (Pdb) p context.keys()
+        # dict_keys(['crossattn', 'vector', 'concat'])
+        # (Pdb) p context['vector'].shape
+        # torch.Size([2, 768])
+        # (Pdb) p context['crossattn'].shape
+        # torch.Size([2, 256, 1024])
+        # (Pdb) p context['concat'].shape
+        # torch.Size([2, 4, 256, 768]) # mv dino spatial features
+
+        # ! clip spatial tokens for append self-attn, thus add a projection layer (self.dino_proj)
+        # DINO features sent via crossattn, thus no proj required (already KV linear layers in crossattn blk)
+        # clip_cls_token, clip_spatial_token = self.cap_embedder(context['vector']), self.clip_spatial_proj(context['crossattn']) # no norm here required? QK norm is enough, since self.ln_post(x) in vit
+        dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t = self.t_embedder(timesteps)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        # if self.roll_out:  # !
+        x = rearrange(x, 'b (c n) h w->(b n) c h w',
+                      n=3)  # downsample with same conv
+        x = self.x_embedder(x)  # (b n) c h/f w/f
+
+        x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
+        x = x + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        for blk_idx, block in enumerate(self.blocks):
+            # x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token)  # (N, T, D)
+            # ! DINO tokens for CA, CLIP tokens for append here.
+            x = block(x, t0, dino_spatial_token=dino_spatial_token)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+
+
+
+# pcd-structured latent ddpm
+
+class DiT_pcd_I23D_PixelArt_MVCond(DiT_I23D_PixelArt_MVCond_noClip):
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim,
+                         pooling_ctx_dim,
+                          roll_out, ImageCondDiTBlockPixelArtRMSNorm,
+                         final_layer_blk,
+                         create_cap_embedder=create_cap_embedder)
+        # ! first, normalize xyz from [-0.45,0.45] to [-1,1]
+        # Then, encode xyz with point fourier feat + MLP projection, serves as PE here.
+        # a separate MLP for the KL feature
+        # add them together in the feature space
+        # use a single MLP (final_layer) to map them back to 16 + 3 dims.
+        self.x_embedder = Mlp(in_features=in_channels,
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+        del self.pos_embed
+
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        t = self.t_embedder(timesteps)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            # x = block(x, t0, dino_spatial_token=dino_spatial_token, clip_spatial_token=clip_spatial_token)  # (N, T, D)
+            # ! DINO tokens for CA, CLIP tokens for append here.
+            x = block(x, t0, dino_spatial_token=dino_spatial_token)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+class DiT_pcd_I23D_PixelArt_MVCond_clay(DiT_PCD_PixelArt):
+    # fine-tune the mv model from text conditioned model
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False, **kwargs
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                        #  mixed_prediction, context_dim, roll_out, ImageCondDiTBlockPixelArt,
+                         mixed_prediction, context_dim,
+                        #  pooling_ctx_dim,
+                          roll_out, vit_blk,
+                         final_layer_blk,)
+                        #  create_cap_embedder=create_cap_embedder)
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert context is not None
+
+        clip_cls_token = self.cap_embedder(context['caption_vector']) # pooled
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        # ! spatial tokens
+        dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # assert context.ndim == 2
+        # if isinstance(context, dict):
+            # context = context['caption_crossattn']  # sgm conditioner compat
+
+        # loop dit block
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, clip_caption_token=context['caption_crossattn'], 
+                             dino_spatial_token=dino_spatial_token)  # (N, T, D)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+# single-img pretrained clay
+
+class DiT_pcd_I23D_PixelArt_MVCond_clay_i23dpt(DiT_I23D_PCD_PixelArt_noclip):
+    # fine-tune the mv model from text conditioned model
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False, **kwargs
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim,
+                         pooling_ctx_dim,
+                          roll_out, vit_blk,
+                         final_layer_blk,)
+
+        self.has_caption = False
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        # clip_cls_token = self.cap_embedder(context['vector'])
+        # clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+        # dino_spatial_token = context['crossattn']
+        # st()
+        dino_spatial_token = context['img_crossattn']
+        dino_pooled_vector = context['img_vector']
+        dino_mv_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        if self.has_caption:
+            clip_caption_token = context.get('caption_crossattn')
+            pooled_vector = torch.cat([dino_pooled_vector, context.get('caption_vector')], -1) # concat dino_vector
+        else:
+            clip_caption_token = None
+            pooled_vector = dino_pooled_vector
+        
+
+        t = self.t_embedder(timesteps) + self.pooled_vec_embedder(pooled_vector)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, dino_mv_spatial_token=dino_mv_spatial_token)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+# stage 2
+class DiT_pcd_I23D_PixelArt_MVCond_clay_i23dpt_stage2(DiT_I23D_PCD_PixelArt_noclip):
+    # fine-tune the mv model from text conditioned model
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False, **kwargs
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim,
+                         pooling_ctx_dim,
+                          roll_out, vit_blk,
+                         final_layer_blk,)
+
+        self.has_caption = False
+
+        self.use_pe_cond = True
+        self.x_embedder = Mlp(in_features=in_channels+3*(1-self.use_pe_cond),
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+
+        if self.use_pe_cond:
+            self.xyz_pos_embed = XYZPosEmbed(hidden_size)
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        # clip_cls_token = self.cap_embedder(context['vector'])
+        # clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+        # dino_spatial_token = context['crossattn']
+        # st()
+        dino_spatial_token = context['img_crossattn']
+        dino_pooled_vector = context['img_vector']
+        dino_mv_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        if self.has_caption:
+            clip_caption_token = context.get('caption_crossattn')
+            pooled_vector = torch.cat([dino_pooled_vector, context.get('caption_vector')], -1) # concat dino_vector
+        else:
+            clip_caption_token = None
+            pooled_vector = dino_pooled_vector
+        
+
+        t = self.t_embedder(timesteps) + self.pooled_vec_embedder(pooled_vector)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        # x = self.x_embedder(x)
+
+        fps_xyz = context['fps-xyz']
+        if self.use_pe_cond:
+            x = self.x_embedder(x) + self.xyz_pos_embed(fps_xyz) # point-wise addition
+        else: # use concat to add info
+            x = torch.cat([fps_xyz, x], dim=-1)
+            x = self.x_embedder(x)
+
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, dino_spatial_token=dino_spatial_token, dino_mv_spatial_token=dino_mv_spatial_token)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+
+
+class DiT_pcd_I23D_PixelArt_MVCond_clay_i23dpt_noi23d(DiT_I23D_PCD_PixelArt_noclip):
+    # fine-tune the mv model from text conditioned model
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        pooling_ctx_dim=768,
+        roll_out=False,
+        vit_blk=ImageCondDiTBlockPixelArt,
+        final_layer_blk=FinalLayer,
+        create_cap_embedder=False, **kwargs
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim,
+                         pooling_ctx_dim,
+                          roll_out, vit_blk,
+                         final_layer_blk,)
+
+        self.has_caption = False
+        del self.pooled_vec_embedder
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        # clip_cls_token = self.cap_embedder(context['vector'])
+        # clip_spatial_token, dino_spatial_token = context['crossattn'][..., :self.clip_ctx_dim], self.dino_proj(context['crossattn'][..., self.clip_ctx_dim:])
+        # dino_spatial_token = context['crossattn']
+        # st()
+        # dino_spatial_token = context['img_crossattn']
+        # dino_pooled_vector = context['img_vector']
+        dino_mv_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        if self.has_caption:
+            clip_caption_token = context.get('caption_crossattn')
+            pooled_vector = torch.cat([dino_pooled_vector, context.get('caption_vector')], -1) # concat dino_vector
+        else:
+            clip_caption_token = None
+            # pooled_vector = dino_pooled_vector
+            pooled_vector = None
+        
+
+        # t = self.t_embedder(timesteps) + self.pooled_vec_embedder(pooled_vector)
+        t = self.t_embedder(timesteps) 
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            # x = block(x, t0, dino_spatial_token=dino_spatial_token, dino_mv_spatial_token=dino_mv_spatial_token)
+            x = block(x, t0, dino_mv_spatial_token=dino_mv_spatial_token)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+#################################################################################
+#                                   DiT_I23D Configs                                  #
+#################################################################################
+
+
+def DiT_XL_2(**kwargs):
+    return DiT_I23D(depth=28,
+                         hidden_size=1152,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_L_2(**kwargs):
+    return DiT_I23D(depth=24,
+                         hidden_size=1024,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_B_2(**kwargs):
+    return DiT_I23D(depth=12,
+                         hidden_size=768,
+                         patch_size=2,
+                         num_heads=12,
+                         **kwargs)
+
+
+def DiT_B_1(**kwargs):
+    return DiT_I23D(depth=12,
+                         hidden_size=768,
+                         patch_size=1,
+                         num_heads=12,
+                         **kwargs)
+
+
+def DiT_L_Pixelart_2(**kwargs):
+    return DiT_I23D_PixelArt(depth=24,
+                         hidden_size=1024,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_B_Pixelart_2(**kwargs):
+    return DiT_I23D_PixelArt(depth=12,
+                         hidden_size=768,
+                         patch_size=2,
+                         num_heads=12,
+                         **kwargs)
+
+def DiT_L_Pixelart_MV_2(**kwargs):
+    return DiT_I23D_PixelArt_MVCond(depth=24,
+                         hidden_size=1024,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+def DiT_L_Pixelart_MV_2_noclip(**kwargs):
+    return DiT_I23D_PixelArt_MVCond_noClip(depth=24,
+                         hidden_size=1024,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+def DiT_XL_Pixelart_MV_2(**kwargs):
+    return DiT_I23D_PixelArt_MVCond(depth=28,
+                         hidden_size=1152,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+
+
+def DiT_B_Pixelart_MV_2(**kwargs):
+    return DiT_I23D_PixelArt_MVCond(depth=12,
+                         hidden_size=768,
+                         patch_size=2,
+                         num_heads=12,
+                         **kwargs)
+
+# pcd latent 
+
+def DiT_L_Pixelart_MV_pcd(**kwargs):
+    return DiT_pcd_I23D_PixelArt_MVCond(depth=24,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         **kwargs)
+
+# raw gs i23d
+def DiT_L_Pixelart_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt(depth=24,
+    # return DiT_I23D_PCD_PixelArt_noclip(depth=24,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         **kwargs)
+
+def DiT_L_Pixelart_clay_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         enable_rope=False,
+                         **kwargs)
+
+def DiT_XL_Pixelart_clay_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip(depth=28,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                        use_clay_ca=True,
+                         hidden_size=1152,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         enable_rope=False,
+                         **kwargs)
+
+
+def DiT_B_Pixelart_clay_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip(depth=12,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                        use_clay_ca=True,
+                         hidden_size=768,
+                         patch_size=1, # no spatial compression here
+                         num_heads=12,
+                         **kwargs)
+
+def DiT_L_Pixelart_clay_pcd_stage2(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip_clay_stage2(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         use_pe_cond=True,
+                         **kwargs)
+
+def DiT_B_Pixelart_clay_pcd_stage2(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip_clay_stage2(depth=12,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                        use_clay_ca=True,
+                         hidden_size=768,
+                         patch_size=1, # no spatial compression here
+                         num_heads=12,
+                         use_pe_cond=True,
+                         **kwargs)
+
+
+
+def DiT_L_Pixelart_clay_tandi_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayText,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         has_caption=True,
+                         **kwargs)
+
+def DiT_B_Pixelart_clay_tandi_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt_noclip(depth=12,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayText,
+                        use_clay_ca=True,
+                         hidden_size=768,
+                         patch_size=1, # no spatial compression here
+                         num_heads=12,
+                         has_caption=True,
+                         **kwargs)
+
+
+def DiT_B_Pixelart_pcd(**kwargs):
+    return DiT_I23D_PCD_PixelArt(depth=12,
+                         hidden_size=768,
+                         patch_size=1, # no spatial compression here
+                         num_heads=12,
+                         **kwargs)
+
+def DiT_B_Pixelart_pcd_cond_diff(**kwargs):
+    return DiT_I23D_PCD_PixelArt_xyz_cond_kl_diff(depth=12,
+                         hidden_size=768,
+                         patch_size=1, # no spatial compression here
+                         num_heads=12,
+                         **kwargs)
+
+def DiT_B_Pixelart_pcd_cond_diff_pe(**kwargs):
+    return DiT_I23D_PCD_PixelArt_xyz_cond_kl_diff(depth=12,
+                         hidden_size=768,
+                         patch_size=1, # no spatial compression here
+                         vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                         num_heads=12,
+                         use_pe_cond=True,
+                         **kwargs)
+
+def DiT_L_Pixelart_pcd_cond_diff_pe(**kwargs):
+    return DiT_I23D_PCD_PixelArt_xyz_cond_kl_diff(depth=24,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         vit_blk=ImageCondDiTBlockPixelArtRMSNormClayLRM,
+                         num_heads=16,
+                         use_pe_cond=True,
+                         **kwargs)
+
+# mv version
+
+def DiT_L_Pixelart_clay_mv_pcd(**kwargs):
+    return DiT_pcd_I23D_PixelArt_MVCond_clay(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayText,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_L_Pixelart_clay_mv_i23dpt_pcd(**kwargs):
+    return DiT_pcd_I23D_PixelArt_MVCond_clay_i23dpt(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayMV,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_L_Pixelart_clay_mv_i23dpt_pcd_noi23d(**kwargs):
+    return DiT_pcd_I23D_PixelArt_MVCond_clay_i23dpt_noi23d(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayMV_noi23d,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_L_Pixelart_clay_mv_i23dpt_pcd_stage2(**kwargs):
+    return DiT_pcd_I23D_PixelArt_MVCond_clay_i23dpt_stage2(depth=24,
+                        vit_blk=ImageCondDiTBlockPixelArtRMSNormClayMV,
+                        use_clay_ca=True,
+                         hidden_size=1024,
+                         patch_size=1, # no spatial compression here
+                         num_heads=16,
+                         **kwargs)
+
+
+
+
+DiT_models = {
+    'DiT-XL/2': DiT_XL_2,
+    'DiT-L/2': DiT_L_2,
+    'DiT-B/2': DiT_B_2,
+    'DiT-B/1': DiT_B_1,
+    'DiT-PixArt-L/2': DiT_L_Pixelart_2,
+    'DiT-PixArt-MV-XL/2': DiT_XL_Pixelart_MV_2,
+    # 'DiT-PixArt-MV-L/2': DiT_L_Pixelart_MV_2,
+    'DiT-PixArt-MV-L/2': DiT_L_Pixelart_MV_2_noclip,
+    'DiT-PixArt-MV-PCD-L': DiT_L_Pixelart_MV_pcd,
+    # raw xyz cond
+    'DiT-PixArt-PCD-L': DiT_L_Pixelart_pcd,
+    'DiT-PixArt-PCD-CLAY-XL': DiT_XL_Pixelart_clay_pcd,
+    'DiT-PixArt-PCD-CLAY-L': DiT_L_Pixelart_clay_pcd,
+    'DiT-PixArt-PCD-CLAY-B': DiT_B_Pixelart_clay_pcd,
+    'DiT-PixArt-PCD-CLAY-stage2-B': DiT_B_Pixelart_clay_pcd_stage2,
+    'DiT-PixArt-PCD-CLAY-stage2-L': DiT_L_Pixelart_clay_pcd_stage2,
+    'DiT-PixArt-PCD-CLAY-TandI-L': DiT_L_Pixelart_clay_tandi_pcd,
+    'DiT-PixArt-PCD-CLAY-TandI-B': DiT_B_Pixelart_clay_tandi_pcd,
+    'DiT-PixArt-PCD-B': DiT_B_Pixelart_pcd,
+    # xyz-conditioned KL feature diffusion
+    'DiT-PixArt-PCD-cond-diff-B': DiT_B_Pixelart_pcd_cond_diff,
+    'DiT-PixArt-PCD-cond-diff-pe-B': DiT_B_Pixelart_pcd_cond_diff_pe,
+    'DiT-PixArt-PCD-cond-diff-pe-L': DiT_L_Pixelart_pcd_cond_diff_pe,
+    'DiT-PixArt-MV-B/2': DiT_B_Pixelart_MV_2,
+    'DiT-PixArt-B/2': DiT_B_Pixelart_2,
+
+    # ! mv version following clay
+    'DiT-PixArt-PCD-MV-L': DiT_L_Pixelart_clay_mv_pcd,
+    'DiT-PixArt-PCD-MV-I23Dpt-L': DiT_L_Pixelart_clay_mv_i23dpt_pcd,
+    'DiT-PixArt-PCD-MV-I23Dpt-L-noI23D': DiT_L_Pixelart_clay_mv_i23dpt_pcd_noi23d,
+    'DiT-PixArt-PCD-MV-I23Dpt-L-stage2': DiT_L_Pixelart_clay_mv_i23dpt_pcd_stage2,
+}
diff --git a/dit/dit_models.py b/dit/dit_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..d296c973696c50eb6bdcfd01cdd05950b63b2879
--- /dev/null
+++ b/dit/dit_models.py
@@ -0,0 +1,702 @@
+# https://github.com/facebookresearch/DiT
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
+from timm.models.vision_transformer import PatchEmbed, Mlp
+from einops import rearrange
+from pdb import set_trace as st
+
+# support flash attention and xformer acceleration
+from vit.vision_transformer import MemEffAttention as Attention
+
+# from torch.nn import LayerNorm
+# from xformers import triton
+# import xformers.triton
+# from xformers.triton import FusedLayerNorm as LayerNorm
+# from xformers.components.activations import build_activation, Activation
+# from xformers.components.feedforward import fused_mlp
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) *
+            torch.arange(start=0, end=half, dtype=torch.float32) /
+            half).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class LabelEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+
+    def __init__(self, num_classes, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding,
+                                            hidden_size)
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0],
+                                  device=labels.device) < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, train, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        return embeddings
+
+
+class ClipProjector(nn.Module):
+
+    def __init__(self, transformer_width, embed_dim, tx_width, *args,
+                 **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        '''a CLIP text encoder projector, adapted from CLIP.encode_text
+        '''
+
+        self.text_projection = nn.Parameter(
+            torch.empty(transformer_width, embed_dim))
+        nn.init.normal_(self.text_projection, std=tx_width**-0.5)
+
+    def forward(self, clip_text_x):
+        return clip_text_x @ self.text_projection
+
+
+#################################################################################
+#                                 Core DiT Model                                #
+#################################################################################
+
+# class DiTBlock(nn.Module):
+#     """
+#     A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+#     """
+
+#     def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
+#         super().__init__()
+#         nn.LayerNorm
+#         self.norm1 = LayerNorm(
+#             hidden_size,
+#             affine=False,
+#             #   elementwise_affine=False,
+#             eps=1e-6)
+#         self.attn = Attention(hidden_size,
+#                               num_heads=num_heads,
+#                               qkv_bias=True,
+#                               **block_kwargs)
+#         self.norm2 = LayerNorm(
+#             hidden_size,
+#             #   elementwise_affine=False,
+#             affine=False,
+#             eps=1e-6)
+
+#         mlp_hidden_dim = int(hidden_size * mlp_ratio)
+#         approx_gelu = lambda: nn.GELU(approximate="tanh")
+
+#         self.mlp = Mlp(in_features=hidden_size,
+#                        hidden_features=mlp_hidden_dim,
+#                        act_layer=approx_gelu,
+#                        drop=0)
+
+#         # self.mlp = fused_mlp.FusedMLP(
+#         #     dim_model=hidden_size,
+#         #     dropout=0,
+#         #     activation=Activation.GeLU,
+#         #     hidden_layer_multiplier=mlp_ratio,
+#         # )
+
+#         self.adaLN_modulation = nn.Sequential(
+#             nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+
+#     def forward(self, x, c):
+#         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+#             c).chunk(6, dim=1)
+#         x = x + gate_msa.unsqueeze(1) * self.attn(
+#             modulate(self.norm1(x), shift_msa, scale_msa))
+#         x = x + gate_mlp.unsqueeze(1) * self.mlp(
+#             modulate(self.norm2(x), shift_mlp, scale_mlp))
+#         return x
+
+
+class DiTBlock(nn.Module):
+    """
+    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=False,
+                                  eps=1e-6)
+        self.attn = Attention(hidden_size,
+                              num_heads=num_heads,
+                              qkv_bias=True,
+                              **block_kwargs)
+        self.norm2 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=False,
+                                  eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(in_features=hidden_size,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=approx_gelu,
+                       drop=0)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+
+    def forward(self, x, c):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            c).chunk(6, dim=1)
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa))
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+
+
+class DiTBlockRollOut(DiTBlock):
+    """
+    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4, **block_kwargs):
+        super().__init__(hidden_size * 3, num_heads, mlp_ratio, **block_kwargs)
+
+    def forward(self, x, c):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            c).chunk(6, dim=1)
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa))
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT, basically the decoder_pred in MAE with adaLN.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(
+            hidden_size,
+            # self.norm_final = LayerNorm(
+            hidden_size,
+            elementwise_affine=False,)
+            # affine=False,
+            # eps=1e-6)
+        self.linear = nn.Linear(hidden_size,
+                                patch_size * patch_size * out_channels,
+                                bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class DiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        roll_out=False,
+        vit_blk=DiTBlock,
+        final_layer_blk=FinalLayer,
+    ):
+        super().__init__()
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.embed_dim = hidden_size
+
+        # st()
+        self.x_embedder = PatchEmbed(input_size,
+                                     patch_size,
+                                     in_channels,
+                                     hidden_size,
+                                     bias=True)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        if num_classes > 0:
+            self.y_embedder = LabelEmbedder(num_classes, hidden_size,
+                                            class_dropout_prob)
+        else:
+            self.y_embedder = None
+
+        if context_dim is not None:
+            self.clip_text_proj = ClipProjector(context_dim,
+                                                hidden_size,
+                                                tx_width=depth)
+        else:
+            self.clip_text_proj = None
+
+        self.roll_out = roll_out
+
+        num_patches = self.x_embedder.num_patches  # 14*14*3
+        # Will use fixed sin-cos embedding:
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size),
+                                      requires_grad=False)
+
+        # if not self.roll_out:
+        self.blocks = nn.ModuleList([
+            vit_blk(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+            for _ in range(depth)
+        ])
+        # else:
+        #     self.blocks = nn.ModuleList([
+        #         DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) if idx % 2 == 0 else
+        #         DiTBlockRollOut(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+        #         for idx in range(depth)
+        #     ])
+
+        self.final_layer = final_layer_blk(hidden_size, patch_size,
+                                           self.out_channels)
+        self.initialize_weights()
+
+        self.mixed_prediction = mixed_prediction  # This enables mixed prediction
+        if self.mixed_prediction:
+            if self.roll_out:
+                logit_ch = in_channels * 3
+            else:
+                logit_ch = in_channels
+            init = mixing_logit_init * torch.ones(
+                size=[1, logit_ch, 1, 1])  # hard coded for now
+            self.mixing_logit = torch.nn.Parameter(init, requires_grad=True)
+
+    def initialize_weights(self):
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        pos_embed = get_2d_sincos_pos_embed(
+            self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5))
+        # st()
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        # Initialize label embedding table:
+        if self.y_embedder is not None:
+            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        # p = self.x_embedder.patch_size[0]
+        p = self.patch_size
+        h = w = int(x.shape[1]**0.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
+        return imgs
+
+    # def forward(self, x, t, y=None, get_attr=''):
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+
+        if isinstance(context, dict):
+            context = context['crossattn'] # sgm conditioner compat
+
+        if get_attr != '':  # not breaking the forward hooks
+            return getattr(self, get_attr)
+
+        t = self.t_embedder(timesteps)  # (N, D)
+
+        st()
+        if self.roll_out:  # !
+            x = rearrange(x, 'b (n c) h w->(b n) c h w', n=3)
+
+        x = self.x_embedder(
+            x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        if self.roll_out:  # ! roll-out in the L dim, not B dim. add condition to all tokens.
+            x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
+
+        if self.y_embedder is not None:
+            assert y is not None
+            y = self.y_embedder(y, self.training)  # (N, D)
+            c = t + y  # (N, D)
+
+        elif context is not None:
+            assert context.ndim == 2
+            context = self.clip_text_proj(context)
+
+            if context.shape[0] < t.shape[
+                    0]:  # same caption context for different view input of the same ID
+                context = torch.repeat_interleave(context,
+                                                  t.shape[0] //
+                                                  context.shape[0],
+                                                  dim=0)
+
+            # if context.ndim == 3: # compat version from SD
+            #     context = context[:, 0, :]
+            c = t + context
+        else:
+            c = t  # BS 1024
+
+        for blk_idx, block in enumerate(self.blocks):
+            # if self.roll_out:
+            #     if blk_idx % 2 == 0: # with-in plane self attention
+            #         x = rearrange(x, 'b (n l) c -> b l (n c) ', n=3)
+            #         x = block(x, torch.repeat_interleave(c, 3, 0))  # (N, T, D)
+            #     else: # global attention
+            #         # x = rearrange(x, '(b n) l c -> b (n l) c ', n=3)
+            #         x = rearrange(x, 'b l (n c) -> b (n l) c ', n=3)
+            #         x = block(x, c)  # (N, T, D)
+            # else:
+            x = block(x, c)  # (N, T, D)
+
+        x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (n c) h w', n=3)
+            # x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
+
+        return x
+
+    def forward_with_cfg(self, x, t, y, cfg_scale):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        half = x[:len(x) // 2]
+        combined = torch.cat([half, half], dim=0)
+        model_out = self.forward(combined, t, y)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        eps, rest = model_out[:, :3], model_out[:, 3:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0)
+        return torch.cat([eps, rest], dim=1)
+
+    def forward_with_cfg_unconditional(self, x, t, y=None, cfg_scale=None):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        # half = x[:len(x) // 2]
+        # combined = torch.cat([half, half], dim=0)
+        combined = x
+        model_out = self.forward(combined, t, y)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        # eps, rest = model_out[:, :3], model_out[:, 3:]
+        # cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        # half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        # eps = torch.cat([half_eps, half_eps], dim=0)
+        # return torch.cat([eps, rest], dim=1)
+        # st()
+        return model_out
+
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+
+def get_2d_sincos_pos_embed(embed_dim,
+                            grid_size,
+                            cls_token=False,
+                            extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, tuple):
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.arange(grid_size_h, dtype=np.float32)
+        grid_w = np.arange(grid_size_w, dtype=np.float32)
+    else:
+        grid_size_h = grid_size_w = grid_size
+        grid_h = np.arange(grid_size, dtype=np.float32)
+        grid_w = np.arange(grid_size, dtype=np.float32)
+
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
+                                              grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
+                                              grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+#################################################################################
+#                                   DiT Configs                                  #
+#################################################################################
+
+
+def DiT_XL_2(**kwargs):
+    return DiT(depth=28,
+               hidden_size=1152,
+               patch_size=2,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_XL_4(**kwargs):
+    return DiT(depth=28,
+               hidden_size=1152,
+               patch_size=4,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_XL_8(**kwargs):
+    return DiT(depth=28,
+               hidden_size=1152,
+               patch_size=8,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_L_2(**kwargs):
+    return DiT(depth=24,
+               hidden_size=1024,
+               patch_size=2,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_L_4(**kwargs):
+    return DiT(depth=24,
+               hidden_size=1024,
+               patch_size=4,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_L_8(**kwargs):
+    return DiT(depth=24,
+               hidden_size=1024,
+               patch_size=8,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_B_2(**kwargs):
+    return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
+
+
+def DiT_B_4(**kwargs):
+    return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
+
+
+def DiT_B_8(**kwargs):
+    return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
+
+
+def DiT_B_16(**kwargs):  # ours cfg
+    return DiT(depth=12,
+               hidden_size=768,
+               patch_size=16,
+               num_heads=12,
+               **kwargs)
+
+
+def DiT_S_2(**kwargs):
+    return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
+
+
+def DiT_S_4(**kwargs):
+    return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
+
+
+def DiT_S_8(**kwargs):
+    return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
+
+
+DiT_models = {
+    'DiT-XL/2': DiT_XL_2,
+    'DiT-XL/4': DiT_XL_4,
+    'DiT-XL/8': DiT_XL_8,
+    'DiT-L/2': DiT_L_2,
+    'DiT-L/4': DiT_L_4,
+    'DiT-L/8': DiT_L_8,
+    'DiT-B/2': DiT_B_2,
+    'DiT-B/4': DiT_B_4,
+    'DiT-B/8': DiT_B_8,
+    'DiT-B/16': DiT_B_16,
+    'DiT-S/2': DiT_S_2,
+    'DiT-S/4': DiT_S_4,
+    'DiT-S/8': DiT_S_8,
+}
diff --git a/dit/dit_models_xformers.py b/dit/dit_models_xformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfcc02d284c2fdc2d82627bbcc0c0ba63bfd78a1
--- /dev/null
+++ b/dit/dit_models_xformers.py
@@ -0,0 +1,1461 @@
+# https://github.com/facebookresearch/DiT
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
+from timm.models.vision_transformer import PatchEmbed, Mlp
+from einops import rearrange
+from pdb import set_trace as st
+
+# support flash attention and xformer acceleration
+from ldm.modules.attention import CrossAttention
+from vit.vision_transformer import MemEffAttention as Attention
+# import apex
+# from apex.normalization import FusedRMSNorm as RMSNorm
+
+from torch.nn import LayerNorm
+
+try:
+    from apex.normalization import FusedRMSNorm as RMSNorm
+except:
+    from dit.norm import RMSNorm
+
+# from torch.nn import LayerNorm
+# from xformers import triton
+# import xformers.triton
+
+if torch.cuda.is_available():
+    # from xformers.triton import FusedLayerNorm as LayerNorm # compat issue
+    from xformers.components.activations import build_activation, Activation
+    from xformers.components.feedforward import fused_mlp
+    # from xformers.components.feedforward import mlp
+
+from ldm.modules.attention import MemoryEfficientCrossAttention, JointMemoryEfficientCrossAttention
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def t2i_modulate(x, shift, scale):  # for pix-art arch
+    return x * (1 + scale) + shift
+
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+
+
+class T2IFinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+    # from torch.nn import LayerNorm
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        # self.norm_final = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm_final = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size,
+                                patch_size * patch_size * out_channels,
+                                bias=True)
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(2, hidden_size) / hidden_size**0.5)
+        self.adaLN_modulation = None
+        self.out_channels = out_channels
+
+    def forward(self, x, t):
+        shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2,
+                                                                         dim=1)
+        x = t2i_modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) *
+            torch.arange(start=0, end=half, dtype=torch.float32) /
+            half).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class LabelEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+
+    def __init__(self, num_classes, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding,
+                                            hidden_size)
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0],
+                                  device=labels.device) < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, train, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        return embeddings
+
+
+class ClipProjector(nn.Module):
+
+    def __init__(self, transformer_width, embed_dim, tx_width, *args,
+                 **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        '''a CLIP text encoder projector, adapted from CLIP.encode_text
+        '''
+
+        self.text_projection = nn.Parameter(
+            torch.empty(transformer_width, embed_dim))
+        nn.init.normal_(self.text_projection, std=tx_width**-0.5)
+
+    def forward(self, clip_text_x):
+        return clip_text_x @ self.text_projection
+
+
+def approx_gelu():
+    return nn.GELU(approximate="tanh")
+
+
+class CaptionEmbedder(nn.Module):
+    """
+    copied from https://github.com/hpcaitech/Open-Sora
+
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 hidden_size,
+                 act_layer=nn.GELU(approximate="tanh"),
+                 token_num=120):
+        super().__init__()
+
+        self.y_proj = Mlp(in_features=in_channels,
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=act_layer,
+                          drop=0)
+        # self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels**0.5))
+        # self.uncond_prob = uncond_prob
+
+    # def token_drop(self, caption, force_drop_ids=None):
+    #     """
+    #     Drops labels to enable classifier-free guidance.
+    #     """
+    #     if force_drop_ids is None:
+    #         drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
+    #     else:
+    #         drop_ids = force_drop_ids == 1
+    #     caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
+    #     return caption
+
+    def forward(self, caption, **kwargs):
+        # if train:
+        #     assert caption.shape[2:] == self.y_embedding.shape
+        # use_dropout = self.uncond_prob > 0
+        # if (train and use_dropout) or (force_drop_ids is not None):
+        #     caption = self.token_drop(caption, force_drop_ids)
+        caption = self.y_proj(caption)
+        return caption
+
+
+#################################################################################
+#                                 Core DiT Model                                #
+#################################################################################
+
+
+class DiTBlock(nn.Module):
+    """
+    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 mlp_ratio=4.0,
+                 context_dim=None,
+                #  enable_rmsnorm=False,
+                 norm_type='layernorm',
+                 qk_norm=True,
+                #  enable_rope=False,
+                 **block_kwargs):
+        super().__init__()
+        # st()
+        assert qk_norm
+        # nn.LayerNorm
+        if norm_type == 'layernorm':
+            self.norm1 = LayerNorm(
+                hidden_size,
+                # affine=False,
+                  elementwise_affine=False,
+                eps=1e-6)
+            self.norm2 = LayerNorm(
+                hidden_size,
+                # affine=False,
+                elementwise_affine=False,
+                eps=1e-6)
+        else:
+            assert norm_type == 'rmsnorm'  # more robust to bf16 training.
+            self.norm1 = RMSNorm(hidden_size, eps=1e-5)
+            self.norm2 = RMSNorm(hidden_size, eps=1e-5)
+
+        # st()
+        self.attn = Attention(hidden_size,
+                              num_heads=num_heads,
+                              qkv_bias=True,
+                              qk_norm=qk_norm,
+                              **block_kwargs)
+        # mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # approx_gelu = lambda: nn.GELU(approximate="tanh")
+
+        # self.mlp = Mlp(in_features=hidden_size,
+        #                hidden_features=mlp_hidden_dim,
+        #                act_layer=approx_gelu,
+        #                drop=0)
+
+        self.mlp = fused_mlp.FusedMLP(
+            dim_model=hidden_size,
+            dropout=0,
+            activation=Activation.GeLU,
+            hidden_layer_multiplier=int(mlp_ratio),
+        )
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+
+    def forward(self, x, c):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            c).chunk(6, dim=1)
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa))
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+
+
+class TextCondDiTBlock(DiTBlock):
+    # https://github.com/hpcaitech/Open-Sora/blob/68b8f60ff0ff4b3a3b63fe1d8cb17d66b7845ef7/opensora/models/stdit/stdit.py#L69
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4, **block_kwargs):
+        super().__init__(hidden_size, num_heads, mlp_ratio, **block_kwargs)
+        self.cross_attn = MemoryEfficientCrossAttention(query_dim=hidden_size,
+                                                        heads=num_heads)
+        # self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
+
+    def forward(self, x, t, context):
+        # B, N, C = x.shape
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #    self.scale_shift_table[None] + t.reshape(B,6,-1)).chunk(6, dim=1)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            t).chunk(6, dim=1)
+
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa))
+
+        # add text embedder via pre-norm cross attention
+        x = x + self.cross_attn(x, context)
+
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+class PixelArtTextCondDiTBlock(DiTBlock):
+    # 1. add shared AdaLN
+    # 2. add return pooled vector token (in the outer loop already)
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 mlp_ratio=4,
+                 context_dim=None,
+                qk_norm=True,
+                 **block_kwargs):
+        super().__init__(hidden_size,
+                         num_heads,
+                         mlp_ratio,
+                         norm_type='rmsnorm',
+                         qk_norm=qk_norm,
+                         **block_kwargs)
+        # super().__init__(hidden_size, num_heads, mlp_ratio, norm_type='layernorm', **block_kwargs)
+        self.cross_attn = MemoryEfficientCrossAttention(
+            query_dim=hidden_size, context_dim=context_dim, heads=num_heads, qk_norm=qk_norm) # ! force QK_Norm
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(6, hidden_size) / hidden_size**0.5)
+        self.adaLN_modulation = None
+        self.attention_y_norm = RMSNorm(
+            context_dim, eps=1e-5
+        )  # https://github.com/Alpha-VLLM/Lumina-T2X/blob/0c8dd6a07a3b7c18da3d91f37b1e00e7ae661293/lumina_t2i/models/model.py#L570C9-L570C61
+
+        self.prenorm_ca_text = RMSNorm(hidden_size, eps=1e-5)
+
+    def forward(self, x, t, context):
+        B, N, C = x.shape
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #    self.scale_shift_table[None] + t.reshape(B,6,-1)).chunk(6, dim=1)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #     t).chunk(6, dim=1)
+
+        x = x + gate_msa * self.attn(
+            t2i_modulate(self.norm1(x), shift_msa, scale_msa))
+
+        # add text embedder via cross attention
+        x = x + self.cross_attn(self.prenorm_ca_text(x), self.attention_y_norm(context))
+
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+class MMTextCondDiTBlock(DiTBlock):
+    # follow SD-3
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4, **block_kwargs):
+        super().__init__(hidden_size, num_heads, mlp_ratio, **block_kwargs)
+        self.cross_attn = MemoryEfficientCrossAttention(query_dim=hidden_size,
+                                                        heads=num_heads)
+        # self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
+
+        self.adaLN_modulation_img = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+
+        self.mlp_img = fused_mlp.FusedMLP(
+            dim_model=hidden_size,
+            dropout=0,
+            activation=Activation.GeLU,
+            hidden_layer_multiplier=int(mlp_ratio),
+        )
+
+    def forward(self, x, t, context):
+        # B, N, C = x.shape
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #    self.scale_shift_table[None] + t.reshape(B,6,-1)).chunk(6, dim=1)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            t).chunk(6, dim=1)
+
+        # TODO, batch inference with above
+        shift_msa_img, scale_msa_img, gate_msa_img, shift_mlp_img, scale_mlp_img, gate_mlp_img = self.adaLN_modulation_img(
+            t).chunk(6, dim=1)
+
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa))
+
+        # add text embedder via cross attention
+        x = x + self.cross_attn(x, context)
+
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+# for image condition
+
+
+class ImageCondDiTBlock(DiTBlock):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                #  enable_rmsnorm=False,
+                 qk_norm=True,
+                 enable_rope=False,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=enable_rmsnorm,
+                         qk_norm=qk_norm,
+                         enable_rope=enable_rope,
+                         **block_kwargs)
+        assert qk_norm
+        self.cross_attn = MemoryEfficientCrossAttention(
+            query_dim=hidden_size,
+            context_dim=context_dim, # ! mv-cond
+            # context_dim=1280,  # clip vit-G, adopted by SVD.
+            # context_dim=1024,  # clip vit-L
+            heads=num_heads,
+            # enable_rmsnorm=enable_rmsnorm, 
+            qk_norm=qk_norm, 
+            enable_rope=enable_rope)
+        assert qk_norm
+        # self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
+
+        self.attention_y_norm = RMSNorm(
+            1024, eps=1e-5
+        )  # https://github.com/Alpha-VLLM/Lumina-T2X/blob/0c8dd6a07a3b7c18da3d91f37b1e00e7ae661293/lumina_t2i/models/model.py#L570C9-L570C61
+
+    def forward(self, x, t, dino_spatial_token, clip_spatial_token):
+        # B, N, C = x.shape
+        # assert isinstance(context, dict)
+        # assert isinstance(context, dict) # clip + dino.
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #    self.scale_shift_table[None] + t.reshape(B,6,-1)).chunk(6, dim=1)
+
+        # TODO t is t + [clip_cls] here. update in base class.
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            t).chunk(6, dim=1)
+
+        post_modulate_selfattn_feat = torch.cat([
+            modulate(self.norm1(x), shift_msa, scale_msa), dino_spatial_token
+        ],
+                                                dim=1)  # concat in L dim
+
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            post_modulate_selfattn_feat
+        )[:, :x.shape[1]]  # remove dino-feat to maintain unchanged dimension.
+
+        # add clip_i spatial embedder via cross attention
+        x = x + self.cross_attn(x, self.attention_y_norm(clip_spatial_token))
+
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+class ImageCondDiTBlockPixelArt(ImageCondDiTBlock):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                #  enable_rmsnorm=False,
+                 qk_norm=False,
+                    norm_type='rmsnorm',
+                 **block_kwargs):
+        # super().__init__(hidden_size, num_heads, mlp_ratio, enable_rmsnorm=True, **block_kwargs)
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                         qk_norm=True, # otherwise AMP fail
+                         norm_type=norm_type,
+                         **block_kwargs)
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(6, hidden_size) / hidden_size**0.5)
+        self.adaLN_modulation = None  # single-adaLN
+        # self.attention_y_norm = RMSNorm(
+        #     1024, eps=1e-5
+        # )  # https://github.com/Alpha-VLLM/Lumina-T2X/blob/0c8dd6a07a3b7c18da3d91f37b1e00e7ae661293/lumina_t2i/models/model.py#L570C9-L570C61
+
+    def forward(self, x, t, dino_spatial_token, clip_spatial_token):
+        B, N, C = x.shape
+        # assert isinstance(context, dict)
+        # assert isinstance(context, dict) # clip + dino.
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #    self.scale_shift_table[None] + t.reshape(B,6,-1)).chunk(6, dim=1)
+
+        # TODO t is t + [clip_cls] here. update in base class.
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        # t).chunk(6, dim=1)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        # st()
+
+        post_modulate_selfattn_feat = torch.cat([
+            t2i_modulate(self.norm1(x), shift_msa, scale_msa),
+            dino_spatial_token
+        ],
+                                                dim=1)  # concat in L dim
+
+        # x = x + gate_msa.unsqueeze(1) * self.attn(
+        x = x + gate_msa * self.attn(post_modulate_selfattn_feat)[:, :x.shape[
+            1]]  # remove dino-feat to maintain unchanged dimension.
+
+        # add clip_i spatial embedder via cross attention
+        x = x + self.cross_attn(x, clip_spatial_token) # attention_y_norm not required, since x_norm_patchtokens?
+        # x = x + self.cross_attn(x, self.attention_y_norm(clip_spatial_token)) # attention_y_norm not required, since x_norm_patchtokens?
+
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+class ImageCondDiTBlockPixelArtNoclip(ImageCondDiTBlock):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                #  enable_rmsnorm=False,
+                 qk_norm=False,
+                 **block_kwargs):
+        # super().__init__(hidden_size, num_heads, mlp_ratio, enable_rmsnorm=True, **block_kwargs)
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                         qk_norm=True, # otherwise AMP fail
+                         **block_kwargs)
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(6, hidden_size) / hidden_size**0.5)
+        self.adaLN_modulation = None  # single-adaLN
+        # self.attention_y_norm = RMSNorm(
+        #     1024, eps=1e-5
+        # )  # https://github.com/Alpha-VLLM/Lumina-T2X/blob/0c8dd6a07a3b7c18da3d91f37b1e00e7ae661293/lumina_t2i/models/model.py#L570C9-L570C61
+
+    # def _forward_cross_attention(self, x, tokens):
+    #     x = x + self.cross_attn(x, tokens) # attention_y_norm not required, since x_norm_patchtokens?
+    #     return x
+
+    # def forward(self, x, t, dino_spatial_token, clip_spatial_token):
+    def forward(self, x, t, dino_spatial_token):
+        B, N, C = x.shape
+        # assert isinstance(context, dict)
+        # assert isinstance(context, dict) # clip + dino.
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        #    self.scale_shift_table[None] + t.reshape(B,6,-1)).chunk(6, dim=1)
+
+        # TODO t is t + [clip_cls] here. update in base class.
+
+        # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+        # t).chunk(6, dim=1)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        # st()
+
+        post_modulate_selfattn_feat = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
+
+            # t2i_modulate(self.norm1(x), shift_msa, scale_msa),
+            # dino_spatial_token
+        # ],
+                                                # dim=1)  # concat in L dim
+
+        # x = x + gate_msa.unsqueeze(1) * self.attn(
+        x = x + gate_msa * self.attn(post_modulate_selfattn_feat) # remove dino-feat to maintain unchanged dimension.
+
+        # add clip_i spatial embedder via cross attention
+        x = x + self.cross_attn(x, dino_spatial_token) # attention_y_norm not required, since x_norm_patchtokens?
+        # x = self._forward_cross_attention(x, dino_spatial_token)
+        # x = x + self.cross_attn(x, self.attention_y_norm(clip_spatial_token)) # attention_y_norm not required, since x_norm_patchtokens?
+
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+class ImageCondDiTBlockPixelArtRMSNorm(ImageCondDiTBlockPixelArt):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                # enable_rmsnorm=False,
+                norm_type='rmsnorm',
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                         norm_type=norm_type,
+                         **block_kwargs)
+
+
+class ImageCondDiTBlockPixelArtRMSNormNoClip(ImageCondDiTBlockPixelArtNoclip):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                         enable_rmsnorm=False,
+                         norm_type='rmsnorm',
+                         **block_kwargs)
+
+# support more conditions
+class ImageCondDiTBlockPixelArtRMSNormClay(ImageCondDiTBlockPixelArtRMSNorm):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                 qk_norm=True,
+                 norm_type='rmsnorm',
+                 enable_text_ca=False,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                         norm_type=norm_type,
+                         qk_norm=qk_norm,
+                         **block_kwargs)
+
+        # ca for text
+        self.cross_attn_dino = MemoryEfficientCrossAttention(
+            query_dim=hidden_size,
+            context_dim=context_dim,
+            heads=num_heads,
+            # enable_rmsnorm=enable_rmsnorm, 
+            qk_norm=qk_norm, 
+            dim_head=hidden_size//num_heads)
+        # if enable_text_ca:
+        #     self.cross_attn_text = MemoryEfficientCrossAttention(
+        #         query_dim=hidden_size,
+        #         context_dim=context_dim,
+        #         heads=num_heads,
+        #         # enable_rmsnorm=enable_rmsnorm, 
+        #         qk_norm=qk_norm)
+        # else:
+        self.cross_attn_text = lambda x, context: torch.zeros_like(x) # always return 0
+        
+    # def _forward_cross_attention(self, x, tokens):
+    #     x = x + self.cross_attn(x, tokens) # attention_y_norm not required, since x_norm_patchtokens?
+    #     return x
+        
+    def forward(self, x, t, dino_spatial_token, clip_spatial_token, clip_caption_token):
+        B, N, C = x.shape
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+
+        post_modulate_selfattn_feat = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
+
+        x = x + gate_msa * self.attn(post_modulate_selfattn_feat) # remove dino-feat to maintain unchanged dimension.
+
+        # can be accelerated by parallel attention
+        x = x + self.cross_attn(x, clip_spatial_token) + self.cross_attn_dino(x, dino_spatial_token) + self.cross_attn_text(x, clip_caption_token)
+
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+# lrm-like, CA-SA-FFN
+class ImageCondDiTBlockPixelArtRMSNormClayLRM(ImageCondDiTBlockPixelArtRMSNorm):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                 qk_norm=True,
+                 norm_type='rmsnorm',
+                 enable_text_ca=False,
+                 enable_rope=False,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                         norm_type=norm_type,
+                         qk_norm=qk_norm,
+                         enable_rope=enable_rope,
+                         **block_kwargs)
+
+        # ca for text
+        self.cross_attn_dino = MemoryEfficientCrossAttention(
+            query_dim=hidden_size,
+            context_dim=context_dim,
+            heads=num_heads,
+            # enable_rmsnorm=enable_rmsnorm, 
+            qk_norm=qk_norm,
+            dim_head=hidden_size//num_heads, 
+            enable_rope=enable_rope)
+        self.prenorm_ca_dino = RMSNorm(hidden_size, eps=1e-5)
+        # if enable_text_ca:
+        #     self.cross_attn_text = MemoryEfficientCrossAttention(
+        #         query_dim=hidden_size,
+        #         context_dim=context_dim,
+        #         heads=num_heads,
+        #         # enable_rmsnorm=enable_rmsnorm, 
+        #         qk_norm=qk_norm)
+        # else:
+        self.cross_attn_text = lambda x, context, freqs_cis: torch.zeros_like(x) # always return 0
+        self.prenorm_ca_text = nn.Identity()
+        del self.cross_attn # not for text now
+        
+    # def _forward_cross_attention(self, x, tokens):
+    #     x = x + self.cross_attn(x, tokens) # attention_y_norm not required, since x_norm_patchtokens?
+    #     return x
+        
+    def forward(self, x, t, dino_spatial_token, clip_caption_token=None, freqs_cis=None, **kwargs):
+        B, N, C = x.shape
+
+        # ! prepare adaLN
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+
+        # post_modulate_selfattn_feat = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
+
+        # ! CA for spaital info (img, mv-img .etc.)
+        x = x + self.cross_attn_dino(self.prenorm_ca_dino(x), dino_spatial_token, freqs_cis=freqs_cis)  # ! pre-norm & residual, as in CLAY
+
+        # ! SA
+        x = x + gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), freqs_cis=freqs_cis) # remove dino-feat to maintain unchanged dimension.
+
+        # # ! CA for text
+        x = x + self.cross_attn_text(self.prenorm_ca_text(x), clip_caption_token, freqs_cis=freqs_cis)
+
+        # ! FFN
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+# support more conditions
+class ImageCondDiTBlockPixelArtRMSNormClayText(PixelArtTextCondDiTBlock):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                 qk_norm=True,
+                #  norm_type='rmsnorm',
+                 enable_text_ca=False,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                        #  norm_type=norm_type,
+                         qk_norm=qk_norm,
+                         **block_kwargs)
+
+        # dino_hidden_size = 768
+        dino_hidden_size = 384 # a dit-s as in clay
+        self.cross_attn_dino = MemoryEfficientCrossAttention(
+            query_dim=hidden_size,
+            context_dim=dino_hidden_size,
+            heads=num_heads,
+            # enable_rmsnorm=enable_rmsnorm, 
+            qk_norm=qk_norm)
+        self.prenorm_ca_dino = RMSNorm(hidden_size, eps=1e-5)
+
+
+    def forward(self, x, t, dino_spatial_token, clip_caption_token=None, **kwargs):
+        B, N, C = x.shape
+
+        # ! prepare adaLN
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+
+        # post_modulate_selfattn_feat = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
+
+        # ! SA
+        x = x + gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)) # remove dino-feat to maintain unchanged dimension.
+
+        # # ! CA for text
+        # x = x + self.cross_attn(self.prenorm_ca_text(x), self.attention_y_norm(clip_caption_token))
+
+        # ! add more conditions through residuals.
+        x = x + self.cross_attn(self.prenorm_ca_text(x), self.attention_y_norm(clip_caption_token)) + self.cross_attn_dino(self.prenorm_ca_dino(x), dino_spatial_token)
+
+        # # ! CA for spaital info (img, mv-img .etc.)
+        # x = x + self.cross_attn_dino(self.prenorm_ca_dino(x), dino_spatial_token)  # ! pre-norm & residual, as in CLAY
+
+        # ! FFN
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+# support more conditions
+class ImageCondDiTBlockPixelArtRMSNormClayMV(ImageCondDiTBlockPixelArtRMSNormClayLRM):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                 qk_norm=True,
+                #  norm_type='rmsnorm',
+                 enable_text_ca=False,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                        #  norm_type=norm_type,
+                         qk_norm=qk_norm,
+                         **block_kwargs)
+
+        # dino_hidden_size = 768
+        dino_hidden_size = 384 # a dit-s as in clay
+        self.cross_attn_dino_mv = MemoryEfficientCrossAttention(
+            query_dim=hidden_size,
+            context_dim=dino_hidden_size,
+            heads=num_heads,
+            # enable_rmsnorm=enable_rmsnorm, 
+            qk_norm=qk_norm)
+        self.prenorm_ca_dino_mv = RMSNorm(hidden_size, eps=1e-5)
+        # self.mv_alpha = nn.Parameter(torch.zeros(1)-2.5) # per-layer blending into the original parameters, like zero-conv.
+        # self.mv_alpha = nn.Parameter(torch.zeros(1)+2.5) # per-layer blending into the original parameters, like zero-conv.
+        self.mv_alpha = nn.Parameter(torch.zeros(1)) # per-layer blending into the original parameters, like zero-conv.
+
+
+    def forward(self, x, t, dino_spatial_token, dino_mv_spatial_token=None, **kwargs):
+        B, N, C = x.shape
+
+        # ! prepare adaLN
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+
+        # post_modulate_selfattn_feat = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
+
+        # ! CA for spaital info (img, mv-img .etc.)
+        x = x + self.cross_attn_dino(self.prenorm_ca_dino(x), dino_spatial_token) + self.cross_attn_dino_mv(self.prenorm_ca_dino_mv(x), dino_mv_spatial_token) * (torch.nn.functional.tanh(self.mv_alpha))
+
+        # ! SA
+        x = x + gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)) # remove dino-feat to maintain unchanged dimension.
+
+        # # ! CA for text
+        # x = x + self.cross_attn_text(self.prenorm_ca_text(x), clip_caption_token)
+
+        # ! FFN
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+# no i23d ca
+class ImageCondDiTBlockPixelArtRMSNormClayMV_noi23d(ImageCondDiTBlockPixelArtRMSNormClayLRM):
+    # follow EMU and SVD, concat + cross attention. Also adopted by concurrent work Direct3D.
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 context_dim,
+                 mlp_ratio=4,
+                 qk_norm=True,
+                #  norm_type='rmsnorm',
+                 enable_text_ca=False,
+                 **block_kwargs):
+        super().__init__(hidden_size=hidden_size,
+                         num_heads=num_heads,
+                         mlp_ratio=mlp_ratio,
+                         context_dim=context_dim,
+                        #  enable_rmsnorm=False,
+                        #  norm_type=norm_type,
+                         qk_norm=qk_norm,
+                         **block_kwargs)
+
+        # del self.cross_attn_dino, self.prenorm_ca_dino
+        del self.cross_attn_dino
+        self.prenorm_ca_dino_mv = self.prenorm_ca_dino
+        # dino_hidden_size = 768
+        dino_hidden_size = 384 # a dit-s as in clay
+        self.cross_attn_dino_mv = MemoryEfficientCrossAttention(
+            query_dim=hidden_size,
+            context_dim=dino_hidden_size,
+            heads=num_heads,
+            # enable_rmsnorm=enable_rmsnorm, 
+            qk_norm=qk_norm)
+        # self.prenorm_ca_dino_mv = RMSNorm(hidden_size, eps=1e-5)
+        # self.mv_alpha = nn.Parameter(torch.zeros(1)-2.5) # per-layer blending into the original parameters, like zero-conv.
+        # self.mv_alpha = nn.Parameter(torch.zeros(1)+2.5) # per-layer blending into the original parameters, like zero-conv.
+        # self.mv_alpha = nn.Parameter(torch.zeros(1)) # per-layer blending into the original parameters, like zero-conv.
+
+
+    # def forward(self, x, t, dino_spatial_token, dino_mv_spatial_token=None, **kwargs):
+    def forward(self, x, t, dino_mv_spatial_token, **kwargs):
+        B, N, C = x.shape
+
+        # ! prepare adaLN
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+
+        # post_modulate_selfattn_feat = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
+
+        # ! CA for spaital info (img, mv-img .etc.)
+        # x = x + self.cross_attn_dino(self.prenorm_ca_dino(x), dino_spatial_token) + self.cross_attn_dino_mv(self.prenorm_ca_dino_mv(x), dino_mv_spatial_token) * (torch.nn.functional.tanh(self.mv_alpha))
+        x = x + self.cross_attn_dino_mv(self.prenorm_ca_dino_mv(x), dino_mv_spatial_token)
+
+        # ! SA
+        x = x + gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)) # remove dino-feat to maintain unchanged dimension.
+
+        # # ! CA for text
+        # x = x + self.cross_attn_text(self.prenorm_ca_text(x), clip_caption_token)
+
+        # ! FFN
+        x = x + gate_mlp * self.mlp(
+            t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
+
+        return x
+
+
+
+
+class DiTBlockRollOut(DiTBlock):
+    """
+    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4, **block_kwargs):
+        super().__init__(hidden_size * 3, num_heads, mlp_ratio, **block_kwargs)
+
+    def forward(self, x, c):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
+            c).chunk(6, dim=1)
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa))
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+# 
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT, basically the decoder_pred in MAE with adaLN.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        # self.norm_final = nn.LayerNorm(hidden_size,
+        self.norm_final = LayerNorm(
+            hidden_size,
+               elementwise_affine=False,#  apex or nn kernel
+            # affine=False,
+            eps=1e-6)
+        self.linear = nn.Linear(hidden_size,
+                                patch_size * patch_size * out_channels,
+                                bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class DiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        roll_out=False,
+        vit_blk=DiTBlock,
+        # vit_blk=TextCondDiTBlock,
+        final_layer_blk=FinalLayer,
+        enable_rope=False,
+    ):
+        super().__init__()
+        self.plane_n = 3
+        # st()
+
+        self.depth = depth
+        self.mlp_ratio = mlp_ratio
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.embed_dim = hidden_size
+
+        self.x_embedder = PatchEmbed(input_size,
+                                     patch_size,
+                                     in_channels,
+                                     hidden_size,
+                                     bias=True)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        if num_classes > 0:
+            self.y_embedder = LabelEmbedder(num_classes, hidden_size,
+                                            class_dropout_prob)
+        else:
+            self.y_embedder = None
+
+        if context_dim is not None:
+            self.clip_text_proj = CaptionEmbedder(context_dim,
+                                                  hidden_size,
+                                                  act_layer=approx_gelu)
+
+        else:
+            self.clip_text_proj = None
+
+        self.roll_out = roll_out
+
+        num_patches = self.x_embedder.num_patches  # 14*14*3
+        # Will use fixed sin-cos embedding:
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size),
+                                      requires_grad=False)
+
+        # if not self.roll_out:
+        self.blocks = nn.ModuleList([
+            vit_blk(hidden_size=hidden_size,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    context_dim=context_dim, 
+                    enable_rope=enable_rope) for _ in range(depth)
+        ])
+        # else:
+        #     self.blocks = nn.ModuleList([
+        #         DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) if idx % 2 == 0 else
+        #         DiTBlockRollOut(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+        #         for idx in range(depth)
+        #     ])
+
+        self.final_layer = final_layer_blk(hidden_size, patch_size,
+                                           self.out_channels)
+        self.initialize_weights()
+
+        # self.mixed_prediction = mixed_prediction  # This enables mixed prediction
+        # if self.mixed_prediction:
+        #     if self.roll_out:
+        #         logit_ch = in_channels * 3
+        #     else:
+        #         logit_ch = in_channels
+        #     init = mixing_logit_init * torch.ones(
+        #         size=[1, logit_ch, 1, 1])  # hard coded for now
+        #     self.mixing_logit = torch.nn.Parameter(init, requires_grad=True)
+
+    # def len(self):
+    #     return len(self.blocks)
+
+    def initialize_weights(self):
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        pos_embed = get_2d_sincos_pos_embed(
+            self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5))
+        # st()
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        # Initialize label embedding table:
+        if self.y_embedder is not None:
+            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.blocks:
+            if block.adaLN_modulation is not None:
+                nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+                nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        if self.final_layer.adaLN_modulation is not None:
+            nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        # p = self.x_embedder.patch_size[0]
+        p = self.patch_size
+        h = w = int(x.shape[1]**0.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
+        return imgs
+
+    # def forward(self, x, t, y=None, get_attr=''):
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+
+        if get_attr != '':  # not breaking the forward hooks
+            return getattr(self, get_attr)
+
+        t = self.t_embedder(timesteps)  # (N, D)
+
+        if self.roll_out:  # !
+            x = rearrange(x, 'b (c n) h w->(b n) c h w', n=3)
+
+        x = self.x_embedder(
+            x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        if self.roll_out:  # ! roll-out in the L dim, not B dim. add condition to all tokens.
+            x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
+
+        # if self.y_embedder is not None:
+        #     assert y is not None
+        #     y = self.y_embedder(y, self.training)  # (N, D)
+        #     c = t + y  # (N, D)
+
+        assert context is not None
+
+        # assert context.ndim == 2
+        if isinstance(context, dict):
+            context = context['crossattn']  # sgm conditioner compat
+        context = self.clip_text_proj(context)
+
+        # c = t + context
+        # else:
+        # c = t  # BS 1024
+
+        for blk_idx, block in enumerate(self.blocks):
+            # if self.roll_out:
+            #     if blk_idx % 2 == 0: # with-in plane self attention
+            #         x = rearrange(x, 'b (n l) c -> b l (n c) ', n=3)
+            #         x = block(x, torch.repeat_interleave(c, 3, 0))  # (N, T, D)
+            #     else: # global attention
+            #         # x = rearrange(x, '(b n) l c -> b (n l) c ', n=3)
+            #         x = rearrange(x, 'b l (n c) -> b (n l) c ', n=3)
+            #         x = block(x, c)  # (N, T, D)
+            # else:
+            # st()
+            x = block(x, t, context)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+            # x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32)
+
+        return x
+
+    def forward_with_cfg(self, x, t, y, cfg_scale):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        half = x[:len(x) // 2]
+        combined = torch.cat([half, half], dim=0)
+        model_out = self.forward(combined, t, y)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        eps, rest = model_out[:, :3], model_out[:, 3:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0)
+        return torch.cat([eps, rest], dim=1)
+
+    def forward_with_cfg_unconditional(self, x, t, y=None, cfg_scale=None):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        # half = x[:len(x) // 2]
+        # combined = torch.cat([half, half], dim=0)
+        combined = x
+        model_out = self.forward(combined, t, y)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        # eps, rest = model_out[:, :3], model_out[:, 3:]
+        # cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        # half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        # eps = torch.cat([half_eps, half_eps], dim=0)
+        # return torch.cat([eps, rest], dim=1)
+        # st()
+        return model_out
+
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+
+def get_2d_sincos_pos_embed(embed_dim,
+                            grid_size,
+                            cls_token=False,
+                            extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, tuple):
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.arange(grid_size_h, dtype=np.float32)
+        grid_w = np.arange(grid_size_w, dtype=np.float32)
+    else:
+        grid_size_h = grid_size_w = grid_size
+        grid_h = np.arange(grid_size, dtype=np.float32)
+        grid_w = np.arange(grid_size, dtype=np.float32)
+
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
+                                              grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
+                                              grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+#################################################################################
+#                                   DiT Configs                                  #
+#################################################################################
+
+
+def DiT_XL_2(**kwargs):
+    return DiT(depth=28,
+               hidden_size=1152,
+               patch_size=2,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_XL_4(**kwargs):
+    return DiT(depth=28,
+               hidden_size=1152,
+               patch_size=4,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_XL_8(**kwargs):
+    return DiT(depth=28,
+               hidden_size=1152,
+               patch_size=8,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_L_2(**kwargs):
+    return DiT(depth=24,
+               hidden_size=1024,
+               patch_size=2,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_L_4(**kwargs):
+    return DiT(depth=24,
+               hidden_size=1024,
+               patch_size=4,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_L_8(**kwargs):
+    return DiT(depth=24,
+               hidden_size=1024,
+               patch_size=8,
+               num_heads=16,
+               **kwargs)
+
+
+def DiT_B_2(**kwargs):
+    return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
+
+
+def DiT_B_4(**kwargs):
+    return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
+
+
+def DiT_B_8(**kwargs):
+    return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
+
+
+def DiT_B_16(**kwargs):  # ours cfg
+    return DiT(depth=12,
+               hidden_size=768,
+               patch_size=16,
+               num_heads=12,
+               **kwargs)
+
+
+def DiT_S_2(**kwargs):
+    return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
+
+
+def DiT_S_4(**kwargs):
+    return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
+
+
+def DiT_S_8(**kwargs):
+    return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
+
+
+DiT_models = {
+    'DiT-XL/2': DiT_XL_2,
+    'DiT-XL/4': DiT_XL_4,
+    'DiT-XL/8': DiT_XL_8,
+    'DiT-L/2': DiT_L_2,
+    'DiT-L/4': DiT_L_4,
+    'DiT-L/8': DiT_L_8,
+    'DiT-B/2': DiT_B_2,
+    'DiT-B/4': DiT_B_4,
+    'DiT-B/8': DiT_B_8,
+    'DiT-B/16': DiT_B_16,
+    'DiT-S/2': DiT_S_2,
+    'DiT-S/4': DiT_S_4,
+    'DiT-S/8': DiT_S_8,
+}
diff --git a/dit/dit_trilatent.py b/dit/dit_trilatent.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f71234856ea0172fc5b20b70f727668d40efa57
--- /dev/null
+++ b/dit/dit_trilatent.py
@@ -0,0 +1,536 @@
+import torch.nn as nn
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from pdb import set_trace as st
+from timm.models.vision_transformer import Mlp
+
+from ldm.modules.attention import MemoryEfficientCrossAttention
+from .dit_models_xformers import DiT, get_2d_sincos_pos_embed, DiTBlock, FinalLayer, t2i_modulate, PixelArtTextCondDiTBlock, T2IFinalLayer, approx_gelu
+
+from torch.nn import LayerNorm
+from vit.vit_triplane import XYZPosEmbed
+
+class DiT_TriLatent(DiT):
+    # DiT with 3D_aware operations
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        roll_out=False,
+        vit_blk=DiTBlock,
+        final_layer_blk=FinalLayer,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim, roll_out, vit_blk,
+                         final_layer_blk)
+
+        assert self.roll_out
+
+    def init_PE_3D_aware(self):
+
+        self.pos_embed = nn.Parameter(torch.zeros(
+            1, self.plane_n * self.x_embedder.num_patches, self.embed_dim),
+                                      requires_grad=False)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        p = int(self.x_embedder.num_patches**0.5)
+        D = self.pos_embed.shape[-1]
+        grid_size = (self.plane_n, p * p)  # B n HW C
+
+        pos_embed = get_2d_sincos_pos_embed(D, grid_size).reshape(
+            self.plane_n * p * p, D)  # H*W, D
+
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+    def initialize_weights(self):
+        super().initialize_weights()
+
+        # ! add 3d-aware PE
+        self.init_PE_3D_aware()
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert context is not None
+
+        t = self.t_embedder(timesteps)  # (N, D)
+
+        # if self.roll_out:  # !
+        x = rearrange(x, 'b (c n) h w->(b n) c h w',
+                      n=3)  # downsample with same conv
+        x = self.x_embedder(x)  # (b n) c h/f w/f
+
+        x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
+        x = x + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        # if self.roll_out:  # ! roll-out in the L dim, not B dim. add condition to all tokens.
+        # x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
+
+        # assert context.ndim == 2
+        if isinstance(context, dict):
+            context = context['crossattn']  # sgm conditioner compat
+
+        context = self.clip_text_proj(context)
+
+        # c = t + context
+        # else:
+        # c = t  # BS 1024
+
+        for blk_idx, block in enumerate(self.blocks):
+            # if self.roll_out:
+            if False:
+                if blk_idx % 2 == 0:  # with-in plane self attention
+                    x = rearrange(x, 'b (n l) c -> (b n) l c', n=3)
+                    x = block(x, repeat(t, 'b c -> (b n) c ', n=3), # TODO, calculate once
+                              repeat(context, 'b l c -> (b n) l c ', n=3))  # (N, T, D)
+
+                else:  # global attention
+                    x = rearrange(x, '(b n) l c -> b (n l) c ', n=self.plane_n)
+                    x = block(x, t, context)  # (N, T, D)
+            else:
+                x = block(x, t, context)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+            # x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32).contiguous()
+        # st()
+
+        return x
+
+
+class DiT_TriLatent_PixelArt(DiT_TriLatent):
+    # DiT with 3D_aware operations
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        roll_out=False,
+        vit_blk=DiTBlock,
+        final_layer_blk=FinalLayer,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim, roll_out, vit_blk,
+                         final_layer_blk)
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+        del self.clip_text_proj
+        self.cap_embedder = nn.Sequential( # TODO, init with zero here.
+            LayerNorm(context_dim),
+            nn.Linear(
+                context_dim,
+                hidden_size,
+            ),
+        )
+        nn.init.constant_(self.cap_embedder[-1].weight, 0)
+        nn.init.constant_(self.cap_embedder[-1].bias, 0)
+
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert context is not None
+
+        clip_cls_token = self.cap_embedder(context['vector']) # pooled
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        # if self.roll_out:  # !
+        x = rearrange(x, 'b (c n) h w->(b n) c h w',
+                      n=3)  # downsample with same conv
+        x = self.x_embedder(x)  # (b n) c h/f w/f
+
+        x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
+        x = x + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+
+        # if self.roll_out:  # ! roll-out in the L dim, not B dim. add condition to all tokens.
+        # x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
+
+        # assert context.ndim == 2
+        if isinstance(context, dict):
+            context = context['crossattn']  # sgm conditioner compat
+
+        # context = self.clip_text_proj(context) # ! with rmsnorm here for 
+
+        # c = t + context
+        # else:
+        # c = t  # BS 1024
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, context)  # (N, T, D)
+
+        # todo later
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
+
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        if self.roll_out:  # move n from L to B axis
+            x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
+            # x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32).contiguous()
+        # st()
+
+        return x
+
+    # ! compat issue
+    def forward_with_cfg(self, x, t, context, cfg_scale):
+        """
+        Forward pass of SiT, but also batches the unconSiTional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        # half = x[: len(x) // 2]
+        # combined = torch.cat([half, half], dim=0)
+        eps = self.forward(x, t, context)
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        # eps, rest = model_out[:, :3], model_out[:, 3:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0)
+        return eps
+
+
+# PCD, general single-stage model.
+class DiT_PCD_PixelArt(DiT_TriLatent_PixelArt):
+    # DiT with 3D_aware operations
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        roll_out=False,
+        vit_blk=PixelArtTextCondDiTBlock,
+        final_layer_blk=FinalLayer,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim, roll_out, vit_blk,
+                         final_layer_blk)
+        # an MLP to transform the input 19-dim feature to high-dim.
+        self.x_embedder = Mlp(in_features=in_channels,
+                          hidden_features=hidden_size,
+                          out_features=hidden_size,
+                          act_layer=approx_gelu,
+                          drop=0)
+        del self.pos_embed
+
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert context is not None
+
+        clip_cls_token = self.cap_embedder(context['caption_vector']) # pooled
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        x = self.x_embedder(x)
+
+        # assert context.ndim == 2
+        if isinstance(context, dict):
+            context = context['caption_crossattn']  # sgm conditioner compat
+
+        # loop dit block
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, context)  # (N, T, D)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+
+        # cast to float32 for better accuracy
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+# ! two-stage version, the second-stage here, for text pretraining.
+class DiT_PCD_PixelArt_tofeat(DiT_PCD_PixelArt):
+    # DiT with 3D_aware operations
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        mixing_logit_init=-3,
+        mixed_prediction=True,
+        context_dim=False,
+        roll_out=False,
+        vit_blk=DiTBlock,
+        final_layer_blk=FinalLayer,
+        use_pe_cond=True,
+    ):
+        super().__init__(input_size, patch_size, in_channels, hidden_size,
+                         depth, num_heads, mlp_ratio, class_dropout_prob,
+                         num_classes, learn_sigma, mixing_logit_init,
+                         mixed_prediction, context_dim, roll_out, PixelArtTextCondDiTBlock,
+                         final_layer_blk)
+
+        self.use_pe_cond = use_pe_cond
+        if use_pe_cond:
+            self.xyz_pos_embed = XYZPosEmbed(hidden_size)
+        else:
+            self.x_embedder = Mlp(in_features=in_channels+3,
+                            hidden_features=hidden_size,
+                            out_features=hidden_size,
+                            act_layer=approx_gelu,
+                            drop=0)
+
+
+    def forward(self,
+                x,
+                timesteps=None,
+                context=None,
+                y=None,
+                get_attr='',
+                **kwargs):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        # t = timesteps
+        assert isinstance(context, dict)
+
+        # dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
+
+        # t = self.t_embedder(timesteps)
+
+        clip_cls_token = self.cap_embedder(context['caption_vector']) # pooled
+        caption_crossattn, fps_xyz = context['caption_crossattn'], context['fps-xyz']
+
+        t = self.t_embedder(timesteps) + clip_cls_token  # (N, D)
+        t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
+
+        if self.use_pe_cond:
+            x = self.x_embedder(x) + self.xyz_pos_embed(fps_xyz) # point-wise addition
+        else: # use concat to add info
+            x = torch.cat([fps_xyz, x], dim=-1)
+            x = self.x_embedder(x)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_pre(x)
+
+        for blk_idx, block in enumerate(self.blocks):
+            x = block(x, t0, caption_crossattn)
+
+        # add a norm layer here, as in point-e
+        # x = self.ln_post(x)
+
+        x = self.final_layer(x, t) # no loss on the xyz side 
+
+        x = x.to(torch.float32).contiguous()
+
+        return x
+
+
+
+#################################################################################
+#                                   DiT_TriLatent Configs                                  #
+#################################################################################
+
+
+def DiT_XL_2(**kwargs):
+    return DiT_TriLatent(depth=28,
+                         hidden_size=1152,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_L_2(**kwargs):
+    return DiT_TriLatent(depth=24,
+                         hidden_size=1024,
+                         patch_size=2,
+                         num_heads=16,
+                         **kwargs)
+
+
+def DiT_B_2(**kwargs):
+    return DiT_TriLatent(depth=12,
+                         hidden_size=768,
+                         patch_size=2,
+                         num_heads=12,
+                         **kwargs)
+
+
+def DiT_B_1(**kwargs):
+    return DiT_TriLatent(depth=12,
+                         hidden_size=768,
+                         patch_size=1,
+                         num_heads=12,
+                         **kwargs)
+
+
+def DiT_B_Pixelart_2(**kwargs):
+    return DiT_TriLatent_PixelArt(depth=12,
+                         hidden_size=768,
+                         patch_size=2,
+                         num_heads=12,
+                        #  vit_blk=PixelArtTextCondDiTBlock,
+                         final_layer_blk=T2IFinalLayer,
+                         **kwargs)
+
+def DiT_L_Pixelart_2(**kwargs):
+    return DiT_TriLatent_PixelArt(depth=24,
+                         hidden_size=1024,
+                         patch_size=2,
+                         num_heads=16,
+                        #  vit_blk=PixelArtTextCondDiTBlock,
+                         final_layer_blk=T2IFinalLayer,
+                         **kwargs)
+
+
+# PCD-DiT
+def DiT_PCD_B(**kwargs):
+
+    return DiT_PCD_PixelArt(depth=12,
+                         hidden_size=768,
+                         patch_size=1,
+                         num_heads=12,
+                         **kwargs)
+
+def DiT_PCD_L(**kwargs):
+
+    return DiT_PCD_PixelArt(depth=24,
+                         hidden_size=1024,
+                         patch_size=1,
+                         num_heads=16,
+                         **kwargs)
+
+def DiT_PCD_B_tofeat(**kwargs):
+
+    return DiT_PCD_PixelArt_tofeat(depth=12,
+                         hidden_size=768,
+                         patch_size=1,
+                         num_heads=12,
+                         **kwargs)
+
+def DiT_PCD_L_tofeat(**kwargs):
+
+    return DiT_PCD_PixelArt_tofeat(depth=24,
+                         hidden_size=1024,
+                         patch_size=1,
+                         num_heads=16,
+                         **kwargs)
+
+def DiT_PCD_XL_tofeat(**kwargs):
+
+    return DiT_PCD_PixelArt_tofeat(depth=28,
+                         hidden_size=1152,
+                         patch_size=1,
+                         num_heads=16,
+                         **kwargs)
+
+
+
+
+DiT_models = {
+    'DiT-XL/2': DiT_XL_2,
+    'DiT-L/2': DiT_L_2,
+    'DiT-PixelArt-L/2': DiT_L_Pixelart_2,
+    'DiT-PixelArt-B/2': DiT_B_Pixelart_2,
+    'DiT-B/2': DiT_B_2,
+    'DiT-B/1': DiT_B_1,
+    'DiT-PCD-B': DiT_PCD_B,
+    'DiT-PCD-L': DiT_PCD_L,
+    'DiT-PCD-B-stage2-xyz2feat': DiT_PCD_B_tofeat,
+    'DiT-PCD-L-stage2-xyz2feat': DiT_PCD_L_tofeat,
+    'DiT-PCD-XL-stage2-xyz2feat': DiT_PCD_XL_tofeat,
+    # 'DiT-PCD-L-stage1-text': DiT_PCD_L_tofeat,
+}
diff --git a/dit/dit_wo_embedder.py b/dit/dit_wo_embedder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a40a05b2b071d9b564551d46b78bafe43156121
--- /dev/null
+++ b/dit/dit_wo_embedder.py
@@ -0,0 +1,439 @@
+import torch
+import torch.nn as nn
+# import numpy as np
+# import math
+# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
+
+from .dit_models import TimestepEmbedder, LabelEmbedder, DiTBlock, get_2d_sincos_pos_embed
+
+
+class DiTwoEmbedder(nn.Module):
+    """
+    Diffusion model with a Transformer backbone, performing directly on the ViT token latents rather than spatial latents.
+    """
+
+    def __init__(
+        self,
+        input_size=224,  # raw img input size
+        # patch_size=14, # dino version
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+    ):
+        super().__init__()
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = 14  # dino-v2 patch sized fixed in this project
+        self.num_heads = num_heads
+
+        # self.x_embedder = PatchEmbed(input_size,
+        #                              patch_size,
+        #                              in_channels,
+        #                              hidden_size,
+        #                              bias=True)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        if num_classes > 0:
+            self.y_embedder = LabelEmbedder(num_classes, hidden_size,
+                                            class_dropout_prob)
+        else:
+            self.y_embedder = None
+
+        # num_patches = self.x_embedder.num_patches # 14*14*3
+        self.num_patches = (input_size // self.patch_size)**2
+
+        # Will use fixed sin-cos embedding:
+        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches,
+                                                  hidden_size),
+                                      requires_grad=False)
+
+        self.blocks = nn.ModuleList([
+            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+            for _ in range(depth)
+        ])
+        # self.final_layer = FinalLayer(hidden_size, patch_size,
+        #                               self.out_channels)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1],
+                                            int(self.num_patches**0.5))
+        # st()
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        # w = self.x_embedder.proj.weight.data
+        # nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        # nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        # Initialize label embedding table:
+        if self.y_embedder is not None:
+            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        # nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        # nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        # nn.init.constant_(self.final_layer.linear.weight, 0)
+        # nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def forward(self, x, t, y=None):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+
+        # ! no embedder operation
+        # x = self.x_embedder(
+        #     x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        x = x + self.pos_embed
+
+        t = self.t_embedder(t)  # (N, D)
+
+        if self.y_embedder is not None:
+            assert y is not None
+            y = self.y_embedder(y, self.training)  # (N, D)
+            c = t + y  # (N, D)
+        else:
+            c = t
+
+        for block in self.blocks:
+            x = block(x, c)  # (N, T, D)
+
+        # x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
+        # x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        return x
+
+    def forward_with_cfg(self, x, t, y, cfg_scale):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        half = x[:len(x) // 2]
+        combined = torch.cat([half, half], dim=0)
+        model_out = self.forward(combined, t, y)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        eps, rest = model_out[:, :3], model_out[:, 3:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0)
+        return torch.cat([eps, rest], dim=1)
+
+    def forward_with_cfg_unconditional(self, x, t, y=None, cfg_scale=None):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+
+        combined = x
+        model_out = self.forward(combined, t, y)
+
+        return model_out
+
+
+class DiTwoEmbedderLongSkipConnection(nn.Module):
+
+    def __init__(
+        self,
+        input_size=224,  # raw img input size
+        patch_size=14,  # dino version
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+    ):
+        """DiT with long skip-connections from U-ViT, CVPR 23'
+        """
+        super().__init__()
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        if num_classes > 0:
+            self.y_embedder = LabelEmbedder(num_classes, hidden_size,
+                                            class_dropout_prob)
+        else:
+            self.y_embedder = None
+
+        # num_patches = self.x_embedder.num_patches # 14*14*3
+        self.num_patches = (input_size // patch_size)**2
+
+        # Will use fixed sin-cos embedding:
+        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches,
+                                                  hidden_size),
+                                      requires_grad=False)
+
+        self.blocks = nn.ModuleList([
+            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+            for _ in range(depth)
+        ])
+
+        # ! add long-skip-connections from U-ViT
+        self.in_blocks = nn.ModuleList([
+            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+            for _ in range(depth // 2)
+        ])
+
+        self.mid_block = DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+
+        self.out_blocks = nn.ModuleList([
+            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
+            for _ in range(depth // 2)
+        ])
+
+        # ! needed or to be replaced?
+        # self.final_layer = FinalLayer(hidden_size, patch_size,
+        #                               self.out_channels)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1],
+                                            int(self.num_patches**0.5))
+        # st()
+        self.pos_embed.data.copy_(
+            torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        # w = self.x_embedder.proj.weight.data
+        # nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        # nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        # Initialize label embedding table:
+        if self.y_embedder is not None:
+            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        # nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        # nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        # nn.init.constant_(self.final_layer.linear.weight, 0)
+        # nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def forward(self, x, t, y=None):
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+
+        # ! no embedder operation
+        # x = self.x_embedder(
+        #     x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        x = x + self.pos_embed
+
+        t = self.t_embedder(t)  # (N, D)
+
+        if self.y_embedder is not None:
+            assert y is not None
+            y = self.y_embedder(y, self.training)  # (N, D)
+            c = t + y  # (N, D)
+        else:
+            c = t
+
+        # ! add long-skip-connections here
+
+        # for block in self.blocks:
+        #     x = block(x, c)  # (N, T, D)
+
+        skips = []
+        for blk in self.in_blocks:
+            x = blk(x)
+            skips.append(x)
+
+        x = self.mid_block(x)
+
+        for blk in self.out_blocks:
+            x = blk(x, skips.pop())
+
+        # ! the order of unpatchify and final_linear swaps in the baseline implementation
+        # x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
+        # x = self.unpatchify(x)  # (N, out_channels, H, W)
+
+        return x
+
+    def forward_with_cfg(self, x, t, y, cfg_scale):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        half = x[:len(x) // 2]
+        combined = torch.cat([half, half], dim=0)
+        model_out = self.forward(combined, t, y)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        eps, rest = model_out[:, :3], model_out[:, 3:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0)
+        return torch.cat([eps, rest], dim=1)
+
+    def forward_with_cfg_unconditional(self, x, t, y=None, cfg_scale=None):
+        """
+        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+
+        combined = x
+        model_out = self.forward(combined, t, y)
+
+        return model_out
+
+
+#################################################################################
+#                                   DiT Configs                                  #
+#################################################################################
+
+# def DiT_XL_2(**kwargs):
+#     return DiT(depth=28,
+#                hidden_size=1152,
+#                patch_size=2,
+#                num_heads=16,
+#                **kwargs)
+
+# def DiT_XL_4(**kwargs):
+#     return DiT(depth=28,
+#                hidden_size=1152,
+#                patch_size=4,
+#                num_heads=16,
+#                **kwargs)
+
+# def DiT_XL_8(**kwargs):
+#     return DiT(depth=28,
+#                hidden_size=1152,
+#                patch_size=8,
+#                num_heads=16,
+#                **kwargs)
+
+# def DiT_L_2(**kwargs):
+#     return DiT(depth=24,
+#                hidden_size=1024,
+#                patch_size=2,
+#                num_heads=16,
+#                **kwargs)
+
+# def DiT_L_4(**kwargs):
+#     return DiT(depth=24,
+#                hidden_size=1024,
+#                patch_size=4,
+#                num_heads=16,
+#                **kwargs)
+
+# def DiT_L_8(**kwargs):
+#     return DiT(depth=24,
+#                hidden_size=1024,
+#                patch_size=8,
+#                num_heads=16,
+#                **kwargs)
+
+# def DiT_B_2(**kwargs):
+#     return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
+
+# def DiT_B_4(**kwargs):
+#     return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
+
+# def DiT_B_8(**kwargs):
+#     return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
+
+# def DiT_B_16(**kwargs): # ours cfg
+#     return DiT(depth=12, hidden_size=768, patch_size=16, num_heads=12, **kwargs)
+
+# def DiT_S_2(**kwargs):
+#     return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
+
+# def DiT_S_4(**kwargs):
+#     return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
+
+# def DiT_S_8(**kwargs):
+#     return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
+
+
+def DiT_woembed_S(**kwargs):
+    return DiTwoEmbedder(depth=12, hidden_size=384, num_heads=6, **kwargs)
+
+
+def DiT_woembed_B(**kwargs):
+    return DiTwoEmbedder(depth=12, hidden_size=768, num_heads=12, **kwargs)
+
+
+def DiT_woembed_L(**kwargs):
+    return DiTwoEmbedder(
+        depth=24,
+        hidden_size=1024,
+        num_heads=16,
+        **kwargs)
+
+
+DiT_woembed_models = {
+    # 'DiT-XL/2': DiT_XL_2,
+    # 'DiT-XL/4': DiT_XL_4,
+    # 'DiT-XL/8': DiT_XL_8,
+    # 'DiT-L/2': DiT_L_2,
+    # 'DiT-L/4': DiT_L_4,
+    # 'DiT-L/8': DiT_L_8,
+    # 'DiT-B/2': DiT_B_2,
+    # 'DiT-B/4': DiT_B_4,
+    # 'DiT-B/8': DiT_B_8,
+    # 'DiT-B/16': DiT_B_16,
+    # 'DiT-S/2': DiT_S_2,
+    # 'DiT-S/4': DiT_S_4,
+    # 'DiT-S/8': DiT_S_8,
+    'DiT-wo-S': DiT_woembed_S,
+    'DiT-wo-B': DiT_woembed_B,
+    'DiT-wo-L': DiT_woembed_L,
+}
diff --git a/dit/norm.py b/dit/norm.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a89bb44e3f66038ed1f960cb6c65379bd227cc0
--- /dev/null
+++ b/dit/norm.py
@@ -0,0 +1,40 @@
+import numbers
+from typing import Dict, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from pdb import set_trace as st
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True):
+        super().__init__()
+
+        self.eps = eps
+
+        if isinstance(dim, numbers.Integral):
+            dim = (dim,)
+
+        self.dim = torch.Size(dim)
+
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim))
+        else:
+            self.weight = None
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+
+        if self.weight is not None:
+            # convert into half-precision if necessary
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                hidden_states = hidden_states.to(self.weight.dtype)
+            hidden_states = hidden_states * self.weight
+        else:
+            hidden_states = hidden_states.to(input_dtype)
+
+        return hidden_states.to(input_dtype)
diff --git a/dnnlib/__init__.py b/dnnlib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd91ed142e955581e83948455fb71cd837215f61
--- /dev/null
+++ b/dnnlib/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+from .util import EasyDict, make_cache_dir_path
diff --git a/dnnlib/__pycache__/__init__.cpython-310.pyc b/dnnlib/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/dnnlib/__pycache__/__init__.cpython-39.pyc b/dnnlib/__pycache__/__init__.cpython-39.pyc
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diff --git a/dnnlib/__pycache__/util.cpython-310.pyc b/dnnlib/__pycache__/util.cpython-310.pyc
new file mode 100644
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diff --git a/dnnlib/__pycache__/util.cpython-39.pyc b/dnnlib/__pycache__/util.cpython-39.pyc
new file mode 100644
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diff --git a/dnnlib/util.py b/dnnlib/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..2430b83469eca9217de68e9ab3fa7cdec14bc5b4
--- /dev/null
+++ b/dnnlib/util.py
@@ -0,0 +1,590 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Miscellaneous utility classes and functions."""
+
+import ctypes
+import fnmatch
+import importlib
+import inspect
+import numpy as np
+import os
+import shutil
+import sys
+import types
+import io
+import pickle
+import re
+import requests
+import html
+import hashlib
+import glob
+import tempfile
+import urllib
+import urllib.request
+import uuid
+
+from distutils.util import strtobool
+from typing import Any, List, Tuple, Union
+
+import torch
+
+# Util classes
+# ------------------------------------------------------------------------------------------
+
+def calculate_adaptive_weight(recon_loss, g_loss, last_layer, disc_weight_max=1.0):
+    recon_grads = torch.autograd.grad(recon_loss, last_layer, retain_graph=True)[0]
+    g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+
+    d_weight = torch.norm(recon_grads) / (torch.norm(g_grads) + 1e-4)
+    d_weight = torch.clamp(d_weight, 0.0, disc_weight_max).detach()
+    return d_weight
+
+
+class EasyDict(dict):
+    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        self[name] = value
+
+    def __delattr__(self, name: str) -> None:
+        del self[name]
+
+
+class Logger(object):
+    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
+
+    def __init__(self,
+                 file_name: str = None,
+                 file_mode: str = "w",
+                 should_flush: bool = True):
+        self.file = None
+
+        if file_name is not None:
+            self.file = open(file_name, file_mode)
+
+        self.should_flush = should_flush
+        self.stdout = sys.stdout
+        self.stderr = sys.stderr
+
+        sys.stdout = self
+        sys.stderr = self
+
+    def __enter__(self) -> "Logger":
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self.close()
+
+    def write(self, text: Union[str, bytes]) -> None:
+        """Write text to stdout (and a file) and optionally flush."""
+        if isinstance(text, bytes):
+            text = text.decode()
+        if len(
+                text
+        ) == 0:  # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
+            return
+
+        if self.file is not None:
+            self.file.write(text)
+
+        self.stdout.write(text)
+
+        if self.should_flush:
+            self.flush()
+
+    def flush(self) -> None:
+        """Flush written text to both stdout and a file, if open."""
+        if self.file is not None:
+            self.file.flush()
+
+        self.stdout.flush()
+
+    def close(self) -> None:
+        """Flush, close possible files, and remove stdout/stderr mirroring."""
+        self.flush()
+
+        # if using multiple loggers, prevent closing in wrong order
+        if sys.stdout is self:
+            sys.stdout = self.stdout
+        if sys.stderr is self:
+            sys.stderr = self.stderr
+
+        if self.file is not None:
+            self.file.close()
+            self.file = None
+
+
+# Cache directories
+# ------------------------------------------------------------------------------------------
+
+_dnnlib_cache_dir = None
+
+
+def set_cache_dir(path: str) -> None:
+    global _dnnlib_cache_dir
+    _dnnlib_cache_dir = path
+
+
+def make_cache_dir_path(*paths: str) -> str:
+    if _dnnlib_cache_dir is not None:
+        return os.path.join(_dnnlib_cache_dir, *paths)
+    if 'DNNLIB_CACHE_DIR' in os.environ:
+        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
+    if 'HOME' in os.environ:
+        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
+    if 'USERPROFILE' in os.environ:
+        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib',
+                            *paths)
+    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
+
+
+# Small util functions
+# ------------------------------------------------------------------------------------------
+
+
+def format_time(seconds: Union[int, float]) -> str:
+    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
+    s = int(np.rint(seconds))
+
+    if s < 60:
+        return "{0}s".format(s)
+    elif s < 60 * 60:
+        return "{0}m {1:02}s".format(s // 60, s % 60)
+    elif s < 24 * 60 * 60:
+        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60,
+                                             s % 60)
+    else:
+        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60),
+                                             (s // (60 * 60)) % 24,
+                                             (s // 60) % 60)
+
+
+def format_time_brief(seconds: Union[int, float]) -> str:
+    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
+    s = int(np.rint(seconds))
+
+    if s < 60:
+        return "{0}s".format(s)
+    elif s < 60 * 60:
+        return "{0}m {1:02}s".format(s // 60, s % 60)
+    elif s < 24 * 60 * 60:
+        return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60)
+    else:
+        return "{0}d {1:02}h".format(s // (24 * 60 * 60),
+                                     (s // (60 * 60)) % 24)
+
+
+def ask_yes_no(question: str) -> bool:
+    """Ask the user the question until the user inputs a valid answer."""
+    while True:
+        try:
+            print("{0} [y/n]".format(question))
+            return strtobool(input().lower())
+        except ValueError:
+            pass
+
+
+def tuple_product(t: Tuple) -> Any:
+    """Calculate the product of the tuple elements."""
+    result = 1
+
+    for v in t:
+        result *= v
+
+    return result
+
+
+_str_to_ctype = {
+    "uint8": ctypes.c_ubyte,
+    "uint16": ctypes.c_uint16,
+    "uint32": ctypes.c_uint32,
+    "uint64": ctypes.c_uint64,
+    "int8": ctypes.c_byte,
+    "int16": ctypes.c_int16,
+    "int32": ctypes.c_int32,
+    "int64": ctypes.c_int64,
+    "float32": ctypes.c_float,
+    "float64": ctypes.c_double
+}
+
+
+def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
+    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
+    type_str = None
+
+    if isinstance(type_obj, str):
+        type_str = type_obj
+    elif hasattr(type_obj, "__name__"):
+        type_str = type_obj.__name__
+    elif hasattr(type_obj, "name"):
+        type_str = type_obj.name
+    else:
+        raise RuntimeError("Cannot infer type name from input")
+
+    assert type_str in _str_to_ctype.keys()
+
+    my_dtype = np.dtype(type_str)
+    my_ctype = _str_to_ctype[type_str]
+
+    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
+
+    return my_dtype, my_ctype
+
+
+def is_pickleable(obj: Any) -> bool:
+    try:
+        with io.BytesIO() as stream:
+            pickle.dump(obj, stream)
+        return True
+    except:
+        return False
+
+
+# Functionality to import modules/objects by name, and call functions by name
+# ------------------------------------------------------------------------------------------
+
+
+def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
+    """Searches for the underlying module behind the name to some python object.
+    Returns the module and the object name (original name with module part removed)."""
+
+    # allow convenience shorthands, substitute them by full names
+    obj_name = re.sub("^np.", "numpy.", obj_name)
+    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
+
+    # list alternatives for (module_name, local_obj_name)
+    parts = obj_name.split(".")
+    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:]))
+                  for i in range(len(parts), 0, -1)]
+
+    # try each alternative in turn
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(
+                module_name)  # may raise ImportError
+            get_obj_from_module(module,
+                                local_obj_name)  # may raise AttributeError
+            return module, local_obj_name
+        except:
+            pass
+
+    # maybe some of the modules themselves contain errors?
+    for module_name, _local_obj_name in name_pairs:
+        try:
+            importlib.import_module(module_name)  # may raise ImportError
+        except ImportError:
+            if not str(sys.exc_info()[1]).startswith("No module named '" +
+                                                     module_name + "'"):
+                raise
+
+    # maybe the requested attribute is missing?
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(
+                module_name)  # may raise ImportError
+            get_obj_from_module(module,
+                                local_obj_name)  # may raise AttributeError
+        except ImportError:
+            pass
+
+    # we are out of luck, but we have no idea why
+    raise ImportError(obj_name)
+
+
+def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
+    """Traverses the object name and returns the last (rightmost) python object."""
+    if obj_name == '':
+        return module
+    obj = module
+    for part in obj_name.split("."):
+        obj = getattr(obj, part)
+    return obj
+
+
+def get_obj_by_name(name: str) -> Any:
+    """Finds the python object with the given name."""
+    module, obj_name = get_module_from_obj_name(name)
+    return get_obj_from_module(module, obj_name)
+
+
+def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
+    """Finds the python object with the given name and calls it as a function."""
+    assert func_name is not None
+    func_obj = get_obj_by_name(func_name)
+    assert callable(func_obj)
+    return func_obj(*args, **kwargs)
+
+
+def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
+    """Finds the python class with the given name and constructs it with the given arguments."""
+    return call_func_by_name(*args, func_name=class_name, **kwargs)
+
+
+def get_module_dir_by_obj_name(obj_name: str) -> str:
+    """Get the directory path of the module containing the given object name."""
+    module, _ = get_module_from_obj_name(obj_name)
+    return os.path.dirname(inspect.getfile(module))
+
+
+def is_top_level_function(obj: Any) -> bool:
+    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
+    return callable(obj) and obj.__name__ in sys.modules[
+        obj.__module__].__dict__
+
+
+def get_top_level_function_name(obj: Any) -> str:
+    """Return the fully-qualified name of a top-level function."""
+    assert is_top_level_function(obj)
+    module = obj.__module__
+    if module == '__main__':
+        module = os.path.splitext(
+            os.path.basename(sys.modules[module].__file__))[0]
+    return module + "." + obj.__name__
+
+
+# File system helpers
+# ------------------------------------------------------------------------------------------
+
+
+def list_dir_recursively_with_ignore(
+        dir_path: str,
+        ignores: List[str] = None,
+        add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
+    """List all files recursively in a given directory while ignoring given file and directory names.
+    Returns list of tuples containing both absolute and relative paths."""
+    assert os.path.isdir(dir_path)
+    base_name = os.path.basename(os.path.normpath(dir_path))
+
+    if ignores is None:
+        ignores = []
+
+    result = []
+
+    for root, dirs, files in os.walk(dir_path, topdown=True):
+        for ignore_ in ignores:
+            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
+
+            # dirs need to be edited in-place
+            for d in dirs_to_remove:
+                dirs.remove(d)
+
+            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
+
+        absolute_paths = [os.path.join(root, f) for f in files]
+        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
+
+        if add_base_to_relative:
+            relative_paths = [
+                os.path.join(base_name, p) for p in relative_paths
+            ]
+
+        assert len(absolute_paths) == len(relative_paths)
+        result += zip(absolute_paths, relative_paths)
+
+    return result
+
+
+def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
+    """Takes in a list of tuples of (src, dst) paths and copies files.
+    Will create all necessary directories."""
+    for file in files:
+        target_dir_name = os.path.dirname(file[1])
+
+        # will create all intermediate-level directories
+        if not os.path.exists(target_dir_name):
+            os.makedirs(target_dir_name)
+
+        shutil.copyfile(file[0], file[1])
+
+
+# URL helpers
+# ------------------------------------------------------------------------------------------
+
+
+def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
+    """Determine whether the given object is a valid URL string."""
+    if not isinstance(obj, str) or not "://" in obj:
+        return False
+    if allow_file_urls and obj.startswith('file://'):
+        return True
+    try:
+        res = requests.compat.urlparse(obj)
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+    except:
+        return False
+    return True
+
+
+def open_url(url: str,
+             cache_dir: str = None,
+             num_attempts: int = 10,
+             verbose: bool = True,
+             return_filename: bool = False,
+             cache: bool = True) -> Any:
+    """Download the given URL and return a binary-mode file object to access the data."""
+    assert num_attempts >= 1
+    assert not (return_filename and (not cache))
+
+    # Doesn't look like an URL scheme so interpret it as a local filename.
+    if not re.match('^[a-z]+://', url):
+        return url if return_filename else open(url, "rb")
+
+    # Handle file URLs.  This code handles unusual file:// patterns that
+    # arise on Windows:
+    #
+    # file:///c:/foo.txt
+    #
+    # which would translate to a local '/c:/foo.txt' filename that's
+    # invalid.  Drop the forward slash for such pathnames.
+    #
+    # If you touch this code path, you should test it on both Linux and
+    # Windows.
+    #
+    # Some internet resources suggest using urllib.request.url2pathname() but
+    # but that converts forward slashes to backslashes and this causes
+    # its own set of problems.
+    if url.startswith('file://'):
+        filename = urllib.parse.urlparse(url).path
+        if re.match(r'^/[a-zA-Z]:', filename):
+            filename = filename[1:]
+        return filename if return_filename else open(filename, "rb")
+
+    assert is_url(url)
+
+    # Lookup from cache.
+    if cache_dir is None:
+        cache_dir = make_cache_dir_path('downloads')
+
+    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
+    if cache:
+        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
+        if len(cache_files) == 1:
+            filename = cache_files[0]
+            return filename if return_filename else open(filename, "rb")
+
+    # Download.
+    url_name = None
+    url_data = None
+    with requests.Session() as session:
+        if verbose:
+            print("Downloading %s ..." % url, end="", flush=True)
+        for attempts_left in reversed(range(num_attempts)):
+            try:
+                with session.get(url) as res:
+                    res.raise_for_status()
+                    if len(res.content) == 0:
+                        raise IOError("No data received")
+
+                    if len(res.content) < 8192:
+                        content_str = res.content.decode("utf-8")
+                        if "download_warning" in res.headers.get(
+                                "Set-Cookie", ""):
+                            links = [
+                                html.unescape(link)
+                                for link in content_str.split('"')
+                                if "export=download" in link
+                            ]
+                            if len(links) == 1:
+                                url = requests.compat.urljoin(url, links[0])
+                                raise IOError("Google Drive virus checker nag")
+                        if "Google Drive - Quota exceeded" in content_str:
+                            raise IOError(
+                                "Google Drive download quota exceeded -- please try again later"
+                            )
+
+                    match = re.search(
+                        r'filename="([^"]*)"',
+                        res.headers.get("Content-Disposition", ""))
+                    url_name = match[1] if match else url
+                    url_data = res.content
+                    if verbose:
+                        print(" done")
+                    break
+            except KeyboardInterrupt:
+                raise
+            except:
+                if not attempts_left:
+                    if verbose:
+                        print(" failed")
+                    raise
+                if verbose:
+                    print(".", end="", flush=True)
+
+    # Save to cache.
+    if cache:
+        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
+        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
+        temp_file = os.path.join(
+            cache_dir,
+            "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
+        os.makedirs(cache_dir, exist_ok=True)
+        with open(temp_file, "wb") as f:
+            f.write(url_data)
+        os.replace(temp_file, cache_file)  # atomic
+        if return_filename:
+            return cache_file
+
+    # Return data as file object.
+    assert not return_filename
+    return io.BytesIO(url_data)
+
+class InfiniteSampler(torch.utils.data.Sampler):
+
+    def __init__(self,
+                 dataset,
+                 rank=0,
+                 num_replicas=1,
+                 shuffle=True,
+                 seed=0,
+                 window_size=0.5):
+        assert len(dataset) > 0
+        assert num_replicas > 0
+        assert 0 <= rank < num_replicas
+        assert 0 <= window_size <= 1
+        super().__init__(dataset)
+        self.dataset = dataset
+        self.rank = rank
+        self.num_replicas = num_replicas
+        self.shuffle = shuffle
+        self.seed = seed
+        self.window_size = window_size
+
+    def __iter__(self):
+        order = np.arange(len(self.dataset))
+        rnd = None
+        window = 0
+        if self.shuffle:
+            rnd = np.random.RandomState(self.seed)
+            rnd.shuffle(order)
+            window = int(np.rint(order.size * self.window_size))
+
+        idx = 0
+        while True:
+            i = idx % order.size
+            if idx % self.num_replicas == self.rank:
+                yield order[i]
+            if window >= 2:
+                j = (i - rnd.randint(window)) % order.size
+                order[i], order[j] = order[j], order[i]
+            idx += 1
+
+def requires_grad(model, flag=True):
+    for p in model.parameters():
+        p.requires_grad = flag
diff --git a/eval_pose.pt b/eval_pose.pt
new file mode 100644
index 0000000000000000000000000000000000000000..cd61c56d751cf1bc695a8f8e6ed55e67bd13c24d
--- /dev/null
+++ b/eval_pose.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09b6e0eb22da1ca5d1e4a4fd1bfe5b08d65d1b1926621aa22601b67f20904f9a
+size 4721
diff --git a/evaluations/README.md b/evaluations/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6ad0ab6c0b3982ad60950df7ffa9af5662d31b2b
--- /dev/null
+++ b/evaluations/README.md
@@ -0,0 +1,72 @@
+# Evaluations
+
+To compare different generative models, we use FID, sFID, Precision, Recall, and Inception Score. These metrics can all be calculated using batches of samples, which we store in `.npz` (numpy) files.
+
+# Download batches
+
+We provide pre-computed sample batches for the reference datasets, our diffusion models, and several baselines we compare against. These are all stored in `.npz` format.
+
+Reference dataset batches contain pre-computed statistics over the whole dataset, as well as 10,000 images for computing Precision and Recall. All other batches contain 50,000 images which can be used to compute statistics and Precision/Recall.
+
+Here are links to download all of the sample and reference batches:
+
+ * LSUN
+   * LSUN bedroom: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/VIRTUAL_lsun_bedroom256.npz)
+     * [ADM (dropout)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/admnet_dropout_lsun_bedroom.npz)
+     * [DDPM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/ddpm_lsun_bedroom.npz)
+     * [IDDPM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/iddpm_lsun_bedroom.npz)
+     * [StyleGAN](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/stylegan_lsun_bedroom.npz)
+   * LSUN cat: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/cat/VIRTUAL_lsun_cat256.npz)
+     * [ADM (dropout)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/cat/admnet_dropout_lsun_cat.npz)
+     * [StyleGAN2](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/cat/stylegan2_lsun_cat.npz)
+   * LSUN horse: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/horse/VIRTUAL_lsun_horse256.npz)
+     * [ADM (dropout)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/horse/admnet_dropout_lsun_horse.npz)
+     * [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/horse/admnet_lsun_horse.npz)
+
+ * ImageNet
+   * ImageNet 64x64: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/VIRTUAL_imagenet64_labeled.npz)
+     * [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/admnet_imagenet64.npz)
+     * [IDDPM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/iddpm_imagenet64.npz)
+     * [BigGAN](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/biggan_deep_imagenet64.npz)
+   * ImageNet 128x128: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/VIRTUAL_imagenet128_labeled.npz)
+     * [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/admnet_imagenet128.npz)
+     * [ADM-G](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/admnet_guided_imagenet128.npz)
+     * [ADM-G, 25 steps](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/admnet_guided_25step_imagenet128.npz)
+     * [BigGAN-deep (trunc=1.0)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/biggan_deep_trunc1_imagenet128.npz)
+   * ImageNet 256x256: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz)
+     * [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_imagenet256.npz)
+     * [ADM-G](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_guided_imagenet256.npz)
+     * [ADM-G, 25 step](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_guided_25step_imagenet256.npz)
+     * [ADM-G + ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_guided_upsampled_imagenet256.npz)
+     * [ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_upsampled_imagenet256.npz)
+     * [BigGAN-deep (trunc=1.0)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/biggan_deep_trunc1_imagenet256.npz)
+   * ImageNet 512x512: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/VIRTUAL_imagenet512.npz)
+     * [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_imagenet512.npz)
+     * [ADM-G](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_guided_imagenet512.npz)
+     * [ADM-G, 25 step](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_guided_25step_imagenet512.npz)
+     * [ADM-G + ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_guided_upsampled_imagenet512.npz)
+     * [ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_upsampled_imagenet512.npz)
+     * [BigGAN-deep (trunc=1.0)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/biggan_deep_trunc1_imagenet512.npz)
+
+# Run evaluations
+
+First, generate or download a batch of samples and download the corresponding reference batch for the given dataset. For this example, we'll use ImageNet 256x256, so the refernce batch is `VIRTUAL_imagenet256_labeled.npz` and we can use the sample batch `admnet_guided_upsampled_imagenet256.npz`.
+
+Next, run the `evaluator.py` script. The requirements of this script can be found in [requirements.txt](requirements.txt). Pass two arguments to the script: the reference batch and the sample batch. The script will download the InceptionV3 model used for evaluations into the current working directory (if it is not already present). This file is roughly 100MB.
+
+The output of the script will look something like this, where the first `...` is a bunch of verbose TensorFlow logging:
+
+```
+$ python evaluator.py VIRTUAL_imagenet256_labeled.npz admnet_guided_upsampled_imagenet256.npz
+...
+computing reference batch activations...
+computing/reading reference batch statistics...
+computing sample batch activations...
+computing/reading sample batch statistics...
+Computing evaluations...
+Inception Score: 215.8370361328125
+FID: 3.9425574129223264
+sFID: 6.140433703346162
+Precision: 0.8265
+Recall: 0.5309
+```
diff --git a/evaluations/evaluator.py b/evaluations/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..9590855d564dc94b9b779027a7eae3e3659dd215
--- /dev/null
+++ b/evaluations/evaluator.py
@@ -0,0 +1,653 @@
+import argparse
+import io
+import os
+import random
+import warnings
+import zipfile
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from functools import partial
+from multiprocessing import cpu_count
+from multiprocessing.pool import ThreadPool
+from typing import Iterable, Optional, Tuple
+
+import numpy as np
+import requests
+import tensorflow.compat.v1 as tf
+from scipy import linalg
+from tqdm.auto import tqdm
+
+INCEPTION_V3_URL = "https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/classify_image_graph_def.pb"
+INCEPTION_V3_PATH = "classify_image_graph_def.pb"
+
+FID_POOL_NAME = "pool_3:0"
+FID_SPATIAL_NAME = "mixed_6/conv:0"
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("ref_batch", help="path to reference batch npz file")
+    parser.add_argument("sample_batch", help="path to sample batch npz file")
+    args = parser.parse_args()
+
+    config = tf.ConfigProto(
+        allow_soft_placement=True  # allows DecodeJpeg to run on CPU in Inception graph
+    )
+    config.gpu_options.allow_growth = True
+    evaluator = Evaluator(tf.Session(config=config))
+
+    print("warming up TensorFlow...")
+    # This will cause TF to print a bunch of verbose stuff now rather
+    # than after the next print(), to help prevent confusion.
+    evaluator.warmup()
+
+    print("computing reference batch activations...")
+    ref_acts = evaluator.read_activations(args.ref_batch)
+    print("computing/reading reference batch statistics...")
+    ref_stats, ref_stats_spatial = evaluator.read_statistics(args.ref_batch, ref_acts)
+
+    print("computing sample batch activations...")
+    sample_acts = evaluator.read_activations(args.sample_batch)
+    print("computing/reading sample batch statistics...")
+    sample_stats, sample_stats_spatial = evaluator.read_statistics(args.sample_batch, sample_acts)
+
+    print("Computing evaluations...")
+    print("Inception Score:", evaluator.compute_inception_score(sample_acts[0]))
+    print("FID:", sample_stats.frechet_distance(ref_stats))
+    print("sFID:", sample_stats_spatial.frechet_distance(ref_stats_spatial))
+    prec, recall = evaluator.compute_prec_recall(ref_acts[0], sample_acts[0])
+    print("Precision:", prec)
+    print("Recall:", recall)
+
+
+class InvalidFIDException(Exception):
+    pass
+
+
+class FIDStatistics:
+    def __init__(self, mu: np.ndarray, sigma: np.ndarray):
+        self.mu = mu
+        self.sigma = sigma
+
+    def frechet_distance(self, other, eps=1e-6):
+        """
+        Compute the Frechet distance between two sets of statistics.
+        """
+        # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L132
+        mu1, sigma1 = self.mu, self.sigma
+        mu2, sigma2 = other.mu, other.sigma
+
+        mu1 = np.atleast_1d(mu1)
+        mu2 = np.atleast_1d(mu2)
+
+        sigma1 = np.atleast_2d(sigma1)
+        sigma2 = np.atleast_2d(sigma2)
+
+        assert (
+            mu1.shape == mu2.shape
+        ), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}"
+        assert (
+            sigma1.shape == sigma2.shape
+        ), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}"
+
+        diff = mu1 - mu2
+
+        # product might be almost singular
+        covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+        if not np.isfinite(covmean).all():
+            msg = (
+                "fid calculation produces singular product; adding %s to diagonal of cov estimates"
+                % eps
+            )
+            warnings.warn(msg)
+            offset = np.eye(sigma1.shape[0]) * eps
+            covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+        # numerical error might give slight imaginary component
+        if np.iscomplexobj(covmean):
+            if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+                m = np.max(np.abs(covmean.imag))
+                raise ValueError("Imaginary component {}".format(m))
+            covmean = covmean.real
+
+        tr_covmean = np.trace(covmean)
+
+        return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+
+
+class Evaluator:
+    def __init__(
+        self,
+        session,
+        batch_size=64,
+        softmax_batch_size=512,
+    ):
+        self.sess = session
+        self.batch_size = batch_size
+        self.softmax_batch_size = softmax_batch_size
+        self.manifold_estimator = ManifoldEstimator(session)
+        with self.sess.graph.as_default():
+            self.image_input = tf.placeholder(tf.float32, shape=[None, None, None, 3])
+            self.softmax_input = tf.placeholder(tf.float32, shape=[None, 2048])
+            self.pool_features, self.spatial_features = _create_feature_graph(self.image_input)
+            self.softmax = _create_softmax_graph(self.softmax_input)
+
+    def warmup(self):
+        self.compute_activations(np.zeros([1, 8, 64, 64, 3]))
+
+    def read_activations(self, npz_path: str) -> Tuple[np.ndarray, np.ndarray]:
+        with open_npz_array(npz_path, "arr_0") as reader:
+            return self.compute_activations(reader.read_batches(self.batch_size))
+
+    def compute_activations(self, batches: Iterable[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute image features for downstream evals.
+
+        :param batches: a iterator over NHWC numpy arrays in [0, 255].
+        :return: a tuple of numpy arrays of shape [N x X], where X is a feature
+                 dimension. The tuple is (pool_3, spatial).
+        """
+        preds = []
+        spatial_preds = []
+        for batch in tqdm(batches):
+            batch = batch.astype(np.float32)
+            pred, spatial_pred = self.sess.run(
+                [self.pool_features, self.spatial_features], {self.image_input: batch}
+            )
+            preds.append(pred.reshape([pred.shape[0], -1]))
+            spatial_preds.append(spatial_pred.reshape([spatial_pred.shape[0], -1]))
+        return (
+            np.concatenate(preds, axis=0),
+            np.concatenate(spatial_preds, axis=0),
+        )
+
+    def read_statistics(
+        self, npz_path: str, activations: Tuple[np.ndarray, np.ndarray]
+    ) -> Tuple[FIDStatistics, FIDStatistics]:
+        obj = np.load(npz_path)
+        if "mu" in list(obj.keys()):
+            return FIDStatistics(obj["mu"], obj["sigma"]), FIDStatistics(
+                obj["mu_s"], obj["sigma_s"]
+            )
+        return tuple(self.compute_statistics(x) for x in activations)
+
+    def compute_statistics(self, activations: np.ndarray) -> FIDStatistics:
+        mu = np.mean(activations, axis=0)
+        sigma = np.cov(activations, rowvar=False)
+        return FIDStatistics(mu, sigma)
+
+    def compute_inception_score(self, activations: np.ndarray, split_size: int = 5000) -> float:
+        softmax_out = []
+        for i in range(0, len(activations), self.softmax_batch_size):
+            acts = activations[i : i + self.softmax_batch_size]
+            softmax_out.append(self.sess.run(self.softmax, feed_dict={self.softmax_input: acts}))
+        preds = np.concatenate(softmax_out, axis=0)
+        # https://github.com/openai/improved-gan/blob/4f5d1ec5c16a7eceb206f42bfc652693601e1d5c/inception_score/model.py#L46
+        scores = []
+        for i in range(0, len(preds), split_size):
+            part = preds[i : i + split_size]
+            kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+            kl = np.mean(np.sum(kl, 1))
+            scores.append(np.exp(kl))
+        return float(np.mean(scores))
+
+    def compute_prec_recall(
+        self, activations_ref: np.ndarray, activations_sample: np.ndarray
+    ) -> Tuple[float, float]:
+        radii_1 = self.manifold_estimator.manifold_radii(activations_ref)
+        radii_2 = self.manifold_estimator.manifold_radii(activations_sample)
+        pr = self.manifold_estimator.evaluate_pr(
+            activations_ref, radii_1, activations_sample, radii_2
+        )
+        return (float(pr[0][0]), float(pr[1][0]))
+
+
+class ManifoldEstimator:
+    """
+    A helper for comparing manifolds of feature vectors.
+
+    Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L57
+    """
+
+    def __init__(
+        self,
+        session,
+        row_batch_size=10000,
+        col_batch_size=10000,
+        nhood_sizes=(3,),
+        clamp_to_percentile=None,
+        eps=1e-5,
+    ):
+        """
+        Estimate the manifold of given feature vectors.
+
+        :param session: the TensorFlow session.
+        :param row_batch_size: row batch size to compute pairwise distances
+                               (parameter to trade-off between memory usage and performance).
+        :param col_batch_size: column batch size to compute pairwise distances.
+        :param nhood_sizes: number of neighbors used to estimate the manifold.
+        :param clamp_to_percentile: prune hyperspheres that have radius larger than
+                                    the given percentile.
+        :param eps: small number for numerical stability.
+        """
+        self.distance_block = DistanceBlock(session)
+        self.row_batch_size = row_batch_size
+        self.col_batch_size = col_batch_size
+        self.nhood_sizes = nhood_sizes
+        self.num_nhoods = len(nhood_sizes)
+        self.clamp_to_percentile = clamp_to_percentile
+        self.eps = eps
+
+    def warmup(self):
+        feats, radii = (
+            np.zeros([1, 2048], dtype=np.float32),
+            np.zeros([1, 1], dtype=np.float32),
+        )
+        self.evaluate_pr(feats, radii, feats, radii)
+
+    def manifold_radii(self, features: np.ndarray) -> np.ndarray:
+        num_images = len(features)
+
+        # Estimate manifold of features by calculating distances to k-NN of each sample.
+        radii = np.zeros([num_images, self.num_nhoods], dtype=np.float32)
+        distance_batch = np.zeros([self.row_batch_size, num_images], dtype=np.float32)
+        seq = np.arange(max(self.nhood_sizes) + 1, dtype=np.int32)
+
+        for begin1 in range(0, num_images, self.row_batch_size):
+            end1 = min(begin1 + self.row_batch_size, num_images)
+            row_batch = features[begin1:end1]
+
+            for begin2 in range(0, num_images, self.col_batch_size):
+                end2 = min(begin2 + self.col_batch_size, num_images)
+                col_batch = features[begin2:end2]
+
+                # Compute distances between batches.
+                distance_batch[
+                    0 : end1 - begin1, begin2:end2
+                ] = self.distance_block.pairwise_distances(row_batch, col_batch)
+
+            # Find the k-nearest neighbor from the current batch.
+            radii[begin1:end1, :] = np.concatenate(
+                [
+                    x[:, self.nhood_sizes]
+                    for x in _numpy_partition(distance_batch[0 : end1 - begin1, :], seq, axis=1)
+                ],
+                axis=0,
+            )
+
+        if self.clamp_to_percentile is not None:
+            max_distances = np.percentile(radii, self.clamp_to_percentile, axis=0)
+            radii[radii > max_distances] = 0
+        return radii
+
+    def evaluate(self, features: np.ndarray, radii: np.ndarray, eval_features: np.ndarray):
+        """
+        Evaluate if new feature vectors are at the manifold.
+        """
+        num_eval_images = eval_features.shape[0]
+        num_ref_images = radii.shape[0]
+        distance_batch = np.zeros([self.row_batch_size, num_ref_images], dtype=np.float32)
+        batch_predictions = np.zeros([num_eval_images, self.num_nhoods], dtype=np.int32)
+        max_realism_score = np.zeros([num_eval_images], dtype=np.float32)
+        nearest_indices = np.zeros([num_eval_images], dtype=np.int32)
+
+        for begin1 in range(0, num_eval_images, self.row_batch_size):
+            end1 = min(begin1 + self.row_batch_size, num_eval_images)
+            feature_batch = eval_features[begin1:end1]
+
+            for begin2 in range(0, num_ref_images, self.col_batch_size):
+                end2 = min(begin2 + self.col_batch_size, num_ref_images)
+                ref_batch = features[begin2:end2]
+
+                distance_batch[
+                    0 : end1 - begin1, begin2:end2
+                ] = self.distance_block.pairwise_distances(feature_batch, ref_batch)
+
+            # From the minibatch of new feature vectors, determine if they are in the estimated manifold.
+            # If a feature vector is inside a hypersphere of some reference sample, then
+            # the new sample lies at the estimated manifold.
+            # The radii of the hyperspheres are determined from distances of neighborhood size k.
+            samples_in_manifold = distance_batch[0 : end1 - begin1, :, None] <= radii
+            batch_predictions[begin1:end1] = np.any(samples_in_manifold, axis=1).astype(np.int32)
+
+            max_realism_score[begin1:end1] = np.max(
+                radii[:, 0] / (distance_batch[0 : end1 - begin1, :] + self.eps), axis=1
+            )
+            nearest_indices[begin1:end1] = np.argmin(distance_batch[0 : end1 - begin1, :], axis=1)
+
+        return {
+            "fraction": float(np.mean(batch_predictions)),
+            "batch_predictions": batch_predictions,
+            "max_realisim_score": max_realism_score,
+            "nearest_indices": nearest_indices,
+        }
+
+    def evaluate_pr(
+        self,
+        features_1: np.ndarray,
+        radii_1: np.ndarray,
+        features_2: np.ndarray,
+        radii_2: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Evaluate precision and recall efficiently.
+
+        :param features_1: [N1 x D] feature vectors for reference batch.
+        :param radii_1: [N1 x K1] radii for reference vectors.
+        :param features_2: [N2 x D] feature vectors for the other batch.
+        :param radii_2: [N x K2] radii for other vectors.
+        :return: a tuple of arrays for (precision, recall):
+                 - precision: an np.ndarray of length K1
+                 - recall: an np.ndarray of length K2
+        """
+        features_1_status = np.zeros([len(features_1), radii_2.shape[1]], dtype=np.bool)
+        features_2_status = np.zeros([len(features_2), radii_1.shape[1]], dtype=np.bool)
+        for begin_1 in range(0, len(features_1), self.row_batch_size):
+            end_1 = begin_1 + self.row_batch_size
+            batch_1 = features_1[begin_1:end_1]
+            for begin_2 in range(0, len(features_2), self.col_batch_size):
+                end_2 = begin_2 + self.col_batch_size
+                batch_2 = features_2[begin_2:end_2]
+                batch_1_in, batch_2_in = self.distance_block.less_thans(
+                    batch_1, radii_1[begin_1:end_1], batch_2, radii_2[begin_2:end_2]
+                )
+                features_1_status[begin_1:end_1] |= batch_1_in
+                features_2_status[begin_2:end_2] |= batch_2_in
+        return (
+            np.mean(features_2_status.astype(np.float64), axis=0),
+            np.mean(features_1_status.astype(np.float64), axis=0),
+        )
+
+
+class DistanceBlock:
+    """
+    Calculate pairwise distances between vectors.
+
+    Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L34
+    """
+
+    def __init__(self, session):
+        self.session = session
+
+        # Initialize TF graph to calculate pairwise distances.
+        with session.graph.as_default():
+            self._features_batch1 = tf.placeholder(tf.float32, shape=[None, None])
+            self._features_batch2 = tf.placeholder(tf.float32, shape=[None, None])
+            distance_block_16 = _batch_pairwise_distances(
+                tf.cast(self._features_batch1, tf.float16),
+                tf.cast(self._features_batch2, tf.float16),
+            )
+            self.distance_block = tf.cond(
+                tf.reduce_all(tf.math.is_finite(distance_block_16)),
+                lambda: tf.cast(distance_block_16, tf.float32),
+                lambda: _batch_pairwise_distances(self._features_batch1, self._features_batch2),
+            )
+
+            # Extra logic for less thans.
+            self._radii1 = tf.placeholder(tf.float32, shape=[None, None])
+            self._radii2 = tf.placeholder(tf.float32, shape=[None, None])
+            dist32 = tf.cast(self.distance_block, tf.float32)[..., None]
+            self._batch_1_in = tf.math.reduce_any(dist32 <= self._radii2, axis=1)
+            self._batch_2_in = tf.math.reduce_any(dist32 <= self._radii1[:, None], axis=0)
+
+    def pairwise_distances(self, U, V):
+        """
+        Evaluate pairwise distances between two batches of feature vectors.
+        """
+        return self.session.run(
+            self.distance_block,
+            feed_dict={self._features_batch1: U, self._features_batch2: V},
+        )
+
+    def less_thans(self, batch_1, radii_1, batch_2, radii_2):
+        return self.session.run(
+            [self._batch_1_in, self._batch_2_in],
+            feed_dict={
+                self._features_batch1: batch_1,
+                self._features_batch2: batch_2,
+                self._radii1: radii_1,
+                self._radii2: radii_2,
+            },
+        )
+
+
+def _batch_pairwise_distances(U, V):
+    """
+    Compute pairwise distances between two batches of feature vectors.
+    """
+    with tf.variable_scope("pairwise_dist_block"):
+        # Squared norms of each row in U and V.
+        norm_u = tf.reduce_sum(tf.square(U), 1)
+        norm_v = tf.reduce_sum(tf.square(V), 1)
+
+        # norm_u as a column and norm_v as a row vectors.
+        norm_u = tf.reshape(norm_u, [-1, 1])
+        norm_v = tf.reshape(norm_v, [1, -1])
+
+        # Pairwise squared Euclidean distances.
+        D = tf.maximum(norm_u - 2 * tf.matmul(U, V, False, True) + norm_v, 0.0)
+
+    return D
+
+
+class NpzArrayReader(ABC):
+    @abstractmethod
+    def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
+        pass
+
+    @abstractmethod
+    def remaining(self) -> int:
+        pass
+
+    def read_batches(self, batch_size: int) -> Iterable[np.ndarray]:
+        def gen_fn():
+            while True:
+                batch = self.read_batch(batch_size)
+                if batch is None:
+                    break
+                yield batch
+
+        rem = self.remaining()
+        num_batches = rem // batch_size + int(rem % batch_size != 0)
+        return BatchIterator(gen_fn, num_batches)
+
+
+class BatchIterator:
+    def __init__(self, gen_fn, length):
+        self.gen_fn = gen_fn
+        self.length = length
+
+    def __len__(self):
+        return self.length
+
+    def __iter__(self):
+        return self.gen_fn()
+
+
+class StreamingNpzArrayReader(NpzArrayReader):
+    def __init__(self, arr_f, shape, dtype):
+        self.arr_f = arr_f
+        self.shape = shape
+        self.dtype = dtype
+        self.idx = 0
+
+    def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
+        if self.idx >= self.shape[0]:
+            return None
+
+        bs = min(batch_size, self.shape[0] - self.idx)
+        self.idx += bs
+
+        if self.dtype.itemsize == 0:
+            return np.ndarray([bs, *self.shape[1:]], dtype=self.dtype)
+
+        read_count = bs * np.prod(self.shape[1:])
+        read_size = int(read_count * self.dtype.itemsize)
+        data = _read_bytes(self.arr_f, read_size, "array data")
+        return np.frombuffer(data, dtype=self.dtype).reshape([bs, *self.shape[1:]])
+
+    def remaining(self) -> int:
+        return max(0, self.shape[0] - self.idx)
+
+
+class MemoryNpzArrayReader(NpzArrayReader):
+    def __init__(self, arr):
+        self.arr = arr
+        self.idx = 0
+
+    @classmethod
+    def load(cls, path: str, arr_name: str):
+        with open(path, "rb") as f:
+            arr = np.load(f)[arr_name]
+        return cls(arr)
+
+    def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
+        if self.idx >= self.arr.shape[0]:
+            return None
+
+        res = self.arr[self.idx : self.idx + batch_size]
+        self.idx += batch_size
+        return res
+
+    def remaining(self) -> int:
+        return max(0, self.arr.shape[0] - self.idx)
+
+
+@contextmanager
+def open_npz_array(path: str, arr_name: str) -> NpzArrayReader:
+    with _open_npy_file(path, arr_name) as arr_f:
+        version = np.lib.format.read_magic(arr_f)
+        if version == (1, 0):
+            header = np.lib.format.read_array_header_1_0(arr_f)
+        elif version == (2, 0):
+            header = np.lib.format.read_array_header_2_0(arr_f)
+        else:
+            yield MemoryNpzArrayReader.load(path, arr_name)
+            return
+        shape, fortran, dtype = header
+        if fortran or dtype.hasobject:
+            yield MemoryNpzArrayReader.load(path, arr_name)
+        else:
+            yield StreamingNpzArrayReader(arr_f, shape, dtype)
+
+
+def _read_bytes(fp, size, error_template="ran out of data"):
+    """
+    Copied from: https://github.com/numpy/numpy/blob/fb215c76967739268de71aa4bda55dd1b062bc2e/numpy/lib/format.py#L788-L886
+
+    Read from file-like object until size bytes are read.
+    Raises ValueError if not EOF is encountered before size bytes are read.
+    Non-blocking objects only supported if they derive from io objects.
+    Required as e.g. ZipExtFile in python 2.6 can return less data than
+    requested.
+    """
+    data = bytes()
+    while True:
+        # io files (default in python3) return None or raise on
+        # would-block, python2 file will truncate, probably nothing can be
+        # done about that.  note that regular files can't be non-blocking
+        try:
+            r = fp.read(size - len(data))
+            data += r
+            if len(r) == 0 or len(data) == size:
+                break
+        except io.BlockingIOError:
+            pass
+    if len(data) != size:
+        msg = "EOF: reading %s, expected %d bytes got %d"
+        raise ValueError(msg % (error_template, size, len(data)))
+    else:
+        return data
+
+
+@contextmanager
+def _open_npy_file(path: str, arr_name: str):
+    with open(path, "rb") as f:
+        with zipfile.ZipFile(f, "r") as zip_f:
+            if f"{arr_name}.npy" not in zip_f.namelist():
+                raise ValueError(f"missing {arr_name} in npz file")
+            with zip_f.open(f"{arr_name}.npy", "r") as arr_f:
+                yield arr_f
+
+
+def _download_inception_model():
+    if os.path.exists(INCEPTION_V3_PATH):
+        return
+    print("downloading InceptionV3 model...")
+    with requests.get(INCEPTION_V3_URL, stream=True) as r:
+        r.raise_for_status()
+        tmp_path = INCEPTION_V3_PATH + ".tmp"
+        with open(tmp_path, "wb") as f:
+            for chunk in tqdm(r.iter_content(chunk_size=8192)):
+                f.write(chunk)
+        os.rename(tmp_path, INCEPTION_V3_PATH)
+
+
+def _create_feature_graph(input_batch):
+    _download_inception_model()
+    prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}"
+    with open(INCEPTION_V3_PATH, "rb") as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+    pool3, spatial = tf.import_graph_def(
+        graph_def,
+        input_map={f"ExpandDims:0": input_batch},
+        return_elements=[FID_POOL_NAME, FID_SPATIAL_NAME],
+        name=prefix,
+    )
+    _update_shapes(pool3)
+    spatial = spatial[..., :7]
+    return pool3, spatial
+
+
+def _create_softmax_graph(input_batch):
+    _download_inception_model()
+    prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}"
+    with open(INCEPTION_V3_PATH, "rb") as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+    (matmul,) = tf.import_graph_def(
+        graph_def, return_elements=[f"softmax/logits/MatMul"], name=prefix
+    )
+    w = matmul.inputs[1]
+    logits = tf.matmul(input_batch, w)
+    return tf.nn.softmax(logits)
+
+
+def _update_shapes(pool3):
+    # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L50-L63
+    ops = pool3.graph.get_operations()
+    for op in ops:
+        for o in op.outputs:
+            shape = o.get_shape()
+            if shape._dims is not None:  # pylint: disable=protected-access
+                # shape = [s.value for s in shape] TF 1.x
+                shape = [s for s in shape]  # TF 2.x
+                new_shape = []
+                for j, s in enumerate(shape):
+                    if s == 1 and j == 0:
+                        new_shape.append(None)
+                    else:
+                        new_shape.append(s)
+                o.__dict__["_shape_val"] = tf.TensorShape(new_shape)
+    return pool3
+
+
+def _numpy_partition(arr, kth, **kwargs):
+    num_workers = min(cpu_count(), len(arr))
+    chunk_size = len(arr) // num_workers
+    extra = len(arr) % num_workers
+
+    start_idx = 0
+    batches = []
+    for i in range(num_workers):
+        size = chunk_size + (1 if i < extra else 0)
+        batches.append(arr[start_idx : start_idx + size])
+        start_idx += size
+
+    with ThreadPool(num_workers) as pool:
+        return list(pool.map(partial(np.partition, kth=kth, **kwargs), batches))
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/compute_cd.py b/evaluations/fidkid-pytorch/3d-metrics/evals/compute_cd.py
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--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/compute_cd.py
@@ -0,0 +1,396 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+import json
+import argparse
+import numpy as np
+import torch
+import os
+import random
+import glob
+from tqdm import tqdm
+import kaolin as kal
+import point_cloud_utils as pcu
+import ipdb
+import pandas as pd
+import numpy as np
+from functools import partial
+from pdb import set_trace as st
+from pathlib import Path
+
+from functools import partial
+
+# unused, already matched
+# varyData = [
+# ["X", 270],
+# ["Z", 180],
+# ]
+
+def rotate_point_cloud(point_cloud, rotations):
+    """
+    Rotates a point cloud along specified axes by the given angles.
+    :param point_cloud: Nx3 numpy array of points
+    :param rotations: list of tuples [(axis, angle_in_degrees), ...]
+                      Example: [('x', 90), ('y', 45)] for composite rotations
+    :return: Rotated point cloud as Nx3 numpy array
+    """
+    rotated_cloud = point_cloud.copy()
+    for axis, angle in rotations:
+        angle_rad = np.radians(angle)  # Convert degrees to radians
+        R = rotation_matrix(axis, angle_rad)
+        rotated_cloud = np.dot(rotated_cloud, R.T)  # Apply rotation matrix
+    
+    return rotated_cloud
+
+
+# transformation to align all results in the same canonical space
+transformation_dict = {
+    'gso': partial(rotate_point_cloud, rotations=[('x', 0)]), # no transformation
+    'LGM_fixpose': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'CRM/Animals': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'Lara': partial(rotate_point_cloud, rotations=[('x', -110), ('z', 33)]),
+    'ln3diff-lite/Animals': partial(rotate_point_cloud, rotations=[('x', 90)]),
+    'One-2-3-45/Animals': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'splatter-img': partial(rotate_point_cloud, rotations=[('x', -60)]),
+    # 
+    'OpenLRM/Animals': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'shape-e/Animals': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    # 
+    'objv-gt': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'GA': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    # un-aligned
+    'scale3d/eval/eval_nerf/Animals': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'scale3d/eval/eval_mesh/Animals': partial(rotate_point_cloud, rotations=[('x', 180), ('z', 180)]),
+}
+
+def VaryPoint(data, axis, degree):
+    # to rotate axis
+    xyzArray = {
+        'X': np.array([[1, 0, 0],
+                [0, cos(radians(degree)), -sin(radians(degree))],
+                [0, sin(radians(degree)), cos(radians(degree))]]),
+        'Y': np.array([[cos(radians(degree)), 0, sin(radians(degree))],
+                [0, 1, 0],
+                [-sin(radians(degree)), 0, cos(radians(degree))]]),
+        'Z': np.array([[cos(radians(degree)), -sin(radians(degree)), 0],
+                [sin(radians(degree)), cos(radians(degree)), 0],
+                [0, 0, 1]])}
+    newData = np.dot(data, xyzArray[axis])
+    return newData
+
+from math import *
+def seed_everything(seed):
+    if seed < 0:
+        return
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+def read_pcd(name, n_sample=2048):
+    v = pcu.load_mesh_v(name)
+    point_clouds = np.random.permutation(v)[:n_sample, :]
+    return torch.from_numpy(point_clouds).unsqueeze(0)
+
+def get_score(results, use_same_numer_for_test=False):
+    if use_same_numer_for_test:
+        results = results[:, :results.shape[0]]
+    mmd = results.min(axis=1).mean()
+    min_ref = results.argmin(axis=0)
+    unique_idx = np.unique(min_ref)
+    cov = float(len(unique_idx)) / results.shape[0]
+
+    # if mmd < 1:
+    #     # Chamfer distance
+    mmd = mmd * 1000  # for showing results
+
+    return mmd, cov * 100
+
+
+def rotation_matrix(axis, angle):
+    """
+    Returns a rotation matrix for a given axis and angle in radians.
+    :param axis: str, the axis to rotate around ('x', 'y', or 'z')
+    :param angle: float, the rotation angle in radians
+    :return: 3x3 rotation matrix
+    """
+    if axis == 'x':
+        return np.array([[1, 0, 0],
+                         [0, np.cos(angle), -np.sin(angle)],
+                         [0, np.sin(angle), np.cos(angle)]])
+    elif axis == 'y':
+        return np.array([[np.cos(angle), 0, np.sin(angle)],
+                         [0, 1, 0],
+                         [-np.sin(angle), 0, np.cos(angle)]])
+    elif axis == 'z':
+        return np.array([[np.cos(angle), -np.sin(angle), 0],
+                         [np.sin(angle), np.cos(angle), 0],
+                         [0, 0, 1]])
+    else:
+        raise ValueError("Axis must be 'x', 'y', or 'z'.")
+
+
+
+def scale_to_unit_sphere(points, center=None):
+    midpoints = (torch.max(points, axis=1)[0] + torch.min(points, axis=1)[0]) / 2
+    # midpoints = np.mean(points, axis=0)
+    points = points - midpoints
+    scale = torch.max(torch.sqrt(torch.sum(points ** 2, axis=2)))
+    points = points / scale
+    return points
+
+def sample_point_with_mesh_name(method_name, name, n_sample=2048, normalized_scale=1.0, rotate_degree=-90):
+    #ipdb.set_trace()
+    # if '.ply' in name:
+    #     v = pcu.load_mesh_v(name)
+    #     point_clouds = np.random.permutation(v)[:n_sample, :]
+    #     scale = point_clouds.max()-point_clouds.min()
+        
+    #     point_clouds = point_clouds / scale #* normalized_scale  # Make them in the same scale pcu.save_mesh_v('a.obj',point_clouds)
+    #     #ipdb.set_trace()
+
+
+    #     return torch.from_numpy(point_clouds).float().cuda().unsqueeze(dim=0)
+    try:
+        mesh_1 = kal.io.obj.import_mesh(name)
+    
+        if mesh_1.vertices.shape[0] == 0:
+            return None
+        vertices = mesh_1.vertices.cuda()
+        #ipdb.set_trace()
+        #scale = (vertices.max(dim=0)[0] - vertices.min(dim=0)[0]).max()
+        mesh_v1 = vertices #/ scale #* normalized_scale     pcu.save_mesh_v('a.ply',points[0].cpu().numpy())
+        mesh_f1 = mesh_1.faces.cuda()
+        points, _ = kal.ops.mesh.sample_points(mesh_v1.unsqueeze(dim=0), mesh_f1, n_sample)
+        #ipdb.set_trace()
+        points=scale_to_unit_sphere(points)
+        #ipdb.set_trace()
+        return points.cuda()
+    except:
+        v = pcu.load_mesh_v(name)
+        point_clouds = np.random.permutation(v)[:n_sample, :]
+        #scale = point_clouds.max()-point_clouds.min()
+        
+        #point_clouds = point_clouds / scale #* normalized_scale  # Make them in the same scale pcu.save_mesh_v('a.obj',point_clouds)
+        #ipdb.set_trace()
+        point_clouds=torch.from_numpy(point_clouds).float().cuda().unsqueeze(dim=0)
+        point_clouds=scale_to_unit_sphere(point_clouds)
+
+        # point_clouds=point_clouds*-1
+
+        # rand rotate
+        # rand_rot = [('x', random.randint(0,359)), ('y', random.randint(0,359)), ('z', random.randint(0,359))]
+        # rand_transform = partial(rotate_point_cloud, rotations=rand_rot) # no transformation
+        # point_clouds = rand_transform(point_clouds[0].cpu().numpy()) # since no canonical space
+        # point_clouds = torch.from_numpy(point_clouds).float().cuda().unsqueeze(dim=0)
+
+        # ipdb.set_trace()
+        pcd_transform = transformation_dict[method_name] # to the same canonical space
+        point_clouds = pcd_transform(point_clouds[0].cpu().numpy()) # since no canonical space
+        point_clouds = torch.from_numpy(point_clouds).float().cuda().unsqueeze(dim=0)
+
+                
+        def VaryPoint(data, axis, degree):
+            xyzArray = {
+                'X': np.array([[1, 0, 0],
+                        [0, cos(radians(degree)), -sin(radians(degree))],
+                        [0, sin(radians(degree)), cos(radians(degree))]]),
+                'Y': np.array([[cos(radians(degree)), 0, sin(radians(degree))],
+                        [0, 1, 0],
+                        [-sin(radians(degree)), 0, cos(radians(degree))]]),
+                'Z': np.array([[cos(radians(degree)), -sin(radians(degree)), 0],
+                        [sin(radians(degree)), cos(radians(degree)), 0],
+                        [0, 0, 1]])}
+            newData = np.dot(data, xyzArray[axis])
+            return newData
+        # if rorate_minus_90:
+
+        # varyData = [
+        # # ["X", rotate_degree], # stl file -90
+        # ]
+
+        # else:
+        varyData = [
+        ["X", 0], # stl file -90
+        ]
+
+        for para in varyData:
+            point_clouds_new = VaryPoint(point_clouds[0,:, :3].cpu().numpy(), para[0], para[1])
+        # ipdb.set_trace()
+
+        return torch.Tensor(point_clouds_new).cuda().unsqueeze(0)
+        #print('error')
+
+
+def chamfer_distance(method_name,ref_name,ref_pcs, sample_pcs, batch_size,save_name):
+    all_rec_pcs = []
+    n_sample = 2048
+    normalized_scale = 1.0
+    # ipdb.set_trace()
+
+    if os.path.exists(os.path.join(save_name,'gt.pth')):
+    # if False:
+        all_rec_pcs=torch.load(os.path.join(save_name,'gt.pth')).to('cuda')
+    else:
+    # if True:
+        for name in tqdm(ref_pcs):
+            # all_rec_pcs.append(sample_point_with_mesh_name(name, n_sample, normalized_scale=normalized_scale, rotate_degree=0))
+            all_rec_pcs.append(sample_point_with_mesh_name(ref_name, name, n_sample, normalized_scale=normalized_scale, rotate_degree=0))
+            # all_rec_pcs.append(read_pcd(name, n_sample))
+
+        all_rec_pcs = [p for p in all_rec_pcs if p is not None]
+        all_rec_pcs = torch.cat(all_rec_pcs, dim=0).to('cuda')
+        #ipdb.set_trace()
+        os.makedirs(os.path.join(save_name), exist_ok=True)
+        torch.save(all_rec_pcs,os.path.join(save_name,'gt.pth'))
+    
+    # methodname=sample_pcs[0].split('/')[-2]
+    #ipdb.set_trace()
+    # if os.path.exists(os.path.join(save_name,'sample.pth')):    
+    if False:
+        all_sample_pcs=torch.load(os.path.join(save_name,'sample.pth')).to('cuda')
+    else:    
+    # if True:
+        all_sample_pcs = []
+        for name in tqdm(sample_pcs):
+            # This is generated
+            #ipdb.set_trace()
+            # all_sample_pcs.append(sample_point_with_mesh_name(name, n_sample, normalized_scale=normalized_scale, rotate_degree=90)) # all_sample_pcs.append(read_pcd(name, n_sample))
+            all_sample_pcs.append(sample_point_with_mesh_name(method_name, name, n_sample, normalized_scale=normalized_scale, rotate_degree=0)) # all_sample_pcs.append(read_pcd(name, n_sample))
+            # ipdb.set_trace()
+            pass
+
+        all_sample_pcs = [p for p in all_sample_pcs if p is not None]
+        all_sample_pcs = torch.cat(all_sample_pcs, dim=0).to('cuda')
+
+        os.makedirs(os.path.join(save_name), exist_ok=True)
+        torch.save(all_sample_pcs,os.path.join(save_name,'sample.pth'))
+    
+    # ipdb.set_trace()
+
+    #all_sample_pcs+=(all_rec_pcs.mean(0).mean(0)-all_sample_pcs.mean(0).mean(0))
+    # all_rec_pcs-=all_rec_pcs.mean(1).unsqueeze(1)
+    # all_sample_pcs-=all_sample_pcs.mean(1).unsqueeze(1)
+
+        #ipdb.set_trace()
+
+    # for para in varyData:
+    #     for i in range(len(all_sample_pcs)):
+    #         #ipdb.set_trace()
+    #         all_sample_pcs[i] = torch.Tensor(VaryPoint(all_sample_pcs[i,:, :3].cpu().numpy(), para[0], para[1])).cuda()
+    # all_sample_pcs+=(all_rec_pcs.mean(0).mean(0)-all_sample_pcs.mean(0).mean(0))
+    # all_sample_pcs.mean(0).mean(0)
+    # all_sample_pcs[:,1,:]-=0.1
+    #all_sample_pcs=all_sample_pcs[:3684]
+    #all_rec_pcs=all_rec_pcs[:1000] all_sample_pcs[...,2]*=-1   pcu.save_mesh_v('a.ply',all_rec_pcs[8].cpu().numpy())  pcu.save_mesh_v('b.ply',all_sample_pcs[1391].reshape(-1,3).cpu().numpy())
+    print('datapreparation')
+    #ipdb.set_trace()
+    all_cd = []
+    for i_ref_p in tqdm(range(len(all_rec_pcs))):
+        ref_p = all_rec_pcs[i_ref_p]
+        cd_lst = []
+        for sample_b_start in range(0, len(sample_pcs), batch_size):
+            sample_b_end = min(len(sample_pcs), sample_b_start + batch_size)
+            sample_batch = all_sample_pcs[sample_b_start:sample_b_end]
+
+            batch_size_sample = sample_batch.size(0)
+            chamfer = kal.metrics.pointcloud.chamfer_distance(
+                ref_p.unsqueeze(dim=0).expand(batch_size_sample, -1, -1),
+                sample_batch)
+            cd_lst.append(chamfer)
+        cd_lst = torch.cat(cd_lst, dim=0)
+        all_cd.append(cd_lst.unsqueeze(dim=0))
+    all_cd = torch.cat(all_cd, dim=0)
+    return all_cd
+
+
+def compute_all_metrics(method_name,ref_name,sample_pcs, ref_pcs, batch_size, save_name=None):
+    results = chamfer_distance(method_name,ref_name,ref_pcs, sample_pcs, batch_size,save_name).data.cpu().numpy()
+    #ipdb.set_trace()
+    #results = results[:, :results.shape[0] * 5]  # Generation is 5 time of the testing set
+    cd_mmd, cd_cov = get_score(results, use_same_numer_for_test=False)
+    #ipdb.set_trace()
+    print('cov,mmd:',(cd_cov, cd_mmd, save_name))
+
+
+
+def evaluate(args):
+    # Set the random seed
+    # seed_everything(0) # for GA default
+    seed_everything(42)
+
+    ref_path=[]
+
+    # shapenet_cls = args.dataset_path.split('/')[-1]
+    # if shapenet_cls == 'chair':
+    #     train_lst=np.loadtxt(f'/mnt/cache/yslan/get3d/{shapenet_cls}_train_list_srn.txt','str')
+    # else:
+    #     train_lst=np.loadtxt(f'/mnt/cache/yslan/get3d/{shapenet_cls}_train_list.txt','str')
+    # for s in os.listdir(args.dataset_path):
+    # ipdb.set_trace()
+
+
+    # ipdb.set_trace()
+    # if 'GA' in args.dataset_path or 'objv-gt' in args.dataset_path:
+
+    gen_path_base='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps'
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    all_objs = dataset_json['Animals'][::3][1100:2200][:600] # pick top 600 instances.
+
+    ref_path = [os.path.join(args.dataset_path, f"{obj.replace('/', '-')}_pcd_4096.ply") for obj in all_objs]
+
+    # ipdb.set_trace()
+
+    # else:
+    #     ref_path = sorted(glob.glob(f'{args.dataset_path}/*.ply') )
+
+    # for s in files:
+    #     if os.path.exists(os.path.join(args.dataset_path, s, 'pcd_4096.ply')):
+    #             ref_path = ref_path+[os.path.join(args.dataset_path, s, 'pcd_4096.ply')]
+
+    # for s in os.listdir(args.dataset_path):
+    #     #ipdb.set_trace()
+    #    # if s=='toy_boat':
+    #         if os.path.isdir(os.path.join(args.dataset_path, s)):
+    #             for instance in os.listdir(os.path.join(args.dataset_path, s)):
+    #                 if os.path.exists(os.path.join(args.dataset_path, s,instance,'Scan','Scan.obj')):
+                        
+    #                     ref_path = ref_path+[os.path.join(args.dataset_path, s, instance,'Scan','Scan.obj') ]
+
+    gen_path = args.gen_path
+    # method_name = '/'.join(gen_path.split('/')[-2:])
+    method_name = str(Path(gen_path).relative_to(gen_path_base))
+    ref_name = str(Path(args.dataset_path).relative_to(gen_path_base))
+
+    #ref_path=ref_path[::100]
+    gen_models = glob.glob(os.path.join(gen_path, '*.ply'))
+    gen_models = sorted(gen_models)
+    
+
+    # if '_cond' in gen_path:
+    #     args.save_name+='_cond'
+    gen_models = gen_models[:args.n_shape]
+    with torch.no_grad():
+        #ipdb.set_trace()
+        compute_all_metrics(method_name,ref_name,gen_models, ref_path, args.batch_size, args.save_name)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--save_name", type=str, default='/mnt/petrelfs/caoziang/3D_generation/cmetric/get3d/omni_final_surface', help="path to the save results")
+    parser.add_argument("--dataset_path", type=str,default='/mnt/petrelfs/share_data/wutong/DATA/OO3D/ply_files/4096', help="path to the original shapenet dataset")
+    parser.add_argument("--gen_path", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel11/ddpm_5/test',help="path to the generated models")
+    parser.add_argument("--n_points", type=int, default=2048, help="Number of points used for evaluation")
+    parser.add_argument("--batch_size", type=int, default=100, help="batch size to compute chamfer distance")
+    parser.add_argument("--n_shape", type=int, default=7500, help="number of shapes for evaluations")
+    parser.add_argument("--use_npz", type=bool, default=False, help="whether the generated shape is npz or not")
+    args = parser.parse_args()
+    evaluate(args)
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/eval_cd.sh b/evaluations/fidkid-pytorch/3d-metrics/evals/eval_cd.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b70a58266dd00b732280d2f4e220b874427041d4
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/eval_cd.sh
@@ -0,0 +1,26 @@
+
+# for method_name in ln3diff-fixpose_192 CRM LGM_fixpose
+# for method_name in GA splatter-img LGM ln3diff-lite/Animals CRM/Animals Lara OpenLRM/Animals One-2-3-45/Animals shape-e/Animals 
+
+# for method_name in scale3d/eval/eval_nerf/Animals scale3d/eval/eval_mesh/Animals
+for method_name in LGM_fixpose
+
+# for method_name in shape-e/Animals GA splatter-img LGM ln3diff-lite/Animals CRM/Animals Lara OpenLRM/Animals One-2-3-45/Animals CRM/Animals
+
+do
+
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+
+gen_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps
+objv_path="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/objv-gt"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-cd/
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-cd-noaug-3/
+
+python compute_cd.py \
+    --n_shape 600 \
+    --n_points 2048 \
+    --dataset_path ${objv_path} \
+    --gen_path ${gen_path}/${method_name} \
+    --save_name ${output_path}/${method_name}
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/eval_fpd_is.sh b/evaluations/fidkid-pytorch/3d-metrics/evals/eval_fpd_is.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cef8cc31a781e5f362edd766bcc6dc412f22affa
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/eval_fpd_is.sh
@@ -0,0 +1,46 @@
+
+set -x 
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+gso_pcd='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics-fps/gso'
+
+# gso_rendering="gso-rendering"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics-fps
+
+# ! for calculating direct fps sampling from surfel gs
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir
+
+# ! for pointnet models
+cache_dir=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics/cache
+
+# method_name=LGM
+# for method_name in LGM CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+
+# ! eval gso
+# for method_name in LGM CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img \
+#     GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh \
+#     GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD \
+
+# do
+
+# python fpd_eval.py \
+#     $gso_pcd \
+#     ${output_path}/${method_name} \
+#     --cache_dir ${cache_dir} \
+
+# done
+
+# ! eval g-objv
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps
+
+for method_name in CRM/Animals Lara ln3diff GA/stage-2/dino_img/ditl-fromditlPCD \
+
+do
+
+python fpd_eval.py \
+    $gso_pcd \
+    ${output_path}/${method_name} \
+    --cache_dir ${cache_dir} \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/eval_fpd_is_objv.sh b/evaluations/fidkid-pytorch/3d-metrics/evals/eval_fpd_is_objv.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e7af22d8061a5172e7f08edd305d74f248773c14
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/eval_fpd_is_objv.sh
@@ -0,0 +1,51 @@
+
+set -x 
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+gso_pcd='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics-fps/gso'
+
+# gso_rendering="gso-rendering"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics-fps
+
+# ! for calculating direct fps sampling from surfel gs
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir
+
+# ! for pointnet models
+cache_dir=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics/cache
+
+# method_name=LGM
+# for method_name in LGM CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+
+# ! eval gso
+# for method_name in LGM CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img \
+#     GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh \
+#     GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD \
+
+# do
+
+# python fpd_eval.py \
+#     $gso_pcd \
+#     ${output_path}/${method_name} \
+#     --cache_dir ${cache_dir} \
+
+# done
+
+# ! eval g-objv
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/
+objv_path="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/objv-gt"
+
+# for method_name in CRM/Animals Lara 
+# for method_name in GA splatter-img shape-e/Animals LGM_fixpose ln3diff-lite/Animals Lara OpenLRM/Animals One-2-3-45/Animals 
+# for method_name in LGM_fixpose
+
+for method_name in CRM/Animals scale3d/eval/eval_nerf/Animals scale3d/eval/eval_mesh/Animals
+
+do
+
+python fpd_eval_objv.py \
+    $objv_path \
+    ${output_path}/${method_name} \
+    --cache_dir ${cache_dir} \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/feature_extractor.py b/evaluations/fidkid-pytorch/3d-metrics/evals/feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..38bababff4db86e9c220faddfac38ef2f6676e1c
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/feature_extractor.py
@@ -0,0 +1,130 @@
+from abc import ABC, abstractmethod
+from multiprocessing.pool import ThreadPool
+from typing import List, Optional, Tuple, Union
+from tqdm import tqdm
+
+from pdb import set_trace as st
+import numpy as np
+import torch
+
+from point_e.models.download import load_checkpoint
+
+from npz_stream import NpzStreamer
+from pointnet2_cls_ssg import get_model
+
+
+def get_torch_devices() -> List[Union[str, torch.device]]:
+    if torch.cuda.is_available():
+        return [torch.device(f"cuda:{i}") for i in range(torch.cuda.device_count())]
+    else:
+        return ["cpu"]
+
+
+class FeatureExtractor(ABC):
+    @property
+    @abstractmethod
+    def supports_predictions(self) -> bool:
+        pass
+
+    @property
+    @abstractmethod
+    def feature_dim(self) -> int:
+        pass
+
+    @property
+    @abstractmethod
+    def num_classes(self) -> int:
+        pass
+
+    @abstractmethod
+    def features_and_preds(self, streamer: NpzStreamer) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        For a stream of point cloud batches, compute feature vectors and class
+        predictions.
+
+        :param point_clouds: a streamer for a sample batch. Typically, arr_0
+                             will contain the XYZ coordinates.
+        :return: a tuple (features, predictions)
+                 - features: a [B x feature_dim] array of feature vectors.
+                 - predictions: a [B x num_classes] array of probabilities.
+        """
+
+
+class PointNetClassifier(FeatureExtractor):
+    def __init__(
+        self,
+        devices: List[Union[str, torch.device]],
+        device_batch_size: int = 64,
+        cache_dir: Optional[str] = None,
+    ):
+        state_dict = load_checkpoint("pointnet", device=torch.device("cpu"), cache_dir=cache_dir)[
+            "model_state_dict"
+        ]
+
+        self.device_batch_size = device_batch_size
+        self.devices = devices
+        # self.models = []
+        # for device in devices:
+        model = get_model(num_class=40, normal_channel=False, width_mult=2)
+        model.load_state_dict(state_dict)
+        model.to('cuda')
+        model.eval()
+        # self.models.append(model)
+        self.model = model
+
+    @property
+    def supports_predictions(self) -> bool:
+        return True
+
+    @property
+    def feature_dim(self) -> int:
+        return 256
+
+    @property
+    def num_classes(self) -> int:
+        return 40
+
+    # def features_and_preds(self, streamer: NpzStreamer) -> Tuple[np.ndarray, np.ndarray]:
+    def features_and_preds(self, streamer) -> Tuple[np.ndarray, np.ndarray]:
+        # batch_size = self.device_batch_size * len(self.devices)
+        # batch_size = self.device_batch_size * len(self.devices)
+        point_clouds = streamer # switch to pytorch stream here
+        # point_clouds = (x["arr_0"] for x in streamer.stream(batch_size, ["arr_0"]))
+        device = 'cuda'
+
+        output_features = []
+        output_predictions = []
+
+        # st()
+
+        # with ThreadPool(len(self.devices)) as pool:
+
+        for _, batch in enumerate(tqdm(point_clouds)): # type: ignore
+            # batch = normalize_point_clouds(batch)
+            # batches = []
+            # for i, device in zip(range(0, len(batch), self.device_batch_size), self.devices):
+                # batches.append(
+            # batch = torch.from_numpy(batch).permute(0, 2, 1).to(dtype=torch.float32, device=device)
+            batch = batch.to(dtype=torch.float32, device=device).permute(0, 2, 1) # B 3 L
+
+            def compute_features(batch):
+                # batch = i_batch
+                with torch.no_grad():
+                    return self.model(batch, features=True)
+
+            # for logits, _, features in pool.imap(compute_features, enumerate(batches)):
+            # for logits, _, features in pool.imap(compute_features, enumerate(batches)):
+
+            logits, _, features = compute_features(batch)
+            output_features.append(features.cpu().numpy())
+            output_predictions.append(logits.exp().cpu().numpy())
+
+        return np.concatenate(output_features, axis=0), np.concatenate(output_predictions, axis=0)
+
+
+def normalize_point_clouds(pc: np.ndarray) -> np.ndarray:
+    centroids = np.mean(pc, axis=1, keepdims=True)
+    pc = pc - centroids
+    m = np.max(np.sqrt(np.sum(pc**2, axis=-1, keepdims=True)), axis=1, keepdims=True)
+    pc = pc / m
+    return pc
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/fid_is.py b/evaluations/fidkid-pytorch/3d-metrics/evals/fid_is.py
new file mode 100644
index 0000000000000000000000000000000000000000..176d06309d0d57f979a669d416893bd423e3f526
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/fid_is.py
@@ -0,0 +1,81 @@
+"""
+Adapted from https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/evaluations/evaluator.py
+"""
+
+
+import warnings
+
+import numpy as np
+from scipy import linalg
+
+
+class InvalidFIDException(Exception):
+    pass
+
+
+class FIDStatistics:
+    def __init__(self, mu: np.ndarray, sigma: np.ndarray):
+        self.mu = mu
+        self.sigma = sigma
+
+    def frechet_distance(self, other, eps=1e-6):
+        """
+        Compute the Frechet distance between two sets of statistics.
+        """
+        # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L132
+        mu1, sigma1 = self.mu, self.sigma
+        mu2, sigma2 = other.mu, other.sigma
+
+        mu1 = np.atleast_1d(mu1)
+        mu2 = np.atleast_1d(mu2)
+
+        sigma1 = np.atleast_2d(sigma1)
+        sigma2 = np.atleast_2d(sigma2)
+
+        assert (
+            mu1.shape == mu2.shape
+        ), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}"
+        assert (
+            sigma1.shape == sigma2.shape
+        ), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}"
+
+        diff = mu1 - mu2
+
+        # product might be almost singular
+        covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+        if not np.isfinite(covmean).all():
+            msg = (
+                "fid calculation produces singular product; adding %s to diagonal of cov estimates"
+                % eps
+            )
+            warnings.warn(msg)
+            offset = np.eye(sigma1.shape[0]) * eps
+            covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+        # numerical error might give slight imaginary component
+        if np.iscomplexobj(covmean):
+            if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+                m = np.max(np.abs(covmean.imag))
+                raise ValueError("Imaginary component {}".format(m))
+            covmean = covmean.real
+
+        tr_covmean = np.trace(covmean)
+
+        return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+
+
+def compute_statistics(feats: np.ndarray) -> FIDStatistics:
+    mu = np.mean(feats, axis=0)
+    sigma = np.cov(feats, rowvar=False)
+    return FIDStatistics(mu, sigma)
+
+
+def compute_inception_score(preds: np.ndarray, split_size: int = 5000) -> float:
+    # https://github.com/openai/improved-gan/blob/4f5d1ec5c16a7eceb206f42bfc652693601e1d5c/inception_score/model.py#L46
+    scores = []
+    for i in range(0, len(preds), split_size):
+        part = preds[i : i + split_size]
+        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+        kl = np.mean(np.sum(kl, 1))
+        scores.append(np.exp(kl))
+    return float(np.mean(scores))
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/fpd_eval.py b/evaluations/fidkid-pytorch/3d-metrics/evals/fpd_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..836ebf989f1513b70b4ca4b86775667fe367ed06
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/fpd_eval.py
@@ -0,0 +1,330 @@
+# https://raw.githubusercontent.com/3dlg-hcvc/omages/refs/heads/main/src/evals/fpd_eval.py
+import os
+import random
+from tqdm import tqdm
+import glob
+from pdb import set_trace as st
+import trimesh
+import sys
+import numpy as np
+import scipy # should be version 1.11.1
+import torch
+
+import argparse
+# from point_e.evals.feature_extractor import PointNetClassifier, get_torch_devices
+from feature_extractor import PointNetClassifier, get_torch_devices
+from point_e.evals.fid_is import compute_statistics
+from point_e.evals.fid_is import compute_inception_score
+from point_e.evals.npz_stream import NpzStreamer    
+
+import numpy as np
+
+def rotation_matrix(axis, angle):
+    """
+    Returns a rotation matrix for a given axis and angle in radians.
+    :param axis: str, the axis to rotate around ('x', 'y', or 'z')
+    :param angle: float, the rotation angle in radians
+    :return: 3x3 rotation matrix
+    """
+    if axis == 'x':
+        return np.array([[1, 0, 0],
+                         [0, np.cos(angle), -np.sin(angle)],
+                         [0, np.sin(angle), np.cos(angle)]])
+    elif axis == 'y':
+        return np.array([[np.cos(angle), 0, np.sin(angle)],
+                         [0, 1, 0],
+                         [-np.sin(angle), 0, np.cos(angle)]])
+    elif axis == 'z':
+        return np.array([[np.cos(angle), -np.sin(angle), 0],
+                         [np.sin(angle), np.cos(angle), 0],
+                         [0, 0, 1]])
+    else:
+        raise ValueError("Axis must be 'x', 'y', or 'z'.")
+
+
+def rotate_point_cloud(point_cloud, rotations):
+    """
+    Rotates a point cloud along specified axes by the given angles.
+    :param point_cloud: Nx3 numpy array of points
+    :param rotations: list of tuples [(axis, angle_in_degrees), ...]
+                      Example: [('x', 90), ('y', 45)] for composite rotations
+    :return: Rotated point cloud as Nx3 numpy array
+    """
+    rotated_cloud = point_cloud.copy()
+    for axis, angle in rotations:
+        angle_rad = np.radians(angle)  # Convert degrees to radians
+        R = rotation_matrix(axis, angle_rad)
+        rotated_cloud = np.dot(rotated_cloud, R.T)  # Apply rotation matrix
+    
+    return rotated_cloud
+
+from functools import partial
+# transformation dictionary
+transformation_dict = {
+    'gso': partial(rotate_point_cloud, rotations=[('x', 0)]), # no transformation
+    'LGM': partial(rotate_point_cloud, rotations=[('x', 90)]),
+    'CRM': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'Lara': partial(rotate_point_cloud, rotations=[('x', -110), ('z', 33)]),
+    'ln3diff': partial(rotate_point_cloud, rotations=[('x', 90)]),
+    'One-2-3-45': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'splatter-img': partial(rotate_point_cloud, rotations=[('x', -60)]),
+    # 
+    'OpenLRM': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'shape-e': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    # un-aligned
+    'ditl-fromditlPCD-fixPose-tomesh': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'ditl-fromditlPCD-fixPose-tomesh-ditxlPCD': partial(rotate_point_cloud, rotations=[('x', 0)]),
+}
+
+
+class PFID_evaluator():
+    def __init__(self, devices=['cuda:0'], batch_size=256, cache_dir='~/.temp/PFID_evaluator'):
+        self.__dict__.update(locals())
+        cache_dir = os.path.expanduser(cache_dir)
+        if not os.path.exists(cache_dir):
+            os.makedirs(cache_dir)
+        self.devices = [torch.device(d) for d in devices]
+        self.clf = PointNetClassifier(devices=self.devices, cache_dir=cache_dir, device_batch_size=self.batch_size)
+
+    def compute_pfid(self, pc_1, pc_2, return_feature=False):
+
+        # print("computing first batch activations")
+        # save clouds to npz files
+        npz_path1 = os.path.join(self.cache_dir, "temp1.npz")
+        npz_path2 = os.path.join(self.cache_dir, "temp2.npz")
+        np.savez(npz_path1, arr_0=pc_1)
+        np.savez(npz_path2, arr_0=pc_2)
+
+        features_1, _ = self.clf.features_and_preds(NpzStreamer(npz_path1))
+        stats_1 = compute_statistics(features_1)
+        # print(features_1.max(), features_1.min(), features_1.mean(), features_1.std() )
+        # print(stats_1.mu.shape, stats_1.sigma.shape)
+
+        features_2, _ = self.clf.features_and_preds(NpzStreamer(npz_path2))
+        stats_2 = compute_statistics(features_2)
+        # print(features_2.max(), features_2.min(), features_2.mean(), features_2.std() )
+        # print(stats_2.mu.shape, stats_2.sigma.shape)
+
+        if return_feature:
+            return features_1, features_2
+        
+        #PFID = stats_1.frechet_distance(stats_2) # same result as the next line
+        PFID= frechet_distance(stats_1.mu, stats_1.sigma, stats_2.mu, stats_2.sigma)
+        PKID = kernel_distance(features_1, features_2)
+
+        print(f"P-FID: {PFID}", f"P-KID: {PKID}")
+        return dict(PFID=PFID, PKID=PKID)
+
+
+# from https://github.com/GaParmar/clean-fid/blob/main/cleanfid/fid.py
+"""
+Numpy implementation of the Frechet Distance.
+The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+and X_2 ~ N(mu_2, C_2) is
+        d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+Stable version by Danica J. Sutherland.
+Params:
+    mu1   : Numpy array containing the activations of a layer of the
+            inception net (like returned by the function 'get_predictions')
+            for generated samples.
+    mu2   : The sample mean over activations, precalculated on an
+            representative data set.
+    sigma1: The covariance matrix over activations for generated samples.
+    sigma2: The covariance matrix over activations, precalculated on an
+            representative data set.
+"""
+def frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = scipy.linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = scipy.linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean)
+
+
+"""
+Compute the KID score given the sets of features
+"""
+def kernel_distance(feats1, feats2, num_subsets=100, max_subset_size=1000):
+    n = feats1.shape[1]
+    m = min(min(feats1.shape[0], feats2.shape[0]), max_subset_size)
+    t = 0
+    for _subset_idx in range(num_subsets):
+        x = feats2[np.random.choice(feats2.shape[0], m, replace=False)]
+        y = feats1[np.random.choice(feats1.shape[0], m, replace=False)]
+        a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3
+        b = (x @ y.T / n + 1) ** 3
+        t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m
+    kid = t / num_subsets / m
+    return float(kid)
+
+
+# load and calculate fid, kid, is
+
+def normalize_point_clouds(pc: np.ndarray) -> np.ndarray:
+    # centroids = np.mean(pc, axis=1, keepdims=True)
+    centroids = np.mean(pc, axis=1, keepdims=True)
+    pc = pc - centroids
+    m = np.max(np.sqrt(np.sum(pc**2, axis=-1, keepdims=True)), axis=1, keepdims=True)
+    pc = pc / m
+    return pc
+
+
+class PCDPathDataset(torch.utils.data.Dataset):
+    def __init__(self, pcd_file_path, transformation, rand_aug=False):
+        files = sorted(glob.glob(f'{pcd_file_path}/*.ply') )
+        # assert len(files)==1030 # gso
+        self.files = files
+        self.transformation = transformation 
+        # self.transforms = transforms
+        # self.reso=reso
+        self.rand_aug = rand_aug
+        # if rand_aug:
+        # else:
+        #     self.rand_transform = None
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, i):
+        path = self.files[i]
+
+        pcd = trimesh.load(path).vertices # pcu may fail sometimes
+        pcd = normalize_point_clouds(pcd[None])[0]
+        pcd = self.transformation(pcd)
+        if self.rand_aug is not None:
+            rand_rot = [('x', random.randint(0,359)), ('y', random.randint(0,359)), ('z', random.randint(0,359))]
+            rand_transform = partial(rotate_point_cloud, rotations=rand_rot) # no transformation
+            pcd = rand_transform(pcd) # since no canonical space
+
+        # try:
+        #     assert pcd.shape[1]==4096
+        # except Exception as e:
+        #     print(path)
+        
+        return pcd
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cache_dir", type=str, default=None)
+    parser.add_argument("batch_1", type=str)
+    parser.add_argument("batch_2", type=str)
+    args = parser.parse_args()
+
+    print("creating classifier...")
+    clf = PointNetClassifier(devices=get_torch_devices(), cache_dir=args.cache_dir)
+
+    worker=2
+    # force_recompute = False
+    force_recompute = True
+
+    feat_1_path = os.path.join(args.batch_1, 'feat.npy')
+    pred_1_path = os.path.join(args.batch_1, 'pred.npy')
+    # if not force_recompute and all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+    # if all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+    if not force_recompute and all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+        print("loading activations", args.batch_1)
+        features_1 = np.load(feat_1_path)
+        preds_1 = np.load(pred_1_path)
+
+    else:
+        print("computing activations", args.batch_1)
+        # gt_dataset = PCDPathDataset(args.batch_1,  transformation_dict['gso'])
+        gt_dataset = PCDPathDataset(args.batch_1,  transformation_dict['gso'], rand_aug=True)
+
+        # gt
+        gt_loader = torch.utils.data.DataLoader(gt_dataset,
+                                                    batch_size=64,
+                                                    shuffle=False,
+                                                    drop_last=False,
+                                                    num_workers=worker)
+        features_1, preds_1 = clf.features_and_preds(gt_loader)
+        np.save(feat_1_path, features_1)
+        np.save(pred_1_path, preds_1)
+
+    feat_2_path = os.path.join(args.batch_2, 'feat.npy')
+    pred_2_path = os.path.join(args.batch_2, 'pred.npy')
+
+    if not force_recompute and all(os.path.exists(path) for path in [feat_2_path, pred_2_path]):
+        features_2 = np.load(feat_2_path)
+        preds_2 = np.load(pred_2_path)
+        print("loading activations", args.batch_2)
+    else:
+
+        print("computing activations", args.batch_2)
+        method_name = args.batch_2.split('/')[-1]
+        # st()
+        pcd_transformation = transformation_dict[method_name]
+
+        pred_dataset = PCDPathDataset(args.batch_2, transformation=pcd_transformation, rand_aug=True)
+
+        # worker=0
+        pred_loader = torch.utils.data.DataLoader(pred_dataset,
+                                                    batch_size=64,
+                                                    shuffle=False,
+                                                    drop_last=False,
+                                                    num_workers=worker)
+        features_2, preds_2 = clf.features_and_preds(pred_loader)
+        np.save(feat_2_path, features_2)
+        np.save(feat_2_path, preds_2)
+
+    print("computing statistics")
+
+    stats_1 = compute_statistics(features_1)
+    # print(features_1.max(), features_1.min(), features_1.mean(), features_1.std() )
+    # print(stats_1.mu.shape, stats_1.sigma.shape)
+
+    stats_2 = compute_statistics(features_2)
+    # print(features_2.max(), features_2.min(), features_2.mean(), features_2.std() )
+    # print(stats_2.mu.shape, stats_2.sigma.shape)
+
+    # if return_feature:
+    #     return features_1, features_2
+    
+    #PFID = stats_1.frechet_distance(stats_2) # same result as the next line
+    PFID= frechet_distance(stats_1.mu, stats_1.sigma, stats_2.mu, stats_2.sigma)
+    PKID = kernel_distance(features_1, features_2)
+
+    # _, preds = clf.features_and_preds(pred_loader)
+
+    # print(f"P-IS: {compute_inception_score(preds)}")
+    # print(f"P-IS: {compute_inception_score(preds)}")
+    method_name = args.batch_2.split('/')[-1]
+
+    # print(method_name, f"P-FID: {PFID}", f"P-KID: {PKID}", f"P-IS: {compute_inception_score(preds_2)}")
+    print(method_name, f"P-FID: {PFID}", f"P-KID: {PKID}")
+    # return dict(PFID=PFID, PKID=PKID)
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/fpd_eval_objv.py b/evaluations/fidkid-pytorch/3d-metrics/evals/fpd_eval_objv.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3ec42d17fdac7a12a5b6846bdaf0fb67c2f18b4
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/fpd_eval_objv.py
@@ -0,0 +1,365 @@
+# https://raw.githubusercontent.com/3dlg-hcvc/omages/refs/heads/main/src/evals/fpd_eval.py
+import json
+import os
+import random
+from tqdm import tqdm
+import glob
+from pdb import set_trace as st
+import trimesh
+import sys
+import numpy as np
+import scipy # should be version 1.11.1
+import torch
+from pathlib import Path
+
+import argparse
+# from point_e.evals.feature_extractor import PointNetClassifier, get_torch_devices
+from feature_extractor import PointNetClassifier, get_torch_devices
+from point_e.evals.fid_is import compute_statistics
+from point_e.evals.fid_is import compute_inception_score
+from point_e.evals.npz_stream import NpzStreamer    
+
+import numpy as np
+
+def rotation_matrix(axis, angle):
+    """
+    Returns a rotation matrix for a given axis and angle in radians.
+    :param axis: str, the axis to rotate around ('x', 'y', or 'z')
+    :param angle: float, the rotation angle in radians
+    :return: 3x3 rotation matrix
+    """
+    if axis == 'x':
+        return np.array([[1, 0, 0],
+                         [0, np.cos(angle), -np.sin(angle)],
+                         [0, np.sin(angle), np.cos(angle)]])
+    elif axis == 'y':
+        return np.array([[np.cos(angle), 0, np.sin(angle)],
+                         [0, 1, 0],
+                         [-np.sin(angle), 0, np.cos(angle)]])
+    elif axis == 'z':
+        return np.array([[np.cos(angle), -np.sin(angle), 0],
+                         [np.sin(angle), np.cos(angle), 0],
+                         [0, 0, 1]])
+    else:
+        raise ValueError("Axis must be 'x', 'y', or 'z'.")
+
+
+def rotate_point_cloud(point_cloud, rotations):
+    """
+    Rotates a point cloud along specified axes by the given angles.
+    :param point_cloud: Nx3 numpy array of points
+    :param rotations: list of tuples [(axis, angle_in_degrees), ...]
+                      Example: [('x', 90), ('y', 45)] for composite rotations
+    :return: Rotated point cloud as Nx3 numpy array
+    """
+    rotated_cloud = point_cloud.copy()
+    for axis, angle in rotations:
+        angle_rad = np.radians(angle)  # Convert degrees to radians
+        R = rotation_matrix(axis, angle_rad)
+        rotated_cloud = np.dot(rotated_cloud, R.T)  # Apply rotation matrix
+    
+    return rotated_cloud
+
+from functools import partial
+# transformation dictionary
+transformation_dict = {
+    'gso': partial(rotate_point_cloud, rotations=[('x', 0)]), # no transformation
+    'LGM_fixpose': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'CRM/Animals': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'Lara': partial(rotate_point_cloud, rotations=[('x', -110), ('z', 33)]),
+    'ln3diff-lite/Animals': partial(rotate_point_cloud, rotations=[('x', 90)]),
+    'One-2-3-45/Animals': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),
+    'splatter-img': partial(rotate_point_cloud, rotations=[('x', -60)]),
+    # 
+    'OpenLRM/Animals': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'shape-e/Animals': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'objv-gt': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    # un-aligned
+    # 'ditl-fromditlPCD-fixPose-tomesh': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'GA': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'scale3d/eval/eval_nerf/Animals': partial(rotate_point_cloud, rotations=[('x', 0)]),
+    'scale3d/eval/eval_mesh/Animals': partial(rotate_point_cloud, rotations=[('x', 180), ('z', 180)]),
+
+    # 'ditl-fromditlPCD-fixPose-tomesh-ditxlPCD': partial(rotate_point_cloud, rotations=[('x', 0)]),
+}
+
+
+class PFID_evaluator():
+    def __init__(self, devices=['cuda:0'], batch_size=256, cache_dir='~/.temp/PFID_evaluator'):
+        self.__dict__.update(locals())
+        cache_dir = os.path.expanduser(cache_dir)
+        if not os.path.exists(cache_dir):
+            os.makedirs(cache_dir)
+        self.devices = [torch.device(d) for d in devices]
+        self.clf = PointNetClassifier(devices=self.devices, cache_dir=cache_dir, device_batch_size=self.batch_size)
+
+    def compute_pfid(self, pc_1, pc_2, return_feature=False):
+
+        # print("computing first batch activations")
+        # save clouds to npz files
+        npz_path1 = os.path.join(self.cache_dir, "temp1.npz")
+        npz_path2 = os.path.join(self.cache_dir, "temp2.npz")
+        np.savez(npz_path1, arr_0=pc_1)
+        np.savez(npz_path2, arr_0=pc_2)
+
+        features_1, _ = self.clf.features_and_preds(NpzStreamer(npz_path1))
+        stats_1 = compute_statistics(features_1)
+        # print(features_1.max(), features_1.min(), features_1.mean(), features_1.std() )
+        # print(stats_1.mu.shape, stats_1.sigma.shape)
+
+        features_2, _ = self.clf.features_and_preds(NpzStreamer(npz_path2))
+        stats_2 = compute_statistics(features_2)
+        # print(features_2.max(), features_2.min(), features_2.mean(), features_2.std() )
+        # print(stats_2.mu.shape, stats_2.sigma.shape)
+
+        if return_feature:
+            return features_1, features_2
+        
+        #PFID = stats_1.frechet_distance(stats_2) # same result as the next line
+        PFID= frechet_distance(stats_1.mu, stats_1.sigma, stats_2.mu, stats_2.sigma)
+        PKID = kernel_distance(features_1, features_2)
+
+        print(f"P-FID: {PFID}", f"P-KID: {PKID}")
+        return dict(PFID=PFID, PKID=PKID)
+
+
+# from https://github.com/GaParmar/clean-fid/blob/main/cleanfid/fid.py
+"""
+Numpy implementation of the Frechet Distance.
+The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+and X_2 ~ N(mu_2, C_2) is
+        d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+Stable version by Danica J. Sutherland.
+Params:
+    mu1   : Numpy array containing the activations of a layer of the
+            inception net (like returned by the function 'get_predictions')
+            for generated samples.
+    mu2   : The sample mean over activations, precalculated on an
+            representative data set.
+    sigma1: The covariance matrix over activations for generated samples.
+    sigma2: The covariance matrix over activations, precalculated on an
+            representative data set.
+"""
+def frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = scipy.linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = scipy.linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean)
+
+
+"""
+Compute the KID score given the sets of features
+"""
+def kernel_distance(feats1, feats2, num_subsets=100, max_subset_size=1000):
+    n = feats1.shape[1]
+    m = min(min(feats1.shape[0], feats2.shape[0]), max_subset_size)
+    t = 0
+    for _subset_idx in range(num_subsets):
+        x = feats2[np.random.choice(feats2.shape[0], m, replace=False)]
+        y = feats1[np.random.choice(feats1.shape[0], m, replace=False)]
+        a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3
+        b = (x @ y.T / n + 1) ** 3
+        t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m
+    kid = t / num_subsets / m
+    return float(kid)
+
+
+# load and calculate fid, kid, is
+
+def normalize_point_clouds(pc: np.ndarray) -> np.ndarray:
+    # centroids = np.mean(pc, axis=1, keepdims=True)
+    centroids = np.mean(pc, axis=1, keepdims=True)
+    pc = pc - centroids
+    m = np.max(np.sqrt(np.sum(pc**2, axis=-1, keepdims=True)), axis=1, keepdims=True)
+    pc = pc / m
+    return pc
+
+
+class PCDPathDataset(torch.utils.data.Dataset):
+    def __init__(self, pcd_file_path, transformation, rand_aug=False):
+        # files = sorted(glob.glob(f'{pcd_file_path}/*.ply') )
+
+
+        objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+        dataset_json = os.path.join(objv_dataset, 'dataset.json')
+        with open(dataset_json, 'r') as f:
+            dataset_json = json.load(f)
+
+        # all_objs = dataset_json['Animals'][::3][:6250]
+        all_objs = dataset_json['Animals'][::3][1100:2200]
+        all_objs = all_objs[:600]
+
+        if 'GA' in pcd_file_path or 'objv-gt' in pcd_file_path:
+            files = [os.path.join(pcd_file_path, f"{obj.replace('/', '-')}_pcd_4096.ply") for obj in all_objs]
+            filter_files = []
+            for file in files:
+                if os.path.exists(file):
+                    filter_files.append(file)
+            self.files = filter_files
+        else:
+            self.files = sorted(glob.glob(f'{pcd_file_path}/*.ply') )
+
+        self.transformation = transformation 
+        # self.transforms = transforms
+        # self.reso=reso
+        self.rand_aug = rand_aug
+        # if rand_aug:
+        # else:
+        #     self.rand_transform = None
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, i):
+        path = self.files[i]
+
+        pcd = trimesh.load(path).vertices # pcu may fail sometimes
+        pcd = normalize_point_clouds(pcd[None])[0]
+        if self.transformation is not None:
+            pcd = self.transformation(pcd)
+
+        # if self.rand_aug:
+        #     rand_rot = [('x', random.randint(0,359)), ('y', random.randint(0,359)), ('z', random.randint(0,359))]
+        #     rand_transform = partial(rotate_point_cloud, rotations=rand_rot) # no transformation
+        #     pcd = rand_transform(pcd) # since no canonical space
+
+        # try:
+        #     assert pcd.shape[1]==4096
+        # except Exception as e:
+        #     print(path)
+        
+        return pcd
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cache_dir", type=str, default=None)
+    parser.add_argument("batch_1", type=str)
+    parser.add_argument("batch_2", type=str)
+    args = parser.parse_args()
+
+    print("creating classifier...")
+    clf = PointNetClassifier(devices=get_torch_devices(), cache_dir=args.cache_dir)
+
+    worker=2
+    # force_recompute = False
+    force_recompute = True
+
+    feat_1_path = os.path.join(args.batch_1, 'feat.npy')
+    pred_1_path = os.path.join(args.batch_1, 'pred.npy')
+    # if not force_recompute and all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+    # if all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+    if not force_recompute and all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+    # if all(os.path.exists(path) for path in [feat_1_path, pred_1_path]):
+        print("loading activations", args.batch_1)
+        features_1 = np.load(feat_1_path)
+        preds_1 = np.load(pred_1_path)
+
+    else:
+        print("computing activations", args.batch_1)
+        # gt_dataset = PCDPathDataset(args.batch_1,  transformation_dict['gso'])
+        gt_dataset = PCDPathDataset(args.batch_1,  transformation_dict['gso'], rand_aug=False)
+        # gt_dataset = PCDPathDataset(args.batch_1,  None, rand_aug=True)
+
+        # gt
+        gt_loader = torch.utils.data.DataLoader(gt_dataset,
+                                                    batch_size=64,
+                                                    shuffle=False,
+                                                    drop_last=False,
+                                                    num_workers=worker)
+        features_1, preds_1 = clf.features_and_preds(gt_loader)
+        np.save(feat_1_path, features_1)
+        np.save(pred_1_path, preds_1)
+
+    feat_2_path = os.path.join(args.batch_2, 'feat.npy')
+    pred_2_path = os.path.join(args.batch_2, 'pred.npy')
+
+    if not force_recompute and all(os.path.exists(path) for path in [feat_2_path, pred_2_path]):
+        features_2 = np.load(feat_2_path)
+        preds_2 = np.load(pred_2_path)
+        print("loading activations", args.batch_2)
+    else:
+
+        gen_path_base='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/'
+        method_name = str(Path(args.batch_2).relative_to(gen_path_base))
+
+        # print("computing activations", args.batch_2)
+        print("computing activations", method_name)
+        # method_name = args.batch_2.split('/')[-1]
+        # st()
+
+        pcd_transformation = transformation_dict[method_name]
+        pred_dataset = PCDPathDataset(args.batch_2, transformation=pcd_transformation, rand_aug=False)
+        # pred_dataset = PCDPathDataset(args.batch_2, transformation=None, rand_aug=True)
+
+        # worker=0
+        pred_loader = torch.utils.data.DataLoader(pred_dataset,
+                                                    batch_size=64,
+                                                    shuffle=False,
+                                                    drop_last=False,
+                                                    num_workers=worker)
+        features_2, preds_2 = clf.features_and_preds(pred_loader)
+        np.save(feat_2_path, features_2)
+        np.save(feat_2_path, preds_2)
+
+    print("computing statistics")
+
+    stats_1 = compute_statistics(features_1)
+    # print(features_1.max(), features_1.min(), features_1.mean(), features_1.std() )
+    # print(stats_1.mu.shape, stats_1.sigma.shape)
+
+    stats_2 = compute_statistics(features_2)
+    # print(features_2.max(), features_2.min(), features_2.mean(), features_2.std() )
+    # print(stats_2.mu.shape, stats_2.sigma.shape)
+
+    # if return_feature:
+    #     return features_1, features_2
+    
+    #PFID = stats_1.frechet_distance(stats_2) # same result as the next line
+    PFID= frechet_distance(stats_1.mu, stats_1.sigma, stats_2.mu, stats_2.sigma)
+    PKID = kernel_distance(features_1, features_2)
+
+    # _, preds = clf.features_and_preds(pred_loader)
+
+    # print(f"P-IS: {compute_inception_score(preds)}")
+    # print(f"P-IS: {compute_inception_score(preds)}")
+    method_name = args.batch_2.split('/')[-1]
+
+    # print(method_name, f"P-FID: {PFID}", f"P-KID: {PKID}", f"P-IS: {compute_inception_score(preds_2)}")
+    print(method_name, f"P-FID: {PFID}", f"P-KID: {PKID}")
+    # return dict(PFID=PFID, PKID=PKID)
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/npz_stream.py b/evaluations/fidkid-pytorch/3d-metrics/evals/npz_stream.py
new file mode 100644
index 0000000000000000000000000000000000000000..12516392734b579542b2d6f9920d97eff444a562
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/npz_stream.py
@@ -0,0 +1,270 @@
+import glob
+import io
+import os
+import re
+import zipfile
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Dict, Iterator, List, Optional, Sequence, Tuple
+
+import numpy as np
+
+
+@dataclass
+class NumpyArrayInfo:
+    """
+    Information about an array in an npz file.
+    """
+
+    name: str
+    dtype: np.dtype
+    shape: Tuple[int]
+
+    @classmethod
+    def infos_from_first_file(cls, glob_path: str) -> Dict[str, "NumpyArrayInfo"]:
+        paths, _ = _npz_paths_and_length(glob_path)
+        return cls.infos_from_file(paths[0])
+
+    @classmethod
+    def infos_from_file(cls, npz_path: str) -> Dict[str, "NumpyArrayInfo"]:
+        """
+        Extract the info of every array in an npz file.
+        """
+        if not os.path.exists(npz_path):
+            raise FileNotFoundError(f"batch of samples was not found: {npz_path}")
+        results = {}
+        with open(npz_path, "rb") as f:
+            with zipfile.ZipFile(f, "r") as zip_f:
+                for name in zip_f.namelist():
+                    if not name.endswith(".npy"):
+                        continue
+                    key_name = name[: -len(".npy")]
+                    with zip_f.open(name, "r") as arr_f:
+                        version = np.lib.format.read_magic(arr_f)
+                        if version == (1, 0):
+                            header = np.lib.format.read_array_header_1_0(arr_f)
+                        elif version == (2, 0):
+                            header = np.lib.format.read_array_header_2_0(arr_f)
+                        else:
+                            raise ValueError(f"unknown numpy array version: {version}")
+                        shape, _, dtype = header
+                        results[key_name] = cls(name=key_name, dtype=dtype, shape=shape)
+        return results
+
+    @property
+    def elem_shape(self) -> Tuple[int]:
+        return self.shape[1:]
+
+    def validate(self):
+        if self.name in {"R", "G", "B"}:
+            if len(self.shape) != 2:
+                raise ValueError(
+                    f"expecting exactly 2-D shape for '{self.name}' but got: {self.shape}"
+                )
+        elif self.name == "arr_0":
+            if len(self.shape) < 2:
+                raise ValueError(f"expecting at least 2-D shape but got: {self.shape}")
+            elif len(self.shape) == 3:
+                # For audio, we require continuous samples.
+                if not np.issubdtype(self.dtype, np.floating):
+                    raise ValueError(
+                        f"invalid dtype for audio batch: {self.dtype} (expected float)"
+                    )
+            elif self.dtype != np.uint8:
+                raise ValueError(f"invalid dtype for image batch: {self.dtype} (expected uint8)")
+
+
+class NpzStreamer:
+    def __init__(self, glob_path: str):
+        self.paths, self.trunc_length = _npz_paths_and_length(glob_path)
+        self.infos = NumpyArrayInfo.infos_from_file(self.paths[0])
+
+    def keys(self) -> List[str]:
+        return list(self.infos.keys())
+
+    def stream(self, batch_size: int, keys: Sequence[str]) -> Iterator[Dict[str, np.ndarray]]:
+        cur_batch = None
+        num_remaining = self.trunc_length
+        for path in self.paths:
+            if num_remaining is not None and num_remaining <= 0:
+                break
+            with open_npz_arrays(path, keys) as readers:
+                combined_reader = CombinedReader(keys, readers)
+                while num_remaining is None or num_remaining > 0:
+                    read_bs = batch_size
+                    if cur_batch is not None:
+                        read_bs -= _dict_batch_size(cur_batch)
+                    if num_remaining is not None:
+                        read_bs = min(read_bs, num_remaining)
+
+                    batch = combined_reader.read_batch(read_bs)
+                    if batch is None:
+                        break
+                    if num_remaining is not None:
+                        num_remaining -= _dict_batch_size(batch)
+                    if cur_batch is None:
+                        cur_batch = batch
+                    else:
+                        cur_batch = {
+                            # pylint: disable=unsubscriptable-object
+                            k: np.concatenate([cur_batch[k], v], axis=0)
+                            for k, v in batch.items()
+                        }
+                    if _dict_batch_size(cur_batch) == batch_size:
+                        yield cur_batch
+                        cur_batch = None
+        if cur_batch is not None:
+            yield cur_batch
+
+
+def _npz_paths_and_length(glob_path: str) -> Tuple[List[str], Optional[int]]:
+    # Match slice syntax like path[:100].
+    count_match = re.match("^(.*)\\[:([0-9]*)\\]$", glob_path)
+    if count_match:
+        raw_path = count_match[1]
+        max_count = int(count_match[2])
+    else:
+        raw_path = glob_path
+        max_count = None
+    paths = sorted(glob.glob(raw_path))
+    if not len(paths):
+        raise ValueError(f"no paths found matching: {glob_path}")
+    return paths, max_count
+
+
+class NpzArrayReader(ABC):
+    @abstractmethod
+    def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
+        pass
+
+
+class StreamingNpzArrayReader(NpzArrayReader):
+    def __init__(self, arr_f, shape, dtype):
+        self.arr_f = arr_f
+        self.shape = shape
+        self.dtype = dtype
+        self.idx = 0
+
+    def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
+        if self.idx >= self.shape[0]:
+            return None
+
+        bs = min(batch_size, self.shape[0] - self.idx)
+        self.idx += bs
+
+        if self.dtype.itemsize == 0:
+            return np.ndarray([bs, *self.shape[1:]], dtype=self.dtype)
+
+        read_count = bs * np.prod(self.shape[1:])
+        read_size = int(read_count * self.dtype.itemsize)
+        data = _read_bytes(self.arr_f, read_size, "array data")
+        return np.frombuffer(data, dtype=self.dtype).reshape([bs, *self.shape[1:]])
+
+
+class MemoryNpzArrayReader(NpzArrayReader):
+    def __init__(self, arr):
+        self.arr = arr
+        self.idx = 0
+
+    @classmethod
+    def load(cls, path: str, arr_name: str):
+        with open(path, "rb") as f:
+            arr = np.load(f)[arr_name]
+        return cls(arr)
+
+    def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
+        if self.idx >= self.arr.shape[0]:
+            return None
+
+        res = self.arr[self.idx : self.idx + batch_size]
+        self.idx += batch_size
+        return res
+
+
+@contextmanager
+def open_npz_arrays(path: str, arr_names: Sequence[str]) -> List[NpzArrayReader]:
+    if not len(arr_names):
+        yield []
+        return
+    arr_name = arr_names[0]
+    with open_array(path, arr_name) as arr_f:
+        version = np.lib.format.read_magic(arr_f)
+        header = None
+        if version == (1, 0):
+            header = np.lib.format.read_array_header_1_0(arr_f)
+        elif version == (2, 0):
+            header = np.lib.format.read_array_header_2_0(arr_f)
+
+        if header is None:
+            reader = MemoryNpzArrayReader.load(path, arr_name)
+        else:
+            shape, fortran, dtype = header
+            if fortran or dtype.hasobject:
+                reader = MemoryNpzArrayReader.load(path, arr_name)
+            else:
+                reader = StreamingNpzArrayReader(arr_f, shape, dtype)
+
+        with open_npz_arrays(path, arr_names[1:]) as next_readers:
+            yield [reader] + next_readers
+
+
+class CombinedReader:
+    def __init__(self, keys: List[str], readers: List[NpzArrayReader]):
+        self.keys = keys
+        self.readers = readers
+
+    def read_batch(self, batch_size: int) -> Optional[Dict[str, np.ndarray]]:
+        batches = [r.read_batch(batch_size) for r in self.readers]
+        any_none = any(x is None for x in batches)
+        all_none = all(x is None for x in batches)
+        if any_none != all_none:
+            raise RuntimeError("different keys had different numbers of elements")
+        if any_none:
+            return None
+        if any(len(x) != len(batches[0]) for x in batches):
+            raise RuntimeError("different keys had different numbers of elements")
+        return dict(zip(self.keys, batches))
+
+
+def _read_bytes(fp, size, error_template="ran out of data"):
+    """
+    Copied from: https://github.com/numpy/numpy/blob/fb215c76967739268de71aa4bda55dd1b062bc2e/numpy/lib/format.py#L788-L886
+
+    Read from file-like object until size bytes are read.
+    Raises ValueError if not EOF is encountered before size bytes are read.
+    Non-blocking objects only supported if they derive from io objects.
+    Required as e.g. ZipExtFile in python 2.6 can return less data than
+    requested.
+    """
+    data = bytes()
+    while True:
+        # io files (default in python3) return None or raise on
+        # would-block, python2 file will truncate, probably nothing can be
+        # done about that.  note that regular files can't be non-blocking
+        try:
+            r = fp.read(size - len(data))
+            data += r
+            if len(r) == 0 or len(data) == size:
+                break
+        except io.BlockingIOError:
+            pass
+    if len(data) != size:
+        msg = "EOF: reading %s, expected %d bytes got %d"
+        raise ValueError(msg % (error_template, size, len(data)))
+    else:
+        return data
+
+
+@contextmanager
+def open_array(path: str, arr_name: str):
+    with open(path, "rb") as f:
+        with zipfile.ZipFile(f, "r") as zip_f:
+            if f"{arr_name}.npy" not in zip_f.namelist():
+                raise ValueError(f"missing {arr_name} in npz file")
+            with zip_f.open(f"{arr_name}.npy", "r") as arr_f:
+                yield arr_f
+
+
+def _dict_batch_size(objs: Dict[str, np.ndarray]) -> int:
+    return len(next(iter(objs.values())))
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/pointnet2_cls_ssg.py b/evaluations/fidkid-pytorch/3d-metrics/evals/pointnet2_cls_ssg.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b1d736f459fefb7a2d33c7c8c863f9666a21d19
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/pointnet2_cls_ssg.py
@@ -0,0 +1,101 @@
+"""
+Based on: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/eb64fe0b4c24055559cea26299cb485dcb43d8dd/models/pointnet2_cls_ssg.py
+
+MIT License
+
+Copyright (c) 2019 benny
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+from pointnet2_utils import PointNetSetAbstraction
+
+
+class get_model(nn.Module):
+    def __init__(self, num_class, normal_channel=True, width_mult=1):
+        super(get_model, self).__init__()
+        self.width_mult = width_mult
+        in_channel = 6 if normal_channel else 3
+        self.normal_channel = normal_channel
+        self.sa1 = PointNetSetAbstraction(
+            npoint=512,
+            radius=0.2,
+            nsample=32,
+            in_channel=in_channel,
+            mlp=[64 * width_mult, 64 * width_mult, 128 * width_mult],
+            group_all=False,
+        )
+        self.sa2 = PointNetSetAbstraction(
+            npoint=128,
+            radius=0.4,
+            nsample=64,
+            in_channel=128 * width_mult + 3,
+            mlp=[128 * width_mult, 128 * width_mult, 256 * width_mult],
+            group_all=False,
+        )
+        self.sa3 = PointNetSetAbstraction(
+            npoint=None,
+            radius=None,
+            nsample=None,
+            in_channel=256 * width_mult + 3,
+            mlp=[256 * width_mult, 512 * width_mult, 1024 * width_mult],
+            group_all=True,
+        )
+        self.fc1 = nn.Linear(1024 * width_mult, 512 * width_mult)
+        self.bn1 = nn.BatchNorm1d(512 * width_mult)
+        self.drop1 = nn.Dropout(0.4)
+        self.fc2 = nn.Linear(512 * width_mult, 256 * width_mult)
+        self.bn2 = nn.BatchNorm1d(256 * width_mult)
+        self.drop2 = nn.Dropout(0.4)
+        self.fc3 = nn.Linear(256 * width_mult, num_class)
+
+    def forward(self, xyz, features=False):
+        B, _, _ = xyz.shape
+        if self.normal_channel:
+            norm = xyz[:, 3:, :]
+            xyz = xyz[:, :3, :]
+        else:
+            norm = None
+        l1_xyz, l1_points = self.sa1(xyz, norm)
+        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
+        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)
+        x = l3_points.view(B, 1024 * self.width_mult)
+        x = self.drop1(F.relu(self.bn1(self.fc1(x))))
+        result_features = self.bn2(self.fc2(x))
+        x = self.drop2(F.relu(result_features))
+        x = self.fc3(x)
+        x = F.log_softmax(x, -1)
+
+        if features:
+            return x, l3_points, result_features
+        else:
+            return x, l3_points
+
+
+class get_loss(nn.Module):
+    def __init__(self):
+        super(get_loss, self).__init__()
+
+    def forward(self, pred, target, trans_feat):
+        total_loss = F.nll_loss(pred, target)
+
+        return total_loss
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/pointnet2_utils.py b/evaluations/fidkid-pytorch/3d-metrics/evals/pointnet2_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..409188b4e7db59f9ee30945f43fb4731479216ae
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/pointnet2_utils.py
@@ -0,0 +1,356 @@
+"""
+Based on: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/eb64fe0b4c24055559cea26299cb485dcb43d8dd/models/pointnet_utils.py
+
+MIT License
+
+Copyright (c) 2019 benny
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+from time import time
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def timeit(tag, t):
+    print("{}: {}s".format(tag, time() - t))
+    return time()
+
+
+def pc_normalize(pc):
+    l = pc.shape[0]
+    centroid = np.mean(pc, axis=0)
+    pc = pc - centroid
+    m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
+    pc = pc / m
+    return pc
+
+
+def square_distance(src, dst):
+    """
+    Calculate Euclid distance between each two points.
+
+    src^T * dst = xn * xm + yn * ym + zn * zm;
+    sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
+    sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
+    dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
+         = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst
+
+    Input:
+        src: source points, [B, N, C]
+        dst: target points, [B, M, C]
+    Output:
+        dist: per-point square distance, [B, N, M]
+    """
+    B, N, _ = src.shape
+    _, M, _ = dst.shape
+    dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
+    dist += torch.sum(src**2, -1).view(B, N, 1)
+    dist += torch.sum(dst**2, -1).view(B, 1, M)
+    return dist
+
+
+def index_points(points, idx):
+    """
+
+    Input:
+        points: input points data, [B, N, C]
+        idx: sample index data, [B, S]
+    Return:
+        new_points:, indexed points data, [B, S, C]
+    """
+    device = points.device
+    B = points.shape[0]
+    view_shape = list(idx.shape)
+    view_shape[1:] = [1] * (len(view_shape) - 1)
+    repeat_shape = list(idx.shape)
+    repeat_shape[0] = 1
+    batch_indices = (
+        torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
+    )
+    new_points = points[batch_indices, idx, :]
+    return new_points
+
+
+def farthest_point_sample(xyz, npoint, deterministic=False):
+    """
+    Input:
+        xyz: pointcloud data, [B, N, 3]
+        npoint: number of samples
+    Return:
+        centroids: sampled pointcloud index, [B, npoint]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
+    distance = torch.ones(B, N).to(device) * 1e10
+    if deterministic:
+        farthest = torch.arange(0, B, dtype=torch.long).to(device)
+    else:
+        farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
+    batch_indices = torch.arange(B, dtype=torch.long).to(device)
+    for i in range(npoint):
+        centroids[:, i] = farthest
+        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)
+        dist = torch.sum((xyz - centroid) ** 2, -1)
+        mask = dist < distance
+        distance[mask] = dist[mask]
+        farthest = torch.max(distance, -1)[1]
+    return centroids
+
+
+def query_ball_point(radius, nsample, xyz, new_xyz):
+    """
+    Input:
+        radius: local region radius
+        nsample: max sample number in local region
+        xyz: all points, [B, N, 3]
+        new_xyz: query points, [B, S, 3]
+    Return:
+        group_idx: grouped points index, [B, S, nsample]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    _, S, _ = new_xyz.shape
+    group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
+    sqrdists = square_distance(new_xyz, xyz)
+    group_idx[sqrdists > radius**2] = N
+    group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]
+    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])
+    mask = group_idx == N
+    group_idx[mask] = group_first[mask]
+    return group_idx
+
+
+def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False, deterministic=False):
+    """
+    Input:
+        npoint:
+        radius:
+        nsample:
+        xyz: input points position data, [B, N, 3]
+        points: input points data, [B, N, D]
+    Return:
+        new_xyz: sampled points position data, [B, npoint, nsample, 3]
+        new_points: sampled points data, [B, npoint, nsample, 3+D]
+    """
+    B, N, C = xyz.shape
+    S = npoint
+    fps_idx = farthest_point_sample(xyz, npoint, deterministic=deterministic)  # [B, npoint, C]
+    new_xyz = index_points(xyz, fps_idx)
+    idx = query_ball_point(radius, nsample, xyz, new_xyz)
+    grouped_xyz = index_points(xyz, idx)  # [B, npoint, nsample, C]
+    grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)
+
+    if points is not None:
+        grouped_points = index_points(points, idx)
+        new_points = torch.cat(
+            [grouped_xyz_norm, grouped_points], dim=-1
+        )  # [B, npoint, nsample, C+D]
+    else:
+        new_points = grouped_xyz_norm
+    if returnfps:
+        return new_xyz, new_points, grouped_xyz, fps_idx
+    else:
+        return new_xyz, new_points
+
+
+def sample_and_group_all(xyz, points):
+    """
+    Input:
+        xyz: input points position data, [B, N, 3]
+        points: input points data, [B, N, D]
+    Return:
+        new_xyz: sampled points position data, [B, 1, 3]
+        new_points: sampled points data, [B, 1, N, 3+D]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    new_xyz = torch.zeros(B, 1, C).to(device)
+    grouped_xyz = xyz.view(B, 1, N, C)
+    if points is not None:
+        new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1)
+    else:
+        new_points = grouped_xyz
+    return new_xyz, new_points
+
+
+class PointNetSetAbstraction(nn.Module):
+    def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):
+        super(PointNetSetAbstraction, self).__init__()
+        self.npoint = npoint
+        self.radius = radius
+        self.nsample = nsample
+        self.mlp_convs = nn.ModuleList()
+        self.mlp_bns = nn.ModuleList()
+        last_channel = in_channel
+        for out_channel in mlp:
+            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))
+            self.mlp_bns.append(nn.BatchNorm2d(out_channel))
+            last_channel = out_channel
+        self.group_all = group_all
+
+    def forward(self, xyz, points):
+        """
+        Input:
+            xyz: input points position data, [B, C, N]
+            points: input points data, [B, D, N]
+        Return:
+            new_xyz: sampled points position data, [B, C, S]
+            new_points_concat: sample points feature data, [B, D', S]
+        """
+        xyz = xyz.permute(0, 2, 1)
+        if points is not None:
+            points = points.permute(0, 2, 1)
+
+        if self.group_all:
+            new_xyz, new_points = sample_and_group_all(xyz, points)
+        else:
+            new_xyz, new_points = sample_and_group(
+                self.npoint, self.radius, self.nsample, xyz, points, deterministic=not self.training
+            )
+        # new_xyz: sampled points position data, [B, npoint, C]
+        # new_points: sampled points data, [B, npoint, nsample, C+D]
+        new_points = new_points.permute(0, 3, 2, 1)  # [B, C+D, nsample,npoint]
+        for i, conv in enumerate(self.mlp_convs):
+            bn = self.mlp_bns[i]
+            new_points = F.relu(bn(conv(new_points)))
+
+        new_points = torch.max(new_points, 2)[0]
+        new_xyz = new_xyz.permute(0, 2, 1)
+        return new_xyz, new_points
+
+
+class PointNetSetAbstractionMsg(nn.Module):
+    def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list):
+        super(PointNetSetAbstractionMsg, self).__init__()
+        self.npoint = npoint
+        self.radius_list = radius_list
+        self.nsample_list = nsample_list
+        self.conv_blocks = nn.ModuleList()
+        self.bn_blocks = nn.ModuleList()
+        for i in range(len(mlp_list)):
+            convs = nn.ModuleList()
+            bns = nn.ModuleList()
+            last_channel = in_channel + 3
+            for out_channel in mlp_list[i]:
+                convs.append(nn.Conv2d(last_channel, out_channel, 1))
+                bns.append(nn.BatchNorm2d(out_channel))
+                last_channel = out_channel
+            self.conv_blocks.append(convs)
+            self.bn_blocks.append(bns)
+
+    def forward(self, xyz, points):
+        """
+        Input:
+            xyz: input points position data, [B, C, N]
+            points: input points data, [B, D, N]
+        Return:
+            new_xyz: sampled points position data, [B, C, S]
+            new_points_concat: sample points feature data, [B, D', S]
+        """
+        xyz = xyz.permute(0, 2, 1)
+        if points is not None:
+            points = points.permute(0, 2, 1)
+
+        B, N, C = xyz.shape
+        S = self.npoint
+        new_xyz = index_points(xyz, farthest_point_sample(xyz, S, deterministic=not self.training))
+        new_points_list = []
+        for i, radius in enumerate(self.radius_list):
+            K = self.nsample_list[i]
+            group_idx = query_ball_point(radius, K, xyz, new_xyz)
+            grouped_xyz = index_points(xyz, group_idx)
+            grouped_xyz -= new_xyz.view(B, S, 1, C)
+            if points is not None:
+                grouped_points = index_points(points, group_idx)
+                grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1)
+            else:
+                grouped_points = grouped_xyz
+
+            grouped_points = grouped_points.permute(0, 3, 2, 1)  # [B, D, K, S]
+            for j in range(len(self.conv_blocks[i])):
+                conv = self.conv_blocks[i][j]
+                bn = self.bn_blocks[i][j]
+                grouped_points = F.relu(bn(conv(grouped_points)))
+            new_points = torch.max(grouped_points, 2)[0]  # [B, D', S]
+            new_points_list.append(new_points)
+
+        new_xyz = new_xyz.permute(0, 2, 1)
+        new_points_concat = torch.cat(new_points_list, dim=1)
+        return new_xyz, new_points_concat
+
+
+class PointNetFeaturePropagation(nn.Module):
+    def __init__(self, in_channel, mlp):
+        super(PointNetFeaturePropagation, self).__init__()
+        self.mlp_convs = nn.ModuleList()
+        self.mlp_bns = nn.ModuleList()
+        last_channel = in_channel
+        for out_channel in mlp:
+            self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))
+            self.mlp_bns.append(nn.BatchNorm1d(out_channel))
+            last_channel = out_channel
+
+    def forward(self, xyz1, xyz2, points1, points2):
+        """
+        Input:
+            xyz1: input points position data, [B, C, N]
+            xyz2: sampled input points position data, [B, C, S]
+            points1: input points data, [B, D, N]
+            points2: input points data, [B, D, S]
+        Return:
+            new_points: upsampled points data, [B, D', N]
+        """
+        xyz1 = xyz1.permute(0, 2, 1)
+        xyz2 = xyz2.permute(0, 2, 1)
+
+        points2 = points2.permute(0, 2, 1)
+        B, N, C = xyz1.shape
+        _, S, _ = xyz2.shape
+
+        if S == 1:
+            interpolated_points = points2.repeat(1, N, 1)
+        else:
+            dists = square_distance(xyz1, xyz2)
+            dists, idx = dists.sort(dim=-1)
+            dists, idx = dists[:, :, :3], idx[:, :, :3]  # [B, N, 3]
+
+            dist_recip = 1.0 / (dists + 1e-8)
+            norm = torch.sum(dist_recip, dim=2, keepdim=True)
+            weight = dist_recip / norm
+            interpolated_points = torch.sum(
+                index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2
+            )
+
+        if points1 is not None:
+            points1 = points1.permute(0, 2, 1)
+            new_points = torch.cat([points1, interpolated_points], dim=-1)
+        else:
+            new_points = interpolated_points
+
+        new_points = new_points.permute(0, 2, 1)
+        for i, conv in enumerate(self.mlp_convs):
+            bn = self.mlp_bns[i]
+            new_points = F.relu(bn(conv(new_points)))
+        return new_points
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/readme.md b/evaluations/fidkid-pytorch/3d-metrics/evals/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..15a189469a60ad696ecd5ad0642cf423b9258641
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/readme.md
@@ -0,0 +1,5 @@
+All metrics: p-FID, p-KID, p-IS
+
+Mainly copied from ```https://github.com/openai/point-e/point_e/evals/```.
+
+The p-KID is copied from ```https://github.com/3dlg-hcvc/omages/blob/main/src/evals/fpd_eval.py```
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/blender_script.py b/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/blender_script.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ddb1cbe181db83f088df5c8c948cadaf49599d1
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/blender_script.py
@@ -0,0 +1,533 @@
+"""
+Script to run within Blender to render a 3D model as RGBAD images.
+
+Example usage
+
+    blender -b -P blender_script.py -- \
+        --input_path ../../examples/example_data/corgi.ply \
+        --output_path render_out
+
+Pass `--camera_pose z-circular-elevated` for the rendering used to compute
+CLIP R-Precision results.
+
+The output directory will include metadata json files for each rendered view,
+as well as a global metadata file for the render. Each image will be saved as
+a collection of 16-bit PNG files for each channel (rgbad), as well as a full
+grayscale render of the view.
+"""
+
+import argparse
+import json
+import math
+import os
+import random
+import sys
+
+import bpy
+from mathutils import Vector
+from mathutils.noise import random_unit_vector
+
+MAX_DEPTH = 5.0
+FORMAT_VERSION = 6
+UNIFORM_LIGHT_DIRECTION = [0.09387503, -0.63953443, -0.7630093]
+
+
+def clear_scene():
+    bpy.ops.object.select_all(action="SELECT")
+    bpy.ops.object.delete()
+
+
+def clear_lights():
+    bpy.ops.object.select_all(action="DESELECT")
+    for obj in bpy.context.scene.objects.values():
+        if isinstance(obj.data, bpy.types.Light):
+            obj.select_set(True)
+    bpy.ops.object.delete()
+
+
+def import_model(path):
+    clear_scene()
+    _, ext = os.path.splitext(path)
+    ext = ext.lower()
+    if ext == ".obj":
+        bpy.ops.import_scene.obj(filepath=path)
+    elif ext in [".glb", ".gltf"]:
+        bpy.ops.import_scene.gltf(filepath=path)
+    elif ext == ".stl":
+        bpy.ops.import_mesh.stl(filepath=path)
+    elif ext == ".fbx":
+        bpy.ops.import_scene.fbx(filepath=path)
+    elif ext == ".dae":
+        bpy.ops.wm.collada_import(filepath=path)
+    elif ext == ".ply":
+        bpy.ops.import_mesh.ply(filepath=path)
+    else:
+        raise RuntimeError(f"unexpected extension: {ext}")
+
+
+def scene_root_objects():
+    for obj in bpy.context.scene.objects.values():
+        if not obj.parent:
+            yield obj
+
+
+def scene_bbox(single_obj=None, ignore_matrix=False):
+    bbox_min = (math.inf,) * 3
+    bbox_max = (-math.inf,) * 3
+    found = False
+    for obj in scene_meshes() if single_obj is None else [single_obj]:
+        found = True
+        for coord in obj.bound_box:
+            coord = Vector(coord)
+            if not ignore_matrix:
+                coord = obj.matrix_world @ coord
+            bbox_min = tuple(min(x, y) for x, y in zip(bbox_min, coord))
+            bbox_max = tuple(max(x, y) for x, y in zip(bbox_max, coord))
+    if not found:
+        raise RuntimeError("no objects in scene to compute bounding box for")
+    return Vector(bbox_min), Vector(bbox_max)
+
+
+def scene_meshes():
+    for obj in bpy.context.scene.objects.values():
+        if isinstance(obj.data, (bpy.types.Mesh)):
+            yield obj
+
+
+def normalize_scene():
+    bbox_min, bbox_max = scene_bbox()
+    scale = 1 / max(bbox_max - bbox_min)
+
+    for obj in scene_root_objects():
+        obj.scale = obj.scale * scale
+
+    # Apply scale to matrix_world.
+    bpy.context.view_layer.update()
+
+    bbox_min, bbox_max = scene_bbox()
+    offset = -(bbox_min + bbox_max) / 2
+    for obj in scene_root_objects():
+        obj.matrix_world.translation += offset
+
+    bpy.ops.object.select_all(action="DESELECT")
+
+
+def create_camera():
+    # https://b3d.interplanety.org/en/how-to-create-camera-through-the-blender-python-api/
+    camera_data = bpy.data.cameras.new(name="Camera")
+    camera_object = bpy.data.objects.new("Camera", camera_data)
+    bpy.context.scene.collection.objects.link(camera_object)
+    bpy.context.scene.camera = camera_object
+
+
+def set_camera(direction, camera_dist=2.0):
+    camera_pos = -camera_dist * direction
+    bpy.context.scene.camera.location = camera_pos
+
+    # https://blender.stackexchange.com/questions/5210/pointing-the-camera-in-a-particular-direction-programmatically
+    rot_quat = direction.to_track_quat("-Z", "Y")
+    bpy.context.scene.camera.rotation_euler = rot_quat.to_euler()
+
+    bpy.context.view_layer.update()
+
+
+def randomize_camera(camera_dist=2.0):
+    direction = random_unit_vector()
+    set_camera(direction, camera_dist=camera_dist)
+
+
+def pan_camera(time, axis="Z", camera_dist=2.0, elevation=-0.1):
+    angle = time * math.pi * 2
+    direction = [-math.cos(angle), -math.sin(angle), -elevation]
+    assert axis in ["X", "Y", "Z"]
+    if axis == "X":
+        direction = [direction[2], *direction[:2]]
+    elif axis == "Y":
+        direction = [direction[0], -elevation, direction[1]]
+    direction = Vector(direction).normalized()
+    set_camera(direction, camera_dist=camera_dist)
+
+
+def place_camera(time, camera_pose_mode="random", camera_dist_min=2.0, camera_dist_max=2.0):
+    camera_dist = random.uniform(camera_dist_min, camera_dist_max)
+    if camera_pose_mode == "random":
+        randomize_camera(camera_dist=camera_dist)
+    elif camera_pose_mode == "z-circular":
+        pan_camera(time, axis="Z", camera_dist=camera_dist)
+    elif camera_pose_mode == "z-circular-elevated":
+        pan_camera(time, axis="Z", camera_dist=camera_dist, elevation=0.2617993878)
+    else:
+        raise ValueError(f"Unknown camera pose mode: {camera_pose_mode}")
+
+
+def create_light(location, energy=1.0, angle=0.5 * math.pi / 180):
+    # https://blender.stackexchange.com/questions/215624/how-to-create-a-light-with-the-python-api-in-blender-2-92
+    light_data = bpy.data.lights.new(name="Light", type="SUN")
+    light_data.energy = energy
+    light_data.angle = angle
+    light_object = bpy.data.objects.new(name="Light", object_data=light_data)
+
+    direction = -location
+    rot_quat = direction.to_track_quat("-Z", "Y")
+    light_object.rotation_euler = rot_quat.to_euler()
+    bpy.context.view_layer.update()
+
+    bpy.context.collection.objects.link(light_object)
+    light_object.location = location
+
+
+def create_random_lights(count=4, distance=2.0, energy=1.5):
+    clear_lights()
+    for _ in range(count):
+        create_light(random_unit_vector() * distance, energy=energy)
+
+
+def create_camera_light():
+    clear_lights()
+    create_light(bpy.context.scene.camera.location, energy=5.0)
+
+
+def create_uniform_light(backend):
+    clear_lights()
+    # Random direction to decorrelate axis-aligned sides.
+    pos = Vector(UNIFORM_LIGHT_DIRECTION)
+    angle = 0.0092 if backend == "CYCLES" else math.pi
+    create_light(pos, energy=5.0, angle=angle)
+    create_light(-pos, energy=5.0, angle=angle)
+
+
+def create_vertex_color_shaders():
+    # By default, Blender will ignore vertex colors in both the
+    # Eevee and Cycles backends, since these colors aren't
+    # associated with a material.
+    #
+    # What we do here is create a simple material shader and link
+    # the vertex color to the material color.
+    for obj in bpy.context.scene.objects.values():
+        if not isinstance(obj.data, (bpy.types.Mesh)):
+            continue
+
+        if len(obj.data.materials):
+            # We don't want to override any existing materials.
+            continue
+
+        color_keys = (obj.data.vertex_colors or {}).keys()
+        if not len(color_keys):
+            # Many objects will have no materials *or* vertex colors.
+            continue
+
+        mat = bpy.data.materials.new(name="VertexColored")
+        mat.use_nodes = True
+
+        # There should be a Principled BSDF by default.
+        bsdf_node = None
+        for node in mat.node_tree.nodes:
+            if node.type == "BSDF_PRINCIPLED":
+                bsdf_node = node
+        assert bsdf_node is not None, "material has no Principled BSDF node to modify"
+
+        socket_map = {}
+        for input in bsdf_node.inputs:
+            socket_map[input.name] = input
+
+        # Make sure nothing lights the object except for the diffuse color.
+        socket_map["Specular"].default_value = 0.0
+        socket_map["Roughness"].default_value = 1.0
+
+        v_color = mat.node_tree.nodes.new("ShaderNodeVertexColor")
+        v_color.layer_name = color_keys[0]
+
+        mat.node_tree.links.new(v_color.outputs[0], socket_map["Base Color"])
+
+        obj.data.materials.append(mat)
+
+
+def create_default_materials():
+    for obj in bpy.context.scene.objects.values():
+        if isinstance(obj.data, (bpy.types.Mesh)):
+            if not len(obj.data.materials):
+                mat = bpy.data.materials.new(name="DefaultMaterial")
+                mat.use_nodes = True
+                obj.data.materials.append(mat)
+
+
+def find_materials():
+    all_materials = set()
+    for obj in bpy.context.scene.objects.values():
+        if not isinstance(obj.data, (bpy.types.Mesh)):
+            continue
+        for mat in obj.data.materials:
+            all_materials.add(mat)
+    return all_materials
+
+
+def get_socket_value(tree, socket):
+    default = socket.default_value
+    if not isinstance(default, float):
+        default = list(default)
+    for link in tree.links:
+        if link.to_socket == socket:
+            return (link.from_socket, default)
+    return (None, default)
+
+
+def clear_socket_input(tree, socket):
+    for link in list(tree.links):
+        if link.to_socket == socket:
+            tree.links.remove(link)
+
+
+def set_socket_value(tree, socket, socket_and_default):
+    clear_socket_input(tree, socket)
+    old_source_socket, default = socket_and_default
+    if isinstance(default, float) and not isinstance(socket.default_value, float):
+        # Codepath for setting Emission to a previous alpha value.
+        socket.default_value = [default] * 3 + [1.0]
+    else:
+        socket.default_value = default
+    if old_source_socket is not None:
+        tree.links.new(old_source_socket, socket)
+
+
+def setup_nodes(output_path, capturing_material_alpha: bool = False):
+    tree = bpy.context.scene.node_tree
+    links = tree.links
+
+    for node in tree.nodes:
+        tree.nodes.remove(node)
+
+    # Helpers to perform math on links and constants.
+    def node_op(op: str, *args, clamp=False):
+        node = tree.nodes.new(type="CompositorNodeMath")
+        node.operation = op
+        if clamp:
+            node.use_clamp = True
+        for i, arg in enumerate(args):
+            if isinstance(arg, (int, float)):
+                node.inputs[i].default_value = arg
+            else:
+                links.new(arg, node.inputs[i])
+        return node.outputs[0]
+
+    def node_clamp(x, maximum=1.0):
+        return node_op("MINIMUM", x, maximum)
+
+    def node_mul(x, y, **kwargs):
+        return node_op("MULTIPLY", x, y, **kwargs)
+
+    input_node = tree.nodes.new(type="CompositorNodeRLayers")
+    input_node.scene = bpy.context.scene
+
+    input_sockets = {}
+    for output in input_node.outputs:
+        input_sockets[output.name] = output
+
+    if capturing_material_alpha:
+        color_socket = input_sockets["Image"]
+    else:
+        raw_color_socket = input_sockets["Image"]
+
+        # We apply sRGB here so that our fixed-point depth map and material
+        # alpha values are not sRGB, and so that we perform ambient+diffuse
+        # lighting in linear RGB space.
+        color_node = tree.nodes.new(type="CompositorNodeConvertColorSpace")
+        color_node.from_color_space = "Linear"
+        color_node.to_color_space = "sRGB"
+        tree.links.new(raw_color_socket, color_node.inputs[0])
+        color_socket = color_node.outputs[0]
+    split_node = tree.nodes.new(type="CompositorNodeSepRGBA")
+    tree.links.new(color_socket, split_node.inputs[0])
+    # Create separate file output nodes for every channel we care about.
+    # The process calling this script must decide how to recombine these
+    # channels, possibly into a single image.
+    for i, channel in enumerate("rgba") if not capturing_material_alpha else [(0, "MatAlpha")]:
+        output_node = tree.nodes.new(type="CompositorNodeOutputFile")
+        output_node.base_path = f"{output_path}_{channel}"
+        links.new(split_node.outputs[i], output_node.inputs[0])
+
+    if capturing_material_alpha:
+        # No need to re-write depth here.
+        return
+
+    depth_out = node_clamp(node_mul(input_sockets["Depth"], 1 / MAX_DEPTH))
+    output_node = tree.nodes.new(type="CompositorNodeOutputFile")
+    output_node.base_path = f"{output_path}_depth"
+    links.new(depth_out, output_node.inputs[0])
+
+
+def render_scene(output_path, fast_mode: bool):
+    use_workbench = bpy.context.scene.render.engine == "BLENDER_WORKBENCH"
+    if use_workbench:
+        # We must use a different engine to compute depth maps.
+        bpy.context.scene.render.engine = "BLENDER_EEVEE"
+        bpy.context.scene.eevee.taa_render_samples = 1  # faster, since we discard image.
+    if fast_mode:
+        if bpy.context.scene.render.engine == "BLENDER_EEVEE":
+            bpy.context.scene.eevee.taa_render_samples = 1
+        elif bpy.context.scene.render.engine == "CYCLES":
+            bpy.context.scene.cycles.samples = 256
+    else:
+        if bpy.context.scene.render.engine == "CYCLES":
+            # We should still impose a per-frame time limit
+            # so that we don't timeout completely.
+            bpy.context.scene.cycles.time_limit = 40
+    bpy.context.view_layer.update()
+    bpy.context.scene.use_nodes = True
+    bpy.context.scene.view_layers["ViewLayer"].use_pass_z = True
+    bpy.context.scene.view_settings.view_transform = "Raw"  # sRGB done in graph nodes
+    bpy.context.scene.render.film_transparent = True
+    bpy.context.scene.render.resolution_x = 512
+    bpy.context.scene.render.resolution_y = 512
+    bpy.context.scene.render.image_settings.file_format = "PNG"
+    bpy.context.scene.render.image_settings.color_mode = "BW"
+    bpy.context.scene.render.image_settings.color_depth = "16"
+    bpy.context.scene.render.filepath = output_path
+    setup_nodes(output_path)
+    bpy.ops.render.render(write_still=True)
+
+    # The output images must be moved from their own sub-directories, or
+    # discarded if we are using workbench for the color.
+    for channel_name in ["r", "g", "b", "a", "depth"]:
+        sub_dir = f"{output_path}_{channel_name}"
+        image_path = os.path.join(sub_dir, os.listdir(sub_dir)[0])
+        name, ext = os.path.splitext(output_path)
+        if channel_name == "depth" or not use_workbench:
+            os.rename(image_path, f"{name}_{channel_name}{ext}")
+        else:
+            os.remove(image_path)
+        os.removedirs(sub_dir)
+
+    if use_workbench:
+        # Re-render RGBA using workbench with texture mode, since this seems
+        # to show the most reasonable colors when lighting is broken.
+        bpy.context.scene.use_nodes = False
+        bpy.context.scene.render.engine = "BLENDER_WORKBENCH"
+        bpy.context.scene.render.image_settings.color_mode = "RGBA"
+        bpy.context.scene.render.image_settings.color_depth = "8"
+        bpy.context.scene.display.shading.color_type = "TEXTURE"
+        bpy.context.scene.display.shading.light = "FLAT"
+        if fast_mode:
+            # Single pass anti-aliasing.
+            bpy.context.scene.display.render_aa = "FXAA"
+        os.remove(output_path)
+        bpy.ops.render.render(write_still=True)
+        bpy.context.scene.render.image_settings.color_mode = "BW"
+        bpy.context.scene.render.image_settings.color_depth = "16"
+
+
+def scene_fov():
+    x_fov = bpy.context.scene.camera.data.angle_x
+    y_fov = bpy.context.scene.camera.data.angle_y
+    width = bpy.context.scene.render.resolution_x
+    height = bpy.context.scene.render.resolution_y
+    if bpy.context.scene.camera.data.angle == x_fov:
+        y_fov = 2 * math.atan(math.tan(x_fov / 2) * height / width)
+    else:
+        x_fov = 2 * math.atan(math.tan(y_fov / 2) * width / height)
+    return x_fov, y_fov
+
+
+def write_camera_metadata(path):
+    x_fov, y_fov = scene_fov()
+    bbox_min, bbox_max = scene_bbox()
+    matrix = bpy.context.scene.camera.matrix_world
+    with open(path, "w") as f:
+        json.dump(
+            dict(
+                format_version=FORMAT_VERSION,
+                max_depth=MAX_DEPTH,
+                bbox=[list(bbox_min), list(bbox_max)],
+                origin=list(matrix.col[3])[:3],
+                x_fov=x_fov,
+                y_fov=y_fov,
+                x=list(matrix.col[0])[:3],
+                y=list(-matrix.col[1])[:3],
+                z=list(-matrix.col[2])[:3],
+            ),
+            f,
+        )
+
+
+def save_rendering_dataset(
+    input_path: str,
+    output_path: str,
+    num_images: int,
+    backend: str,
+    light_mode: str,
+    camera_pose: str,
+    camera_dist_min: float,
+    camera_dist_max: float,
+    fast_mode: bool,
+):
+    assert light_mode in ["random", "uniform", "camera"]
+    assert camera_pose in ["random", "z-circular", "z-circular-elevated"]
+
+    import_model(input_path)
+    bpy.context.scene.render.engine = backend
+    normalize_scene()
+    if light_mode == "random":
+        create_random_lights()
+    elif light_mode == "uniform":
+        create_uniform_light(backend)
+    create_camera()
+    create_vertex_color_shaders()
+    for i in range(num_images):
+        t = i / max(num_images - 1, 1)  # same as np.linspace(0, 1, num_images)
+        place_camera(
+            t,
+            camera_pose_mode=camera_pose,
+            camera_dist_min=camera_dist_min,
+            camera_dist_max=camera_dist_max,
+        )
+        if light_mode == "camera":
+            create_camera_light()
+        render_scene(
+            os.path.join(output_path, f"{i:05}.png"),
+            fast_mode=fast_mode,
+        )
+        write_camera_metadata(os.path.join(output_path, f"{i:05}.json"))
+    with open(os.path.join(output_path, "info.json"), "w") as f:
+        info = dict(
+            backend=backend,
+            light_mode=light_mode,
+            fast_mode=fast_mode,
+            format_version=FORMAT_VERSION,
+            channels=["R", "G", "B", "A", "D"],
+            scale=0.5,  # The scene is bounded by [-scale, scale].
+        )
+        json.dump(info, f)
+
+
+def main():
+    try:
+        dash_index = sys.argv.index("--")
+    except ValueError as exc:
+        raise ValueError("arguments must be preceded by '--'") from exc
+
+    raw_args = sys.argv[dash_index + 1 :]
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_path", required=True, type=str)
+    parser.add_argument("--output_path", required=True, type=str)
+    parser.add_argument("--num_images", type=int, default=20)
+    parser.add_argument("--backend", type=str, default="BLENDER_EEVEE")
+    parser.add_argument("--light_mode", type=str, default="uniform")
+    parser.add_argument("--camera_pose", type=str, default="random")
+    parser.add_argument("--camera_dist_min", type=float, default=2.0)
+    parser.add_argument("--camera_dist_max", type=float, default=2.0)
+    parser.add_argument("--fast_mode", action="store_true")
+    args = parser.parse_args(raw_args)
+
+    save_rendering_dataset(
+        input_path=args.input_path,
+        output_path=args.output_path,
+        num_images=args.num_images,
+        backend=args.backend,
+        light_mode=args.light_mode,
+        camera_pose=args.camera_pose,
+        camera_dist_min=args.camera_dist_min,
+        camera_dist_max=args.camera_dist_max,
+        fast_mode=args.fast_mode,
+    )
+
+
+main()
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/evaluate_pfid.py b/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/evaluate_pfid.py
new file mode 100644
index 0000000000000000000000000000000000000000..d392d957c941a092563566af7fd16e91ebacf57a
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/evaluate_pfid.py
@@ -0,0 +1,40 @@
+"""
+Evaluate P-FID between two batches of point clouds.
+
+The point cloud batches should be saved to two npz files, where there
+is an arr_0 key of shape [N x K x 3], where K is the dimensionality of
+each point cloud and N is the number of clouds.
+"""
+
+import argparse
+
+from point_e.evals.feature_extractor import PointNetClassifier, get_torch_devices
+from point_e.evals.fid_is import compute_statistics
+from point_e.evals.npz_stream import NpzStreamer
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cache_dir", type=str, default=None)
+    parser.add_argument("batch_1", type=str)
+    parser.add_argument("batch_2", type=str)
+    args = parser.parse_args()
+
+    print("creating classifier...")
+    clf = PointNetClassifier(devices=get_torch_devices(), cache_dir=args.cache_dir)
+
+    print("computing first batch activations")
+
+    features_1, _ = clf.features_and_preds(NpzStreamer(args.batch_1))
+    stats_1 = compute_statistics(features_1)
+    del features_1
+
+    features_2, _ = clf.features_and_preds(NpzStreamer(args.batch_2))
+    stats_2 = compute_statistics(features_2)
+    del features_2
+
+    print(f"P-FID: {stats_1.frechet_distance(stats_2)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/evaluate_pis.py b/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/evaluate_pis.py
new file mode 100644
index 0000000000000000000000000000000000000000..05395fc0a57b31d0ca314fe46aec088dbaff0772
--- /dev/null
+++ b/evaluations/fidkid-pytorch/3d-metrics/evals/scripts/evaluate_pis.py
@@ -0,0 +1,31 @@
+"""
+Evaluate P-IS of a batch of point clouds.
+
+The point cloud batch should be saved to an npz file, where there is an
+arr_0 key of shape [N x K x 3], where K is the dimensionality of each
+point cloud and N is the number of clouds.
+"""
+
+import argparse
+
+from point_e.evals.feature_extractor import PointNetClassifier, get_torch_devices
+from point_e.evals.fid_is import compute_inception_score
+from point_e.evals.npz_stream import NpzStreamer
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cache_dir", type=str, default=None)
+    parser.add_argument("batch", type=str)
+    args = parser.parse_args()
+
+    print("creating classifier...")
+    clf = PointNetClassifier(devices=get_torch_devices(), cache_dir=args.cache_dir)
+
+    print("computing batch predictions")
+    _, preds = clf.features_and_preds(NpzStreamer(args.batch))
+    print(f"P-IS: {compute_inception_score(preds)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/evaluations/fidkid-pytorch/align_axis.ipynb b/evaluations/fidkid-pytorch/align_axis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..68fba8044bda3b320182a89dff6f06f859cb104b
--- /dev/null
+++ b/evaluations/fidkid-pytorch/align_axis.ipynb
@@ -0,0 +1,66505 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'trimesh'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtrimesh\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mshutil\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'trimesh'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import trimesh\n",
+    "import os\n",
+    "import shutil\n",
+    "import imageio.v3 as imageio\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def rotation_matrix(axis, angle):\n",
+    "    \"\"\"\n",
+    "    Returns a rotation matrix for a given axis and angle in radians.\n",
+    "    :param axis: str, the axis to rotate around ('x', 'y', or 'z')\n",
+    "    :param angle: float, the rotation angle in radians\n",
+    "    :return: 3x3 rotation matrix\n",
+    "    \"\"\"\n",
+    "    if axis == 'x':\n",
+    "        return np.array([[1, 0, 0],\n",
+    "                         [0, np.cos(angle), -np.sin(angle)],\n",
+    "                         [0, np.sin(angle), np.cos(angle)]])\n",
+    "    elif axis == 'y':\n",
+    "        return np.array([[np.cos(angle), 0, np.sin(angle)],\n",
+    "                         [0, 1, 0],\n",
+    "                         [-np.sin(angle), 0, np.cos(angle)]])\n",
+    "    elif axis == 'z':\n",
+    "        return np.array([[np.cos(angle), -np.sin(angle), 0],\n",
+    "                         [np.sin(angle), np.cos(angle), 0],\n",
+    "                         [0, 0, 1]])\n",
+    "    else:\n",
+    "        raise ValueError(\"Axis must be 'x', 'y', or 'z'.\")\n",
+    "\n",
+    "def rotate_point_cloud(point_cloud, rotations):\n",
+    "    \"\"\"\n",
+    "    Rotates a point cloud along specified axes by the given angles.\n",
+    "    :param point_cloud: Nx3 numpy array of points\n",
+    "    :param rotations: list of tuples [(axis, angle_in_degrees), ...]\n",
+    "                      Example: [('x', 90), ('y', 45)] for composite rotations\n",
+    "    :return: Rotated point cloud as Nx3 numpy array\n",
+    "    \"\"\"\n",
+    "    rotated_cloud = point_cloud.copy()\n",
+    "    for axis, angle in rotations:\n",
+    "        angle_rad = np.radians(angle)  # Convert degrees to radians\n",
+    "        R = rotation_matrix(axis, angle_rad)\n",
+    "        rotated_cloud = np.dot(rotated_cloud, R.T)  # Apply rotation matrix\n",
+    "    \n",
+    "    return rotated_cloud\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 7.07106781e-01  0.00000000e+00 -7.07106781e-01]\n",
+      " [ 7.07106781e-01  6.12323400e-17  7.07106781e-01]\n",
+      " [ 4.32978028e-17 -1.00000000e+00  4.32978028e-17]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example point cloud (Nx3 matrix)\n",
+    "point_cloud = np.array([[1, 0, 0],\n",
+    "                        [0, 1, 0],\n",
+    "                        [0, 0, 1]])\n",
+    "\n",
+    "# Composite rotation: 90 degrees around x, then 45 degrees around y\n",
+    "rotations = [('x', 90), ('y', 45)]\n",
+    "\n",
+    "# Rotate the point cloud\n",
+    "rotated_cloud = rotate_point_cloud(point_cloud, rotations)\n",
+    "print(rotated_cloud)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rotate different to align in gso canonical space\n",
+    "# import open3d as o3d\n",
+    "# import numpy as np\n",
+    "import plotly.graph_objects as go\n",
+    "import matplotlib.pyplot as plt\n",
+    "import point_cloud_utils as pcu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vis_colored_pcd(points, colors, size=1.0):\n",
+    "\n",
+    "    fig = go.Figure(\n",
+    "      data=[\n",
+    "        go.Scatter3d(\n",
+    "          x=points[:,0], y=points[:,1], z=points[:,2],\n",
+    "          mode='markers',\n",
+    "          marker=dict(size=size, color=colors)\n",
+    "    )\n",
+    "\n",
+    "      ],\n",
+    "      layout=dict(    \n",
+    "        paper_bgcolor='rgba(0,0,0,0)', \n",
+    "        width=512, \n",
+    "        height=512,\n",
+    "        scene=dict(\n",
+    "          xaxis=dict(visible=False),\n",
+    "          yaxis=dict(visible=False),\n",
+    "          zaxis=dict(visible=False)\n",
+    "    )\n",
+    "    )\n",
+    "    )\n",
+    "    config = {\n",
+    "  'toImageButtonOptions': {\n",
+    "    'format': 'png', # one of png, svg, jpeg, webp\n",
+    "    'filename': 'custom_image',\n",
+    "    'height': 512,\n",
+    "    'width': 512,\n",
+    "    'scale':4 # Multiply title/legend/axis/canvas sizes by this factor\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "\n",
+    "    fig.show(config=config)\n",
+    "#     fig.write_image(\"fig1.png\")\n",
+    "\n",
+    "# vis_colored_pcd(points, colors, size=1.3)\n",
+    "# vis_colored_pcd(split_points, splint_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "# transformation dictionary\n",
+    "transformation_dict = {\n",
+    "    'gso': partial(rotate_point_cloud, rotations=[('x', 0)]), # no transformation\n",
+    "    'LGM': partial(rotate_point_cloud, rotations=[('x', 90)]),\n",
+    "    'CRM': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),\n",
+    "    'Lara': partial(rotate_point_cloud, rotations=[('x', -110), ('z', 33)]),\n",
+    "    'ln3diff': partial(rotate_point_cloud, rotations=[('x', 90)]),\n",
+    "    'One-2-3-45': partial(rotate_point_cloud, rotations=[('x', 90), ('z', 180)]),\n",
+    "    'splatter-img': partial(rotate_point_cloud, rotations=[('x', -60)]),\n",
+    "    # \n",
+    "    'OpenLRM': partial(rotate_point_cloud, rotations=[('x', 0)]),\n",
+    "    'shape-e': partial(rotate_point_cloud, rotations=[('x', 0)]),\n",
+    "    'GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD': partial(rotate_point_cloud, rotations=[('x', 0)]),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# instance_name = '3D_Dollhouse_Happy_Brother_pcd_4096'\n",
+    "# instance_name = 'FRUIT_VEGGIE_DOMINO_GRADIENT_pcd_4096'\n",
+    "# instance_name = 'SpiderMan_Titan_Hero_12Inch_Action_Figure_5Hnn4mtkFsP_pcd_4096'\n",
+    "instance_name = 'Android_Figure_Panda_pcd_4096'\n",
+    "\n",
+    "method_name='LGM'\n",
+    "save_root=f\"/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics-fps\"\n",
+    "\n",
+    "def vis_pcd(method_name, instance_name):\n",
+    "\n",
+    "    pcd_path = os.path.join(save_root, method_name, f'{instance_name}.ply')\n",
+    "    pcd = trimesh.load(pcd_path).vertices\n",
+    "\n",
+    "    pcd = transformation_dict[method_name](pcd)\n",
+    "\n",
+    "    color = np.zeros_like(pcd) # just black\n",
+    "    vis_colored_pcd(pcd, color)\n",
+    "\n",
+    "    # return pcd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly",
+        "toImageButtonOptions": {
+         "filename": "custom_image",
+         "format": "png",
+         "height": 512,
+         "scale": 4,
+         "width": 512
+        }
+       },
+       "data": [
+        {
+         "marker": {
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+       "var gd = document.getElementById('b2ff53a1-7a35-4b7c-aaa1-6b67ae8c1b30');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# vis_pcd('LGM', instance_name)\n",
+    "# vis_pcd('Lara', instance_name) # wrong\n",
+    "# vis_pcd('ln3diff', instance_name) # \n",
+    "# vis_pcd('CRM', instance_name) # \n",
+    "# vis_pcd('One-2-3-45', instance_name) # \n",
+    "# vis_pcd('splatter-img', instance_name) # \n",
+    "# vis_pcd('shape-e', instance_name) # \n",
+    "# vis_pcd('CRM', instance_name) # \n",
+    "# vis_pcd('OpenLRM', instance_name) # \n",
+    "\n",
+    "vis_pcd('GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD', instance_name) # "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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                       {\"toImageButtonOptions\": {\"format\": \"png\", \"filename\": \"custom_image\", \"height\": 512, \"width\": 512, \"scale\": 4}, \"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('56a56206-e49c-4f3a-9e8a-ae2eeeb815a2');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis_pcd('gso', instance_name)\n",
+    "# vis_pcd('shape-e', instance_name) # \n",
+    "# vis_pcd('LGM', instance_name) # \n",
+    "# vis_pcd('splatter-img', instance_name) # "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# organize a dataset for fid eval\n",
+    "\n",
+    "objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'\n",
+    "dataset_json = os.path.join(objv_dataset, 'dataset.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "with open(dataset_json, 'r') as f:\n",
+    "    dataset_json = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['Transportations_tar', 'Furnitures', 'Food', 'BuildingsOutdoor', 'Electronics', 'daily-used', 'Human-Shape', 'Plants', 'Animals'])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_json.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37461"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dataset_json['Animals'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instances_for_use = dataset_json['Animals'][::3][:6250]\n",
+    "instances_for_use = dataset_json['Animals'][::3][:6250][1100:2200]\n",
+    "# instances_for_use = dataset_json['Animals'][:6250]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Animals/53/275475/1', 'Animals/53/278344/1', 'Animals/53/279428/1']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# len(instances_for_use)\n",
+    "instances_for_use[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1100/1100 [01:32<00:00, 11.87it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# save to local dir, with the same format as obj_folder, 8 images \n",
+    "# output_dir = '/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval'\n",
+    "output_dir = '/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-new'\n",
+    "# output_dir = '/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K'\n",
+    "# output_dir = '/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K-new'\n",
+    "\n",
+    "# for object in instances_for_use:\n",
+    "for idx, object in enumerate(tqdm(instances_for_use[:])):\n",
+    "    img_path = os.path.join(objv_dataset, object, 'raw_img.jpg')\n",
+    "    img = imageio.imread(img_path)\n",
+    "\n",
+    "    if idx % 3 == 0:\n",
+    "        indices_to_save = [0,1]\n",
+    "    else:\n",
+    "        indices_to_save = [1,0]\n",
+    "    # indices_to_save = [0,1,2,3,4,9,10,11,12][:2]\n",
+    "    # else:\n",
+    "        # indices_to_save = [1,2,3,4,9,10,11,12]\n",
+    "\n",
+    "    instance_output_dir = os.path.join(output_dir, object)\n",
+    "    os.makedirs(instance_output_dir, exist_ok=True)\n",
+    "\n",
+    "    for idx, w_position in enumerate(indices_to_save):\n",
+    "        patch = img[:, w_position*512:(w_position+1)*512, :]\n",
+    "        imageio.imwrite(os.path.join(instance_output_dir, f'{idx}.jpg'), patch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert gt pcd to the right format\n",
+    "\n",
+    "\n",
+    "\n",
+    "objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'\n",
+    "dataset_json = os.path.join(objv_dataset, 'dataset.json')\n",
+    "with open(dataset_json, 'r') as f:\n",
+    "    dataset_json = json.load(f)\n",
+    "\n",
+    "all_objs = dataset_json['Animals'][::3][1100:2200]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1100/1100 [00:33<00:00, 32.89it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# from tqdm import tqdm        \n",
+    "# import shutil\n",
+    "\n",
+    "# ! copy gt\n",
+    "\n",
+    "gt_path = \"/mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd\"\n",
+    "output_path = \"/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/objv-gt\"\n",
+    "\n",
+    "for obj_folder in tqdm(all_objs):\n",
+    "    save_name = '-'.join(obj_folder.split('/'))\n",
+    "    obj_folder = '/'.join(obj_folder.split('/')[:-1])\n",
+    "\n",
+    "    shutil.copy(os.path.join(gt_path, obj_folder, 'fps-4096.ply'), os.path.join(output_path, f'{save_name}_pcd_4096.ply'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1100/1100 [00:14<00:00, 77.74it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ! copy ours pred\n",
+    "\n",
+    "pred_path = \"/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/GA/stage-2/dino_img/ditl-fromditlPCD\"\n",
+    "output_path = \"/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/GA\"\n",
+    "\n",
+    "for obj_folder in tqdm(all_objs[:]):\n",
+    "    save_name = '-'.join(obj_folder.split('/'))\n",
+    "    obj_folder = '/'.join(obj_folder.split('/')[1:-1])\n",
+    "    try:\n",
+    "        shutil.copy(os.path.join(pred_path, obj_folder, '0', 'sample-0-gaussian-4096.ply'), os.path.join(output_path, f'{save_name}_pcd_4096.ply'))\n",
+    "    except:\n",
+    "        continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copy output images into a single folder for clean-FID calculation\n",
+    "\n",
+    "\n",
+    "objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'\n",
+    "dataset_json = os.path.join(objv_dataset, 'dataset.json')\n",
+    "with open(dataset_json, 'r') as f:\n",
+    "    dataset_json = json.load(f)\n",
+    "\n",
+    "# all_objs = dataset_json['Animals'][::3][:6250]\n",
+    "all_objs = dataset_json['Animals'][::3][1100:2200]\n",
+    "all_objs = all_objs[:600]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 600/600 [00:00<00:00, 17405.80it/s]\n",
+      "100%|██████████| 14400/14400 [03:20<00:00, 71.66it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import shutil\n",
+    "\n",
+    "# for method_name in ln3diff-fixpose_192 CRM \n",
+    "# for method_name in \n",
+    "# OpenLRM/Animals One-2-3-45/Animals shape-e/Animals splatter-img/Animals \n",
+    "\n",
+    "output_path='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv'\n",
+    "\n",
+    "# for method_name in ['LGM_fixpose', 'GA-fixpose']:\n",
+    "# for method_name in []:\n",
+    "# for method_name in ['LGM_fixpose/Animals', 'GA/stage-2/dino_img/ditl-fromditlPCD/']:\n",
+    "for method_name in ['GA/stage-2/dino_img/ditl-fromditlPCD/']:\n",
+    "# for method_name in ['objv-gt']:\n",
+    "    path = os.path.join(output_path, method_name)\n",
+    "    files = []\n",
+    "\n",
+    "    for obj_folder in tqdm(all_objs):\n",
+    "        obj_folder = '/'.join(obj_folder.split('/')[1:])\n",
+    "        for idx in range(24):\n",
+    "            # files.append(os.path.join(path, obj_folder, f'{idx}.jpg'))\n",
+    "            if 'Lara' in path:\n",
+    "                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', f'{idx}.jpg'))\n",
+    "            # elif 'LGM' in path:\n",
+    "            #     files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))\n",
+    "            elif 'GA' in path:\n",
+    "                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'sample-0-{idx}.jpg'))\n",
+    "            elif 'LRM' in path:\n",
+    "                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))\n",
+    "            else:\n",
+    "                files.append(os.path.join(path, obj_folder, '0', f'{idx}.jpg'))\n",
+    "\n",
+    "\n",
+    "\n",
+    "    output_path = '/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/lint-dir'\n",
+    "\n",
+    "    method_output_path = os.path.join(output_path, method_name)\n",
+    "    os.makedirs(method_output_path, exist_ok=True)\n",
+    "\n",
+    "    for idx, file_path in enumerate(tqdm(files[:])):\n",
+    "        shutil.copy(file_path, os.path.join(method_output_path, f'{idx}.jpg'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:00<00:00, 60.38it/s]\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-new/LGM_fixpose/Animals/9.jpg'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.path.join(method_output_path, f'{idx}.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 600/600 [00:00<00:00, 20171.71it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gso_rendering=\"/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K\"\n",
+    "\n",
+    "\n",
+    "# for method_name in ['GA/stage-2/dino_img/ditl-fromditlPCD/']:\n",
+    "# # for method_name in ['objv-gt']:\n",
+    "#     path = os.path.join(output_path, method_name)\n",
+    "files = []\n",
+    "\n",
+    "for obj_folder in tqdm(all_objs):\n",
+    "    obj_folder = obj_folder[:-2] # to load 3 chunks\n",
+    "    for batch in range(1,4):\n",
+    "        for idx in range(8):\n",
+    "            files.append(os.path.join(gso_rendering, obj_folder, str(batch), f'{idx}.jpg'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 14400/14400 [04:21<00:00, 55.06it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "output_path = '/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/lint-dir'\n",
+    "gt_output_path = os.path.join(output_path, 'objv-gt')\n",
+    "\n",
+    "os.makedirs(gt_output_path, exist_ok=True)\n",
+    "\n",
+    "for idx, file_path in enumerate(tqdm(files[:])):\n",
+    "    shutil.copy(file_path, os.path.join(gt_output_path, f'{idx}.jpg'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_vid_paths = \"/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/2-30_CONSTRUCTION_SET-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/60-BAGEL_WITH_CHEESE-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/62-BALANCING_CACTUS-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/76-Baby_Elements_Stacking_Cups-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/98-Breyer_Horse_Of_The_Year_2015-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/112-COAST_GUARD_BOAT-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/113-CONE_SORTING-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/115-CREATIVE_BLOCKS_35_MM-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/155-Cole_Hardware_Mini_Honey_Dipper-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/275-FAIRY_TALE_BLOCKS-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/277-FIRE_ENGINE-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/280-FOOD_BEVERAGE_SET-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/303-GEOMETRIC_PEG_BOARD-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/324-Great_Dinos_Triceratops_Toy-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/365-JUICER_SET-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/789-STACKING_BEAR-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/791-STACKING_RING-0-sample-0-gs.mp4 /mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/GA-fixpose/803-Schleich_African_Black_Rhino-0-sample-0-gs.mp4 \""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['BAGEL_WITH_CHEESE',\n",
+       " 'BALANCING_CACTUS',\n",
+       " 'Baby_Elements_Stacking_Cups',\n",
+       " 'Breyer_Horse_Of_The_Year_2015',\n",
+       " 'COAST_GUARD_BOAT',\n",
+       " 'CONE_SORTING',\n",
+       " 'CREATIVE_BLOCKS_35_MM',\n",
+       " 'Cole_Hardware_Mini_Honey_Dipper',\n",
+       " 'FAIRY_TALE_BLOCKS',\n",
+       " 'FIRE_ENGINE',\n",
+       " 'FOOD_BEVERAGE_SET',\n",
+       " 'GEOMETRIC_PEG_BOARD',\n",
+       " 'Great_Dinos_Triceratops_Toy',\n",
+       " 'JUICER_SET',\n",
+       " 'STACKING_BEAR',\n",
+       " 'STACKING_RING',\n",
+       " 'Schleich_African_Black_Rhino']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "instances = [\n",
+    "    path.split('/')[-1].split('-')[1]\n",
+    "    for path in all_vid_paths.split()\n",
+    "]\n",
+    "instances[1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/evaluations/fidkid-pytorch/clean_fid_gen3d_baseline.sh b/evaluations/fidkid-pytorch/clean_fid_gen3d_baseline.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3a3992738602ca18410b14d033aebb897dbf8852
--- /dev/null
+++ b/evaluations/fidkid-pytorch/clean_fid_gen3d_baseline.sh
@@ -0,0 +1,60 @@
+set -x 
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="gso-rendering"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir
+
+
+# ! gso stuffs
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K"
+gt_rendering="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/lint-dir/objv-gt"
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/lint-dir
+
+# method_name=LGM
+# for method_name in CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD
+# for method_name in CRM/Animals ln3diff Lara
+# Lara 
+
+# for method_name in Lara ln3diff/Animals 
+# for method_name in Lara GA/stage-2/dino_img/ditl-fromditlPCD ln3diff/Animals 
+# for method_name in CRM/Animals
+# for method_name in ln3diff-lite/Animals 
+# for method_name in GA/stage-2/dino_img/ditl-fromditxlPCD 
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD LGM_fixpose/Animals 
+# for method_name in GA/stage-2/dino_img/ditl-fromditxlPCD-47w
+
+
+# for method_name in GA/stage-2/dino_img/ditl-withtop LGM_fixpose_withtop/Animals ln3diff-lite-withtop/Animals
+
+
+# ! GSO
+
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+# for method_name in ln3diff-fixpose_192 CRM LGM_fixpose
+# for method_name in GA-fixpose
+# for method_name in LGM_fixpose_r=1.8/Animals
+for method_name in GA/stage-2/dino_img/ditl-fromditlPCD LGM_fixpose/Animals
+
+# OpenLRM/Animals One-2-3-45/Animals shape-e/Animals splatter-img/Animals 
+
+do
+
+python cleanfid_score_gso.py $gt_rendering ${output_path}/${method_name} \
+    --dataset ${method_name} \
+    --num-workers 4 \
+    --reso 512 \
+    # --save-stats \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/cleanfid_score_gso.py b/evaluations/fidkid-pytorch/cleanfid_score_gso.py
new file mode 100644
index 0000000000000000000000000000000000000000..75c5baf29aae63a459af565c26f91db3efe4bbb0
--- /dev/null
+++ b/evaluations/fidkid-pytorch/cleanfid_score_gso.py
@@ -0,0 +1,518 @@
+"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
+
+The FID metric calculates the distance between two distributions of images.
+Typically, we have summary statistics (mean & covariance matrix) of one
+of these distributions, while the 2nd distribution is given by a GAN.
+
+When run as a stand-alone program, it compares the distribution of
+images that are stored as PNG/JPEG at a specified location with a
+distribution given by summary statistics (in pickle format).
+
+The FID is calculated by assuming that X_1 and X_2 are the activations of
+the pool_3 layer of the inception net for generated samples and real world
+samples respectively.
+
+See --help to see further details.
+
+Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
+of Tensorflow
+
+Copyright 2018 Institute of Bioinformatics, JKU Linz
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import ipdb
+import os
+from pathlib import Path
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+import pyiqa
+
+from pdb import set_trace as st
+
+import json
+import numpy as np
+import torch
+import torchvision.transforms as TF
+from PIL import Image
+from scipy import linalg
+from torch.nn.functional import adaptive_avg_pool2d
+import cv2
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If tqdm is not available, provide a mock version of it
+    def tqdm(x):
+        return x
+
+from pytorch_fid.inception import InceptionV3
+
+parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+parser.add_argument('--batch-size', type=int, default=100,
+                    help='Batch size to use')
+parser.add_argument('--reso', type=int, default=128,
+                    help='Batch size to use')
+parser.add_argument('--num-workers', type=int, default=8,
+                    help=('Number of processes to use for data loading. '
+                          'Defaults to `min(8, num_cpus)`'))
+parser.add_argument('--device', type=str, default=None,
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+parser.add_argument('--dataset', type=str, default='omni',
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+parser.add_argument('--dims', type=int, default=2048,
+                    choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
+                    help=('Dimensionality of Inception features to use. '
+                          'By default, uses pool3 features'))
+parser.add_argument('--save-stats', action='store_true',
+                    help=('Generate an npz archive from a directory of samples. '
+                          'The first path is used as input and the second as output.'))
+parser.add_argument('path', type=str, nargs=2,
+                    help=('Paths to the generated images or '
+                          'to .npz statistic files'))
+
+IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm',
+                    'tif', 'tiff', 'webp'}
+
+
+class ImagePathDataset(torch.utils.data.Dataset):
+    def __init__(self, files, reso,transforms=None):
+        self.files = files
+        self.transforms = transforms
+        self.reso=reso
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, i):
+        path = self.files[i]
+        #ipdb.set_trace()
+        try:
+            img=cv2.imread(path)
+            #if img.mean(-1)>254.9:
+            #img[np.where(img.mean(-1)>254.9)]=0
+            img=cv2.resize(img,(self.reso,self.reso),interpolation=cv2.INTER_CUBIC)
+            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+        except:
+            img=cv2.imread(self.files[0])
+            #if img.mean(-1)>254.9:
+            #img[np.where(img.mean(-1)>254.9)]=0
+            img=cv2.resize(img,(self.reso,self.reso),interpolation=cv2.INTER_CUBIC)
+            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+            print(path)
+        
+
+        #img = Image.open(path).convert('RGB')
+        if self.transforms is not None:
+            img = self.transforms(img)
+        #ipdb.set_trace()
+        
+        return img
+
+
+def get_activations(files, model, batch_size=50, dims=2048, device='cpu',
+                    num_workers=16,reso=128):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : Batch size of images for the model to process at once.
+                     Make sure that the number of samples is a multiple of
+                     the batch size, otherwise some samples are ignored. This
+                     behavior is retained to match the original FID score
+                     implementation.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- A numpy array of dimension (num images, dims) that contains the
+       activations of the given tensor when feeding inception with the
+       query tensor.
+    """
+    model.eval()
+
+    if batch_size > len(files):
+        print(('Warning: batch size is bigger than the data size. '
+               'Setting batch size to data size'))
+        batch_size = len(files)
+
+    dataset = ImagePathDataset(files, reso,transforms=TF.ToTensor())
+    dataloader = torch.utils.data.DataLoader(dataset,
+                                             batch_size=batch_size,
+                                             shuffle=False,
+                                             drop_last=False,
+                                             num_workers=num_workers)
+
+    pred_arr = np.empty((len(files), dims))
+
+    start_idx = 0
+
+    for batch in tqdm(dataloader):
+        batch = batch.to(device)
+        #ipdb.set_trace()
+
+        with torch.no_grad():
+            pred = model(batch)[0]
+
+        # If model output is not scalar, apply global spatial average pooling.
+        # This happens if you choose a dimensionality not equal 2048.
+        if pred.size(2) != 1 or pred.size(3) != 1:
+            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
+
+        #ipdb.set_trace()
+
+        pred = pred.squeeze(3).squeeze(2).cpu().numpy()
+
+        pred_arr[start_idx:start_idx + pred.shape[0]] = pred
+
+        start_idx = start_idx + pred.shape[0]
+
+    return pred_arr
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+
+    Stable version by Dougal J. Sutherland.
+
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representative data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representative data set.
+
+    Returns:
+    --   : The Frechet Distance.
+    """
+    #ipdb.set_trace()
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1)
+            + np.trace(sigma2) - 2 * tr_covmean)
+
+
+def calculate_activation_statistics(files, model, batch_size=50, dims=2048,
+                                    device='cpu', num_workers=1,reso=128):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : The images numpy array is split into batches with
+                     batch size batch_size. A reasonable batch size
+                     depends on the hardware.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    act = get_activations(files, model, batch_size, dims, device, num_workers,reso=reso)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def compute_statistics_of_path(path, model, batch_size, dims, device,
+                               num_workers=1,reso=512,dataset='gso'):
+    basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-withtop/gso_gt"
+
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid-withtop/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-/metrics/fid-withtop/gso_gt"
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600][:]
+    # all_objs = all_objs[100:600]
+    # all_objs = all_objs[:500]
+
+
+    # if 'shapenet' in dataset:
+    # if 'shapenet' in dataset:
+
+    try:
+        try:
+            
+            m=np.load(os.path.join(basepath,path.split('/')[-1]+str(reso)+'mean.npy'))
+            s=np.load(os.path.join(basepath,path.split('/')[-1]+str(reso)+'std.npy'))
+            print('loading_dataset',dataset)
+        except:
+            files=[]
+            # ! load instances for I23D inference
+            # for obj_folder in tqdm(sorted(os.listdir(path))):
+            #     for idx in range(0,25):
+            #         img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+            #         files.append(img_name)
+                    
+            # ! free3d rendering
+            # for obj_folder in tqdm(sorted(os.listdir(path))):
+            #     for idx in range(0,25):
+            #         # img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+            #         img_name = os.path.join(path, obj_folder, 'render_mvs_25', 'model', f'{idx:03}.png')
+            #         files.append(img_name)
+
+            # ! objv loading
+            for obj_folder in tqdm(all_objs):
+                obj_folder = obj_folder[:-2] # to load 3 chunks
+                for batch in range(1,4):
+                    for idx in range(8):
+                        files.append(os.path.join(path, obj_folder, str(batch), f'{idx}.jpg'))
+
+            # for name in os.listdir(path):
+            #     #ipdb.set_trace()
+            #     # if name not in false1: #and name not in false2 and name not in false3:
+            #     if name in false1: #and name not in false2 and name not in false3:
+            #         img=os.path.join(path,name,'rgb')
+            #         #ipdb.set_trace()
+            #         files = files+sorted([os.path.join(img, idd) for idd in os.listdir(img) if idd.endswith('.png')])
+
+                if len(files) > 50000:
+                    files = files[:50000]
+                    break
+
+            #files=files[:5]
+            m, s = calculate_activation_statistics(files, model, batch_size,
+                                                    dims, device, num_workers,reso=reso)
+            path = Path(path)
+            # ipdb.set_trace()
+            np.save(os.path.join(basepath,path.name+str(reso)+'mean'), m)
+            np.save(os.path.join(basepath,path.name+str(reso)+'std'), s)
+    except Exception as e:
+        print(f'{dataset} failed, ', e)
+
+
+    return m, s
+
+
+def compute_statistics_of_path_new(path, model, batch_size, dims, device,
+                               num_workers=1,reso=128,dataset='omni'):
+    # basepath='/mnt/lustre/yslan/logs/nips23/LSGM/cldm/cmetric/shapenet-outs/fid'+str(reso)+'test'+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/metrics/fid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-subset/'+str(reso)+dataset
+
+    basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-withtop/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid/'+str(reso)+dataset
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+    sample_name=path.split('/')[-1]
+
+    try:
+        try:
+            # ipdb.set_trace()
+            m=np.load(os.path.join(basepath,sample_name+str(reso)+'mean.npy'))
+            s=np.load(os.path.join(basepath,sample_name+str(reso)+'std.npy'))
+            print('loading_sample')
+        except:
+            files=[]
+
+            # for name in os.listdir(path):
+            #     img=os.path.join(path,name)
+            #     files.append(img) # ! directly append
+
+            # for loading gso-like folder
+            # st()
+            # for obj_folder in sorted(os.listdir(path)):
+
+            #     if obj_folder == 'runs':
+            #         continue
+
+            #     if not os.path.isdir(os.path.join(path, obj_folder)):
+            #         continue
+
+            #     for idx in [0]:
+            #         for i in range(24):
+            #             if 'GA' in path:
+            #                 img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+            #             else:
+            #                 img=os.path.join(path,obj_folder, str(idx),f'{i}.jpg')
+            #                 # ipdb.set_trace()
+            #             files.append(img) 
+
+            for obj_folder in tqdm(all_objs):
+                obj_folder = '/'.join(obj_folder.split('/')[1:])
+                for idx in range(24):
+                    # files.append(os.path.join(path, obj_folder, f'{idx}.jpg'))
+                    if 'Lara' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', f'{idx}.jpg'))
+                    elif 'GA' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'sample-0-{idx}.jpg'))
+                    elif 'LRM' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))
+                    else:
+                        files.append(os.path.join(path, obj_folder, '0', f'{idx}.jpg'))
+
+
+            files=files[:50000]
+            m, s = calculate_activation_statistics(files, model, batch_size,
+                                                    dims, device, num_workers,reso=reso)
+            path = Path(path)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'mean'), m)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'std'), s)
+    except Exception as e:
+        print('error sample image', e)
+
+    #ipdb.set_trace()
+
+    return m, s
+
+
+def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1,reso=128,dataset='omni'):
+    """Calculates the FID of two paths"""
+    # for p in paths:
+    #     if not os.path.exists(p):
+    #         raise RuntimeError('Invalid path: %s' % p)
+
+    # block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    # model = InceptionV3([block_idx]).to(device)
+
+    musiq_metric = pyiqa.create_metric('musiq')
+    all_musiq = []
+
+    for file in tqdm(os.listdir(str(paths[1]))[:]):
+        musiq_value = musiq_metric(os.path.join(paths[1], file))
+        all_musiq.append(musiq_value)
+
+    musiq_value = sum(all_musiq) / len(all_musiq)
+
+    # fid_metric = pyiqa.create_metric('fid')
+    # fid_value = fid_metric(paths[0], paths[1])
+    
+
+    # m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, # ! GT data
+    #                                     dims, device, num_workers,reso=reso,dataset=dataset)
+    # # ipdb.set_trace()
+    # m2, s2 = compute_statistics_of_path_new(paths[1], model, batch_size, # ! generated data
+    #                                     dims, device, num_workers,reso=reso,dataset=dataset)
+    # fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+
+    # return fid_value
+    return musiq_value
+
+
+def save_fid_stats(paths, batch_size, device, dims, num_workers=1):
+    """Calculates the FID of two paths"""
+    # if not os.path.exists(paths[0]):
+    #     raise RuntimeError('Invalid path: %s' % paths[0])
+
+    # if os.path.exists(paths[1]):
+    #     raise RuntimeError('Existing output file: %s' % paths[1])
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    print(f"Saving statistics for {paths[0]}")
+
+    m1, s1 = compute_statistics_of_path(paths[0], model, batch_size,
+                                        dims, device, num_workers)
+
+    np.savez_compressed(paths[1], mu=m1, sigma=s1)
+
+
+def main():
+    args = parser.parse_args()
+
+    if args.device is None:
+        device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
+    else:
+        device = torch.device(args.device)
+
+    if args.num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = args.num_workers
+
+    if args.save_stats:
+        save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers)
+        return
+    #ipdb.set_trace()
+    fid_value = calculate_fid_given_paths(args.path,
+                                          args.batch_size,
+                                          device,
+                                          args.dims,
+                                          num_workers,args.reso,args.dataset)
+    print(f'{args.dataset} FID: ', fid_value)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/evaluations/fidkid-pytorch/fid_gen3d_baseline.sh b/evaluations/fidkid-pytorch/fid_gen3d_baseline.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3939d76ade987c53e40f513e04b34ad3846eb4eb
--- /dev/null
+++ b/evaluations/fidkid-pytorch/fid_gen3d_baseline.sh
@@ -0,0 +1,61 @@
+set -x 
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="gso-rendering"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir
+
+
+# ! gso stuffs
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+gso_rendering="/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K"
+
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv
+
+# method_name=LGM
+# for method_name in CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD
+# for method_name in CRM/Animals ln3diff Lara
+# Lara 
+
+# for method_name in Lara ln3diff/Animals 
+# for method_name in Lara GA/stage-2/dino_img/ditl-fromditlPCD ln3diff/Animals 
+# for method_name in CRM/Animals
+# for method_name in ln3diff-lite/Animals 
+# for method_name in GA/stage-2/dino_img/ditl-fromditxlPCD 
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD LGM_fixpose/Animals 
+# for method_name in GA/stage-2/dino_img/ditl-fromditxlPCD-47w
+
+
+# for method_name in GA/stage-2/dino_img/ditl-withtop LGM_fixpose_withtop/Animals ln3diff-lite-withtop/Animals
+
+
+# ! GSO
+
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+# for method_name in ln3diff-fixpose_192 CRM LGM_fixpose
+# for method_name in GA-fixpose
+# for method_name in LGM_fixpose_r=1.8/Animals
+# for method_name in GA/stage-2/dino_img/ditl-witholdmodel
+for method_name in scale3d/eval/eval_nerf/Animals scale3d/eval/eval_mesh/Animals
+
+# OpenLRM/Animals One-2-3-45/Animals shape-e/Animals splatter-img/Animals 
+
+do
+
+python fid_score_gso.py $gso_rendering ${output_path}/${method_name} \
+    --dataset ${method_name} \
+    --num-workers 4 \
+    --reso 512 \
+    # --save-stats \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/fid_score_gso.py b/evaluations/fidkid-pytorch/fid_score_gso.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0a473aec63ff4368553034ffcbbb619c70f1db3
--- /dev/null
+++ b/evaluations/fidkid-pytorch/fid_score_gso.py
@@ -0,0 +1,503 @@
+"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
+
+The FID metric calculates the distance between two distributions of images.
+Typically, we have summary statistics (mean & covariance matrix) of one
+of these distributions, while the 2nd distribution is given by a GAN.
+
+When run as a stand-alone program, it compares the distribution of
+images that are stored as PNG/JPEG at a specified location with a
+distribution given by summary statistics (in pickle format).
+
+The FID is calculated by assuming that X_1 and X_2 are the activations of
+the pool_3 layer of the inception net for generated samples and real world
+samples respectively.
+
+See --help to see further details.
+
+Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
+of Tensorflow
+
+Copyright 2018 Institute of Bioinformatics, JKU Linz
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import ipdb
+import os
+from pathlib import Path
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+
+import json
+import numpy as np
+import torch
+import torchvision.transforms as TF
+from PIL import Image
+from scipy import linalg
+from torch.nn.functional import adaptive_avg_pool2d
+import cv2
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If tqdm is not available, provide a mock version of it
+    def tqdm(x):
+        return x
+
+from pytorch_fid.inception import InceptionV3
+
+parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+parser.add_argument('--batch-size', type=int, default=100,
+                    help='Batch size to use')
+parser.add_argument('--reso', type=int, default=128,
+                    help='Batch size to use')
+parser.add_argument('--num-workers', type=int, default=8,
+                    help=('Number of processes to use for data loading. '
+                          'Defaults to `min(8, num_cpus)`'))
+parser.add_argument('--device', type=str, default=None,
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+parser.add_argument('--dataset', type=str, default='omni',
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+parser.add_argument('--dims', type=int, default=2048,
+                    choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
+                    help=('Dimensionality of Inception features to use. '
+                          'By default, uses pool3 features'))
+parser.add_argument('--save-stats', action='store_true',
+                    help=('Generate an npz archive from a directory of samples. '
+                          'The first path is used as input and the second as output.'))
+parser.add_argument('path', type=str, nargs=2,
+                    help=('Paths to the generated images or '
+                          'to .npz statistic files'))
+
+IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm',
+                    'tif', 'tiff', 'webp'}
+
+
+class ImagePathDataset(torch.utils.data.Dataset):
+    def __init__(self, files, reso,transforms=None):
+        self.files = files
+        self.transforms = transforms
+        self.reso=reso
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, i):
+        path = self.files[i]
+        #ipdb.set_trace()
+        try:
+            img=cv2.imread(path)
+            #if img.mean(-1)>254.9:
+            #img[np.where(img.mean(-1)>254.9)]=0
+            img=cv2.resize(img,(self.reso,self.reso),interpolation=cv2.INTER_CUBIC)
+            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+        except:
+            img=cv2.imread(self.files[0])
+            #if img.mean(-1)>254.9:
+            #img[np.where(img.mean(-1)>254.9)]=0
+            img=cv2.resize(img,(self.reso,self.reso),interpolation=cv2.INTER_CUBIC)
+            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+            print(path)
+        
+
+        #img = Image.open(path).convert('RGB')
+        if self.transforms is not None:
+            img = self.transforms(img)
+        #ipdb.set_trace()
+        
+        return img
+
+
+def get_activations(files, model, batch_size=50, dims=2048, device='cpu',
+                    num_workers=16,reso=128):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : Batch size of images for the model to process at once.
+                     Make sure that the number of samples is a multiple of
+                     the batch size, otherwise some samples are ignored. This
+                     behavior is retained to match the original FID score
+                     implementation.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- A numpy array of dimension (num images, dims) that contains the
+       activations of the given tensor when feeding inception with the
+       query tensor.
+    """
+    model.eval()
+
+    if batch_size > len(files):
+        print(('Warning: batch size is bigger than the data size. '
+               'Setting batch size to data size'))
+        batch_size = len(files)
+
+    dataset = ImagePathDataset(files, reso,transforms=TF.ToTensor())
+    dataloader = torch.utils.data.DataLoader(dataset,
+                                             batch_size=batch_size,
+                                             shuffle=False,
+                                             drop_last=False,
+                                             num_workers=num_workers)
+
+    pred_arr = np.empty((len(files), dims))
+
+    start_idx = 0
+
+    for batch in tqdm(dataloader):
+        batch = batch.to(device)
+        #ipdb.set_trace()
+
+        with torch.no_grad():
+            pred = model(batch)[0]
+
+        # If model output is not scalar, apply global spatial average pooling.
+        # This happens if you choose a dimensionality not equal 2048.
+        if pred.size(2) != 1 or pred.size(3) != 1:
+            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
+
+        #ipdb.set_trace()
+
+        pred = pred.squeeze(3).squeeze(2).cpu().numpy()
+
+        pred_arr[start_idx:start_idx + pred.shape[0]] = pred
+
+        start_idx = start_idx + pred.shape[0]
+
+    return pred_arr
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+
+    Stable version by Dougal J. Sutherland.
+
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representative data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representative data set.
+
+    Returns:
+    --   : The Frechet Distance.
+    """
+    #ipdb.set_trace()
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1)
+            + np.trace(sigma2) - 2 * tr_covmean)
+
+
+def calculate_activation_statistics(files, model, batch_size=50, dims=2048,
+                                    device='cpu', num_workers=1,reso=128):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : The images numpy array is split into batches with
+                     batch size batch_size. A reasonable batch size
+                     depends on the hardware.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    act = get_activations(files, model, batch_size, dims, device, num_workers,reso=reso)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def compute_statistics_of_path(path, model, batch_size, dims, device,
+                               num_workers=1,reso=512,dataset='gso'):
+    basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-withtop/gso_gt"
+
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid-withtop/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-/metrics/fid-withtop/gso_gt"
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600][:]
+    # all_objs = all_objs[100:600]
+    # all_objs = all_objs[:500]
+
+
+    # if 'shapenet' in dataset:
+    # if 'shapenet' in dataset:
+
+    try:
+        try:
+            
+            m=np.load(os.path.join(basepath,path.split('/')[-1]+str(reso)+'mean.npy'))
+            s=np.load(os.path.join(basepath,path.split('/')[-1]+str(reso)+'std.npy'))
+            print('loading_dataset',dataset)
+        except:
+            files=[]
+            # ! load instances for I23D inference
+            # for obj_folder in tqdm(sorted(os.listdir(path))):
+            #     for idx in range(0,25):
+            #         img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+            #         files.append(img_name)
+                    
+            # ! free3d rendering
+            # for obj_folder in tqdm(sorted(os.listdir(path))):
+            #     for idx in range(0,25):
+            #         # img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+            #         img_name = os.path.join(path, obj_folder, 'render_mvs_25', 'model', f'{idx:03}.png')
+            #         files.append(img_name)
+
+            # ! objv loading
+            for obj_folder in tqdm(all_objs):
+                obj_folder = obj_folder[:-2] # to load 3 chunks
+                for batch in range(1,4):
+                    for idx in range(8):
+                        files.append(os.path.join(path, obj_folder, str(batch), f'{idx}.jpg'))
+
+            # for name in os.listdir(path):
+            #     #ipdb.set_trace()
+            #     # if name not in false1: #and name not in false2 and name not in false3:
+            #     if name in false1: #and name not in false2 and name not in false3:
+            #         img=os.path.join(path,name,'rgb')
+            #         #ipdb.set_trace()
+            #         files = files+sorted([os.path.join(img, idd) for idd in os.listdir(img) if idd.endswith('.png')])
+
+                if len(files) > 50000:
+                    files = files[:50000]
+                    break
+
+            #files=files[:5]
+            m, s = calculate_activation_statistics(files, model, batch_size,
+                                                    dims, device, num_workers,reso=reso)
+            path = Path(path)
+            # ipdb.set_trace()
+            np.save(os.path.join(basepath,path.name+str(reso)+'mean'), m)
+            np.save(os.path.join(basepath,path.name+str(reso)+'std'), s)
+    except Exception as e:
+        print(f'{dataset} failed, ', e)
+
+
+    return m, s
+
+
+def compute_statistics_of_path_new(path, model, batch_size, dims, device,
+                               num_workers=1,reso=128,dataset='omni'):
+    # basepath='/mnt/lustre/yslan/logs/nips23/LSGM/cldm/cmetric/shapenet-outs/fid'+str(reso)+'test'+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/metrics/fid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-subset/'+str(reso)+dataset
+
+    basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-withtop/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid/'+str(reso)+dataset
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+    sample_name=path.split('/')[-1]
+
+    try:
+        try:
+            # ipdb.set_trace()
+            m=np.load(os.path.join(basepath,sample_name+str(reso)+'mean.npy'))
+            s=np.load(os.path.join(basepath,sample_name+str(reso)+'std.npy'))
+            print('loading_sample')
+        except:
+            files=[]
+
+            # for name in os.listdir(path):
+            #     img=os.path.join(path,name)
+            #     files.append(img) # ! directly append
+
+            # for loading gso-like folder
+            # st()
+            # for obj_folder in sorted(os.listdir(path)):
+
+            #     if obj_folder == 'runs':
+            #         continue
+
+            #     if not os.path.isdir(os.path.join(path, obj_folder)):
+            #         continue
+
+            #     for idx in [0]:
+            #         for i in range(24):
+            #             if 'GA' in path:
+            #                 img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+            #             else:
+            #                 img=os.path.join(path,obj_folder, str(idx),f'{i}.jpg')
+            #                 # ipdb.set_trace()
+            #             files.append(img) 
+
+            for obj_folder in tqdm(all_objs):
+                obj_folder = '/'.join(obj_folder.split('/')[1:])
+                for idx in range(24):
+                    # files.append(os.path.join(path, obj_folder, f'{idx}.jpg'))
+                    if 'Lara' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', f'{idx}.jpg'))
+                    elif 'GA' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'sample-0-{idx}.jpg'))
+                    elif 'scale3d' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '1', f'{idx}.png'))
+                    elif 'LRM' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))
+                    else:
+                        files.append(os.path.join(path, obj_folder, '0', f'{idx}.jpg'))
+
+
+            files=files[:50000]
+            m, s = calculate_activation_statistics(files, model, batch_size,
+                                                    dims, device, num_workers,reso=reso)
+            path = Path(path)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'mean'), m)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'std'), s)
+    except Exception as e:
+        print('error sample image', e)
+
+    #ipdb.set_trace()
+
+    return m, s
+
+
+def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1,reso=128,dataset='omni'):
+    """Calculates the FID of two paths"""
+    # for p in paths:
+    #     if not os.path.exists(p):
+    #         raise RuntimeError('Invalid path: %s' % p)
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, # ! GT data
+                                        dims, device, num_workers,reso=reso,dataset=dataset)
+    # ipdb.set_trace()
+    m2, s2 = compute_statistics_of_path_new(paths[1], model, batch_size, # ! generated data
+                                        dims, device, num_workers,reso=reso,dataset=dataset)
+    fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+
+    return fid_value
+
+
+def save_fid_stats(paths, batch_size, device, dims, num_workers=1):
+    """Calculates the FID of two paths"""
+    # if not os.path.exists(paths[0]):
+    #     raise RuntimeError('Invalid path: %s' % paths[0])
+
+    # if os.path.exists(paths[1]):
+    #     raise RuntimeError('Existing output file: %s' % paths[1])
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    print(f"Saving statistics for {paths[0]}")
+
+    m1, s1 = compute_statistics_of_path(paths[0], model, batch_size,
+                                        dims, device, num_workers)
+
+    np.savez_compressed(paths[1], mu=m1, sigma=s1)
+
+
+def main():
+    args = parser.parse_args()
+
+    if args.device is None:
+        device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
+    else:
+        device = torch.device(args.device)
+
+    if args.num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = args.num_workers
+
+    if args.save_stats:
+        save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers)
+        return
+    #ipdb.set_trace()
+    fid_value = calculate_fid_given_paths(args.path,
+                                          args.batch_size,
+                                          device,
+                                          args.dims,
+                                          num_workers,args.reso,args.dataset)
+    print(f'{args.dataset} FID: ', fid_value)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/evaluations/fidkid-pytorch/fps-sample_pcd4096.sh b/evaluations/fidkid-pytorch/fps-sample_pcd4096.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ff3179f701ab795795908ed5300c63e7f84dd034
--- /dev/null
+++ b/evaluations/fidkid-pytorch/fps-sample_pcd4096.sh
@@ -0,0 +1,67 @@
+set -x
+
+# n_proc=4
+# n_points=4096
+# focal=525
+
+# pre-requisites: kaolin and nvdiffrast
+# pip install kaolin==0.16.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.3.0_cu118.html
+
+n_proc=1
+n_points=4096
+focal=24 # as in blender config 
+
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/02958343/fffb1660a38af30ba4cf3601fb6b2442/models/
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/02958343/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd/car
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/03001627/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd-full/chair
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/02691156/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd/plane
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/03001627/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd-full/chair
+
+# ! render gt
+# shape_root=/mnt/sfs-common/yslan/Dataset/GSO/gso-unzip/gso-unzip
+# save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics/gso
+
+# ! use FPS later:
+# splatter-img LGM
+
+# for method_name in ln3diff One-2-3-45 OpenLRM shape-e Lara CRM splatter-img
+# for method_name in ln3diff One-2-3-45 OpenLRM
+# for method_name in shape-e Lara CRM splatter-img
+# for method_name in CRM splatter-img
+
+# for method_name in splatter-img LGM
+
+# for method_name in splatter-img/Animals LGM/Animals
+# for method_name in splatter-img LGM
+
+for method_name in LGM_fixpose
+
+do
+
+# shape_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/${method_name}
+# save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics/${method_name}
+
+shape_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/${method_name}
+save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/${method_name}
+
+python fps_gs.py \
+    --shape_root ${shape_root} \
+    --save_root ${save_root} \
+    --n_proc ${n_proc} \
+    --n_points ${n_points} \
+    --image_height 512 \
+    --image_width 512 \
+    --focal_length_x ${focal} \
+    --focal_length_y ${focal} \
+    --principal_point_x 256 \
+    --principal_point_y 256 \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/fps_gs.py b/evaluations/fidkid-pytorch/fps_gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..902b7022777200ff6e11c26482e5cd78f369b5b5
--- /dev/null
+++ b/evaluations/fidkid-pytorch/fps_gs.py
@@ -0,0 +1,444 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+import json
+import math
+import numpy as np
+import os
+import argparse
+import multiprocessing as mp
+from multiprocessing import Pool
+import trimesh
+import tqdm
+import torch
+import nvdiffrast.torch as dr
+import kaolin as kal
+import glob
+import ipdb
+import pytorch3d.ops
+
+parser = argparse.ArgumentParser(description='sample surface points from mesh')
+parser.add_argument(
+    '--n_proc', type=int, default=8,
+    help='Number of processes to run in parallel'
+         '(0 means sequential execution).')
+parser.add_argument(
+    '--n_points', type=int, default=5000,
+    help='Number of points to sample per model.')
+parser.add_argument(
+    '--n_views', type=int, default=100,
+    help='Number of views per model.')
+parser.add_argument(
+    '--image_height', type=int, default=640,
+    help='Depth image height.')
+parser.add_argument(
+    '--image_width', type=int, default=640,
+    help='Depth image width.')
+parser.add_argument(
+    '--focal_length_x', type=float, default=640,
+    help='Focal length in x direction.')
+parser.add_argument(
+    '--focal_length_y', type=float, default=640,
+    help='Focal length in y direction.')
+parser.add_argument(
+    '--principal_point_x', type=float, default=320,
+    help='Principal point location in x direction.')
+parser.add_argument(
+    '--principal_point_y', type=float, default=320,
+    help='Principal point location in y direction.')
+parser.add_argument("--shape_root", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel27_omni_ablation2/ddpm_5000/test', help="path to the save resules shapenet dataset")
+parser.add_argument("--save_root", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel27_omni_ablation2/ddpm_vis_ab2surface', help="path to the split shapenet dataset")
+
+options = parser.parse_args()
+
+# create array for inverse mapping
+coordspx2 = np.stack(np.nonzero(np.ones((options.image_height, options.image_width))), -1).astype(np.float32)
+coordspx2 = coordspx2[:, ::-1]
+fusion_intrisics = np.array(
+    [
+        [options.focal_length_x, 0, options.principal_point_x],
+        [0, options.focal_length_y, options.principal_point_y],
+        [0, 0, 1]
+    ])
+# glctx = dr.RasterizeGLContext() # EGL/egl.h: No such file or directory
+glctx = dr.RasterizeCudaContext()
+
+
+def CalcLinearZ(depth):
+    # depth = depth * 2 - 1
+    zFar = 100.0
+    zNear = 0.1
+    linear = zNear / (zFar - depth * (zFar - zNear)) * zFar
+    return linear
+
+
+def projection_cv_new(fx, fy, cx, cy, width, height, n=1.0, f=50.0):
+    return np.array(
+        [[-2 * fx / width, 0.0, (width - 2 * cx) / width, 0.0],
+         [0.0, -2 * fy / height, (height - 2 * cy) / height, 0.0],
+         [0.0, 0.0, (-f - n) / (f - n), -2.0 * f * n / (f - n)],
+         [0.0, 0.0, -1.0, 0.0]])
+
+
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    return dr.interpolate(
+        attr.contiguous(), rast, attr_idx, rast_db=rast_db,
+        diff_attrs=None if rast_db is None else 'all')
+
+
+def render_nvdiffrast(v_pos, tris, T_bx4x4):
+    # T_bx4x4 - world to cam
+    proj = projection_cv_new(
+        fx=options.focal_length_x, fy=options.focal_length_y, cx=options.principal_point_x,
+        cy=options.principal_point_y,
+        width=options.image_width, height=options.image_height, n=0.1, f=100.0)
+
+    fix = torch.eye(4, dtype=torch.float32, device='cuda')
+    fix[2, 2] = -1
+    fix[1, 1] = -1
+    fix[0, 0] = -1
+    fix = fix.unsqueeze(0).repeat(T_bx4x4.shape[0], 1, 1)
+
+    proj = torch.tensor(proj, dtype=torch.float32, device='cuda').unsqueeze(0).repeat(T_bx4x4.shape[0], 1, 1)
+    T_world_cam_bx4x4 = torch.bmm(fix, T_bx4x4)
+    mvp = torch.bmm(proj, T_world_cam_bx4x4)
+    v_pos_clip = torch.matmul(
+        torch.nn.functional.pad(v_pos, pad=(0, 1), mode='constant', value=1.0),
+        torch.transpose(mvp, 1, 2))
+    rast, db = dr.rasterize(
+        glctx, torch.tensor(v_pos_clip, dtype=torch.float32, device='cuda'), tris.int(),
+        (options.image_height, options.image_width))
+
+    v_pos_cam = torch.matmul(
+        torch.nn.functional.pad(v_pos, pad=(0, 1), mode='constant', value=1.0),
+        torch.transpose(T_world_cam_bx4x4, 1, 2))
+    gb_pos_cam, _ = interpolate(v_pos_cam, rast, tris.int())
+    depth_maps = gb_pos_cam[..., 2].abs()
+    return depth_maps
+
+
+def as_mesh(scene_or_mesh):
+    """
+    Convert a possible scene to a mesh.
+
+    If conversion occurs, the returned mesh has only vertex and face data.
+    """
+    if isinstance(scene_or_mesh, trimesh.Scene):
+        if len(scene_or_mesh.geometry) == 0:
+            mesh = None  # empty scene
+        else:
+            # we lose texture information here
+            mesh = trimesh.util.concatenate(
+                tuple(
+                    trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
+                    for g in scene_or_mesh.geometry.values()))
+    else:
+        assert (isinstance(scene_or_mesh, trimesh.Trimesh))
+        mesh = scene_or_mesh
+    return mesh
+
+
+def render(mesh_v, mesh_f, Rs):
+    """
+    Render the given mesh using the generated views.
+
+    :param base_mesh: mesh to render
+    :type base_mesh: mesh.Mesh
+    :param Rs: rotation matrices
+    :type Rs: [numpy.ndarray]
+    :return: depth maps
+    :rtype: numpy.ndarray
+    """
+    T_bx4x4 = torch.zeros((options.n_views, 4, 4), dtype=torch.float32, device='cuda')
+    T_bx4x4[:, 3, 3] = 1
+    T_bx4x4[:, 2, 3] = 1
+    T_bx4x4[:, :3, :3] = torch.tensor(Rs, dtype=torch.float32, device='cuda')
+    depthmaps = render_nvdiffrast(
+        mesh_v,
+        mesh_f, T_bx4x4)
+    return depthmaps
+
+
+def get_points():
+    """
+    :param n_points: number of points
+    :type n_points: int
+    :return: list of points
+    :rtype: numpy.ndarray
+    """
+
+    rnd = 1.
+    points = []
+    offset = 2. / options.n_views
+    increment = math.pi * (3. - math.sqrt(5.))
+
+    for i in range(options.n_views):
+        y = ((i * offset) - 1) + (offset / 2)
+        r = math.sqrt(1 - pow(y, 2))
+
+        phi = ((i + rnd) % options.n_views) * increment
+
+        x = math.cos(phi) * r
+        z = math.sin(phi) * r
+
+        points.append([x, y, z])
+    return np.array(points)
+
+
+def get_views(semi_sphere=False):
+    """
+    Generate a set of views to generate depth maps from.
+
+    :param n_views: number of views per axis
+    :type n_views: int
+    :return: rotation matrices
+    :rtype: [numpy.ndarray]
+    """
+
+    Rs = []
+    points = get_points()
+    if semi_sphere:
+        points[:, 2] = -np.abs(points[:, 2]) - 0.1
+
+    for i in range(points.shape[0]):
+        longitude = - math.atan2(points[i, 0], points[i, 1])
+        latitude = math.atan2(points[i, 2], math.sqrt(points[i, 0] ** 2 + points[i, 1] ** 2))
+
+        R_x = np.array(
+            [[1, 0, 0],
+             [0, math.cos(latitude), -math.sin(latitude)],
+             [0, math.sin(latitude), math.cos(latitude)]])
+        R_y = np.array(
+            [[math.cos(longitude), 0, math.sin(longitude)],
+             [0, 1, 0],
+             [-math.sin(longitude), 0, math.cos(longitude)]])
+        R = R_x @ R_y
+        Rs.append(R)
+
+    return Rs
+
+
+def fusion(depthmaps, Rs):
+    """
+    Fuse the rendered depth maps.
+
+    :param depthmaps: depth maps
+    :type depthmaps: numpy.ndarray
+    :param Rs: rotation matrices corresponding to views
+    :type Rs: [numpy.ndarray]
+    :return: (T)SDF
+    :rtype: numpy.ndarray
+    """
+
+    # sample points inside mask
+    sample_per_view = options.n_points // options.n_views
+    sample_bxn = torch.zeros((options.n_views, sample_per_view), device='cuda', dtype=torch.long)
+    for i in range(len(Rs)):
+        mask = depthmaps[i] > 0
+        valid_idx = torch.nonzero(mask.reshape(-1)).squeeze(-1)
+        idx = list(range(valid_idx.shape[0]))
+        np.random.shuffle(idx)
+        idx = idx[:sample_per_view]
+        sample_bxn[i] = torch.tensor(valid_idx[idx])
+
+    depthmaps = torch.gather(depthmaps.reshape(options.n_views, -1), 1, sample_bxn)
+
+    inv_Ks_bx3x3 = torch.tensor(np.linalg.inv(fusion_intrisics), dtype=torch.float32, device='cuda').unsqueeze(
+        0).repeat(options.n_views, 1, 1)
+    T_bx4x4 = torch.zeros((options.n_views, 4, 4), dtype=torch.float32, device='cuda')
+    T_bx4x4[:, 3, 3] = 1
+    T_bx4x4[:, 2, 3] = 1
+    T_bx4x4[:, :3, :3] = torch.tensor(Rs, dtype=torch.float32, device='cuda')
+    inv_T_bx4x4 = torch.inverse(T_bx4x4)
+
+    tf_coords_bxpx2 = torch.tensor(coordspx2.copy(), dtype=torch.float32, device='cuda').unsqueeze(0).repeat(
+        options.n_views, 1, 1)
+    tf_coords_bxpx2 = torch.gather(tf_coords_bxpx2, 1, sample_bxn.unsqueeze(-1).repeat(1, 1, 2))
+
+    tf_coords_bxpx3 = torch.cat([tf_coords_bxpx2, torch.ones_like(tf_coords_bxpx2[..., :1])], -1)
+    tf_coords_bxpx3 *= depthmaps.reshape(options.n_views, -1, 1)
+    tf_cam_bxpx3 = torch.bmm(inv_Ks_bx3x3, tf_coords_bxpx3.transpose(1, 2)).transpose(1, 2)
+    tf_cam_bxpx4 = torch.cat([tf_cam_bxpx3, torch.ones_like(tf_cam_bxpx3[..., :1])], -1)
+    tf_world_bxpx3 = torch.bmm(inv_T_bx4x4, tf_cam_bxpx4.transpose(1, 2)).transpose(1, 2)[..., :3]
+
+    return tf_world_bxpx3.reshape(-1, 3)
+
+
+def normalize(vertices, faces, normalized_scale=0.9, rotate_x=False):
+    vertices = vertices.cuda()
+
+    if rotate_x: # rotate along x axis for 90 degrees to match the two coordiantes
+        rot_mat = torch.eye(n=3, device='cuda')
+        theta = np.pi / 90  # degree
+        rot_mat[1,1] = np.cos(theta)
+        rot_mat[2,2] = np.cos(theta)
+        rot_mat[1,2] =-np.sin(theta)
+        rot_mat[2,1] = np.sin(theta)
+        # ipdb.set_trace()
+        vertices = rot_mat @ vertices.transpose(0,1)
+        vertices = vertices.transpose(0,1)
+
+    scale = (vertices.max(dim=0)[0] - vertices.min(dim=0)[0]).max()
+    mesh_v1 = vertices / scale * normalized_scale
+    mesh_f1 = faces.long().cuda()
+    return mesh_v1, mesh_f1
+
+
+
+def sample_surface_pts(path):
+    # ipdb.set_trace()
+    try:
+        mesh_path, output_pth, debug = path
+        # mesh = kal.io.obj.import_mesh(mesh_path)
+        # ipdb.set_trace()
+        mesh = trimesh.load(mesh_path) # fail to load ply?
+        #ipdb.set_trace()
+        if mesh.vertices.shape[0] == 0:
+            return
+        mesh_v = torch.Tensor(mesh.vertices)
+        mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=0.9, rotate_x=True)
+
+        # generate camera matrices
+        # Rs = get_views()
+        # Rs = get_views(semi_sphere=True)
+        Rs = get_views(semi_sphere=False)
+        # get depth images
+        depths = render(mesh_v, mesh_f, Rs)
+        # project to world space
+        try:
+            pcd = fusion(depths, Rs)
+        except:
+            return
+        pcd = pcd.cpu().numpy()
+
+        #np.savez(output_pth, pcd=pcd)
+        #ipdb.set_trace()
+        #if debug:
+        pcd = trimesh.points.PointCloud(pcd)
+        pcd.export(output_pth.replace('.npz', '.obj'))
+    except Exception as e:
+        # print('error')
+        print(e, flush=True)
+
+
+if __name__ == '__main__':
+    mp.set_start_method('spawn')
+
+    shapenet_root = options.shape_root
+    save_root = options.save_root
+
+    debug = True
+    #model_list = sorted(os.listdir(shapenet_root))[:7500]
+
+    # model_list=glob.glob(os.path.join(shapenet_root, '*.obj'))
+    # os.makedirs(save_root, exist_ok=True)
+
+    # cmds = [(os.path.join(shapenet_root, id.split('/')[-1]), os.path.join(save_root, id.split('/')[-1]), debug) for id in model_list]
+
+    # cmds = [(os.path.join(shapenet_root, id.split('/')[-1]), os.path.join(save_root, 'pcd_4096.ply'), debug) for id in model_list]
+
+    # cmds += [(os.path.join(shapenet_root, id.split('/')[-1]), os.path.join(save_root, 'test.obj'), debug) for id in model_list]
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    cmds = [] 
+
+    # for instance_name in os.listdir(shapenet_root)[:]:
+    #     cmds += [(os.path.join(shapenet_root, instance_name), os.path.join(save_root, f'{instance_name.split(".")[0]}_pcd_4096.ply'), debug)]
+
+    # ! for gt
+    # for obj_folder in sorted(os.listdir(shapenet_root)):
+    #     cmds += [(os.path.join(shapenet_root, obj_folder, 'meshes/model.obj'), os.path.join(save_root, f'{obj_folder}_pcd_4096.ply'), debug)]
+
+    # ! for baseline samples
+
+    os.makedirs(save_root, exist_ok=True)
+
+    # ! free3d
+    # for obj_folder in tqdm.tqdm(sorted(os.listdir(shapenet_root))):
+    #     if not os.path.isdir(os.path.join(shapenet_root, obj_folder)):
+    #         continue
+        
+    #     if 'LGM' in shapenet_root:
+    #         gs_path = os.path.join(shapenet_root,obj_folder, f'0gaussian.ply') 
+    #     else: # splatter-img
+    #         gs_path = os.path.join(shapenet_root,obj_folder, f'0/mesh.ply') 
+
+    #     pcd = trimesh.load(gs_path).vertices # unsqueeze()
+    #     fps_pcd, fps_idx = pytorch3d.ops.sample_farthest_points(
+    #         # torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4096,
+    #         torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4000,
+    #         random_start_point=True)  # B self.latent_num
+    #     # assert fps_pcd.shape[1] == 4096
+
+    #     pcd = trimesh.points.PointCloud(fps_pcd[0].cpu().numpy())
+    #     output_path = os.path.join(save_root, f'{obj_folder}_pcd_4096.ply')
+    #     pcd.export(output_path.replace('.npz', '.obj'))
+
+    # objv
+    # for obj_folder in tqdm.tqdm(sorted(os.listdir(all_objs))):
+    for obj_folder in tqdm.tqdm(all_objs):
+        # ipdb.set_trace()
+
+        if not os.path.isdir(os.path.join(shapenet_root, obj_folder)):
+            continue
+
+        save_name = '-'.join(obj_folder.split('/'))
+    
+        
+        if 'LGM' in shapenet_root:
+            gs_path = os.path.join(shapenet_root,obj_folder, f'0gaussian.ply') 
+        else: # splatter-img
+            gs_path = os.path.join(shapenet_root,obj_folder, f'0/mesh.ply') 
+
+        pcd = trimesh.load(gs_path).vertices # unsqueeze()
+        fps_pcd, fps_idx = pytorch3d.ops.sample_farthest_points(
+            # torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4096,
+            torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4000,
+            random_start_point=True)  # B self.latent_num
+        # assert fps_pcd.shape[1] == 4096
+
+        pcd = trimesh.points.PointCloud(fps_pcd[0].cpu().numpy())
+        output_path = os.path.join(save_root, f'{save_name}_pcd_4096.ply')
+        pcd.export(output_path.replace('.npz', '.obj'))
+
+
+        # ! lgm
+
+        # for idx in [0]:
+            # for i in range(10):
+            #     img=os.path.join(shapenet_root,obj_folder, str(idx),f'{i}.jpg')
+                # img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+                # files.append(img) 
+
+            # if 'CRM' in shapenet_root:
+            #     # ipdb.set_trace()
+            #     mesh_path = glob.glob(os.path.join(shapenet_root, obj_folder, f'{idx}', '*.obj'))[0]
+            # else:
+
+            # if os.path.exists((os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.obj'))):
+            #     mesh_path = os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.obj')
+            # else:
+            #     mesh_path = os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.ply')
+
+            # cmds += [(mesh_path, os.path.join(save_root, f'{obj_folder}_pcd_4096.ply'), debug)]
+
+
+    if options.n_proc == 0:
+        for filepath in tqdm.tqdm(cmds):
+            sample_surface_pts(filepath)
+    else:
+        with Pool(options.n_proc) as p:
+            list(tqdm.tqdm(p.imap(sample_surface_pts, cmds), total=len(cmds)))
diff --git a/evaluations/fidkid-pytorch/kid_gen3d_baseline.sh b/evaluations/fidkid-pytorch/kid_gen3d_baseline.sh
new file mode 100644
index 0000000000000000000000000000000000000000..17fd01f125d4a59978ec8bdd0829ba3c106f6bfa
--- /dev/null
+++ b/evaluations/fidkid-pytorch/kid_gen3d_baseline.sh
@@ -0,0 +1,43 @@
+set -x 
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="gso-rendering"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir
+
+# method_name=LGM
+# for method_name in LGM CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD
+
+
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv
+gso_rendering="/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K"
+
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+# ! objv dataset eval
+# for method_name in Lara CRM/Animals ln3diff-lite/Animals GA/stage-2/dino_img/ditl-fromditxlPCD One-2-3-45/Animals shape-e/Animals splatter-img/Animals OpenLRM/Animals LGM/Animals
+# LGM_fixpose/Animals 
+# for method_name in One-2-3-45/Animals 
+
+# for method_name in ln3diff-fixpose_192 CRM LGM_fixpose One-2-3-45/Animals Lara CRM/Animals ln3diff-lite/Animals GA/stage-2/dino_img/ditl-fromditxlPCD One-2-3-45/Animals shape-e/Animals splatter-img/Animals OpenLRM/Animals LGM/Animals
+
+# for method_name in GA-fixpose
+
+# for method_name in scale3d/eval/eval_nerf/Animals scale3d/eval/eval_mesh/Animals
+
+for method_name in LGM_fixpose/Animals Lara CRM/Animals ln3diff-lite/Animals GA/stage-2/dino_img/ditl-fromditxlPCD One-2-3-45/Animals shape-e/Animals splatter-img/Animals OpenLRM/Animals
+
+do
+
+python kid_score_gso.py \
+    --true $gso_rendering \
+    --fake ${output_path}/${method_name} \
+    --dataset ${method_name} \
+    --reso 512 \
+    --gpu 0 \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/kid_score_gso.py b/evaluations/fidkid-pytorch/kid_score_gso.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a7ac1372ac82d83f13ed10f3382d05ead183b15
--- /dev/null
+++ b/evaluations/fidkid-pytorch/kid_score_gso.py
@@ -0,0 +1,525 @@
+#!/usr/bin/env python3
+"""Calculates the Kernel Inception Distance (KID) to evalulate GANs
+"""
+import os
+import pathlib
+import sys
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
+
+import json
+import numpy as np
+import torch
+from sklearn.metrics.pairwise import polynomial_kernel
+from scipy import linalg
+from PIL import Image
+from torch.nn.functional import adaptive_avg_pool2d
+import ipdb
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If not tqdm is not available, provide a mock version of it
+    def tqdm(x): return x
+import cv2
+from models.inception import InceptionV3
+from models.lenet import LeNet5
+import glob
+import pathlib
+
+def get_activations(files, model, batch_size=50, dims=2048,
+                    cuda=False, verbose=False,reso=128):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : Batch size of images for the model to process at once.
+                     Make sure that the number of samples is a multiple of
+                     the batch size, otherwise some samples are ignored. This
+                     behavior is retained to match the original FID score
+                     implementation.
+    -- dims        : Dimensionality of features returned by Inception
+    -- cuda        : If set to True, use GPU
+    -- verbose     : If set to True and parameter out_step is given, the number
+                     of calculated batches is reported.
+    Returns:
+    -- A numpy array of dimension (num images, dims) that contains the
+       activations of the given tensor when feeding inception with the
+       query tensor.
+    """
+    model.eval()
+
+    is_numpy = True if type(files[0]) == np.ndarray else False
+
+    if len(files) % batch_size != 0:
+        print(('Warning: number of images is not a multiple of the '
+               'batch size. Some samples are going to be ignored.'))
+    if batch_size > len(files):
+        print(('Warning: batch size is bigger than the data size. '
+               'Setting batch size to data size'))
+        batch_size = len(files)
+
+    n_batches = len(files) // batch_size
+    n_used_imgs = n_batches * batch_size
+
+    pred_arr = np.empty((n_used_imgs, dims))
+
+    for i in tqdm(range(n_batches)):
+        if verbose:
+            print('\rPropagating batch %d/%d' % (i + 1, n_batches), end='', flush=True)
+        start = i * batch_size
+        end = start + batch_size
+        if is_numpy:
+            images = np.copy(files[start:end]) + 1
+            images /= 2.
+        else:
+            images=[]
+            #ipdb.set_trace()
+            for f in files[start:end]:
+                try:
+                    img=cv2.imread(str(f))
+                    #if img.mean(-1)>254.9:
+                    #img[np.where(img.mean(-1)>254.9)]=0
+                    img=cv2.resize(img,(reso,reso),interpolation=cv2.INTER_CUBIC)
+                    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+                except:
+                    img=cv2.imread(str(files[0]))
+                    #if img.mean(-1)>254.9:
+                    #img[np.where(img.mean(-1)>254.9)]=0
+                    img=cv2.resize(img,(reso,reso),interpolation=cv2.INTER_CUBIC)
+                    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+                    print(str(f))
+                #ipdb.set_trace()
+                images.append(img)
+            #ipdb.set_trace()
+
+
+            #images = [np.array(Image.open(str(f)).convert('RGB')) for f in files[start:end]]
+            images = np.stack(images).astype(np.float32) / 255.
+            # Reshape to (n_images, 3, height, width)
+            images = images.transpose((0, 3, 1, 2))
+            #ipdb.set_trace()
+            
+
+        batch = torch.from_numpy(images).type(torch.FloatTensor)
+        if cuda:
+            batch = batch.cuda()
+
+        pred = model(batch)[0]
+
+        # If model output is not scalar, apply global spatial average pooling.
+        # This happens if you choose a dimensionality not equal 2048.
+        if pred.shape[2] != 1 or pred.shape[3] != 1:
+            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
+
+        pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1)
+
+    if verbose:
+        print('done', np.min(images))
+
+    return pred_arr
+
+
+def extract_lenet_features(imgs, net):
+    net.eval()
+    feats = []
+    imgs = imgs.reshape([-1, 100] + list(imgs.shape[1:]))
+    if imgs[0].min() < -0.001:
+      imgs = (imgs + 1)/2.0
+    print(imgs.shape, imgs.min(), imgs.max())
+    imgs = torch.from_numpy(imgs)
+    for i, images in enumerate(imgs):
+        feats.append(net.extract_features(images).detach().cpu().numpy())
+    feats = np.vstack(feats)
+    return feats
+
+
+def _compute_activations(path, model, batch_size, dims, cuda, model_type,reso,dataset):
+    sample_name=path.split('/')[-1]
+    # basepath='/mnt/petrelfs/caoziang/3D_generation/cmetric/kid'+str(reso)
+    # basepath='/mnt/lustre/yslan/logs/nips23/LSGM/cldm/cmetric/shapenet-outs/kid'+str(reso)+'test'+dataset
+    # basepath='/mnt/lustre/yslan/logs/nips23/LSGM/cldm/cmetric/shapenet-outs-testTra/kid'+str(reso)+'test'+dataset
+
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/metrics/kid/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/kid/gso_gt"
+
+    basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/kid/gso_gt"
+
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+    files=[]
+
+    #path = pathlib.Path(path)
+    #oripath=path
+
+    # ! objv dataset
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    for obj_folder in tqdm(all_objs):
+        obj_folder = obj_folder[:-2] # to load 3 chunks
+        for batch in range(1,4):
+            for idx in range(8):
+                files.append(os.path.join(path, obj_folder, str(batch), f'{idx}.jpg'))
+
+
+    # for obj_folder in tqdm(sorted(os.listdir(path))):
+    #     for idx in range(0,25):
+    #         # img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+    #         img_name = os.path.join(path, obj_folder, 'render_mvs_25', 'model', f'{idx:03}.png')
+            # files.append(img_name)
+
+
+    '''
+    if not os.path.exists(os.path.join(basepath,path.split('/')[-1]+str(reso)+'kid.npy')):
+        import glob
+        import pathlib
+        path = pathlib.Path(path)
+        if not type(path) == np.ndarray:
+            files=[]
+
+            # load gso
+            for obj_folder in tqdm(sorted(os.listdir(path))):
+                for idx in range(0,25):
+                # for idx in [0]:
+                    img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+                    files.append(img_name)
+
+                if len(files) > 50000:
+                    files = files[:50000]
+                    break
+    '''
+
+    if model_type == 'inception':
+        if os.path.exists(os.path.join(basepath,sample_name+str(reso)+'kid.npy')):
+            act=np.load(os.path.join(basepath,sample_name+str(reso)+'kid.npy'))
+            print('load_dataset',dataset)
+        else:
+            act = get_activations(files, model, batch_size, dims, cuda,reso=reso)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'kid'),act)
+    elif model_type == 'lenet':
+        act = extract_lenet_features(files, model)
+    #ipdb.set_trace()
+    return act
+
+
+def _compute_activations_new(path, model, batch_size, dims, cuda, model_type,reso,dataset):
+    sample_name=path.split('/')[-1]
+    # basepath='/mnt/petrelfs/caoziang/3D_generation/cmetric/get3d/kid'+str(reso)+'test'+dataset
+    # basepath='/mnt/lustre/yslan/logs/nips23/LSGM/cldm/cmetric/shapenet-outs/kid'+str(reso)+'test'+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/metrics/kid'+str(reso)+'test'+dataset
+    # if '_cond' in path:
+    #     basepath=basepath+'_cond'
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/kid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/kid/'+str(reso)+dataset
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    all_objs = dataset_json['Animals'][::3][1100:2200][:600]
+
+
+    if not type(path) == np.ndarray:
+        import glob
+        import pathlib
+
+        #ipdb.set_trace()
+        
+        files=[]
+
+        # path = pathlib.Path(path)
+        # for classname in os.listdir(path):
+        #     classpath=os.path.join(path,classname)
+        #     #ipdb.set_trace()
+            
+        #     for instance in os.listdir(classpath):
+
+        # if os.path.isdir(os.path.join(classpath,instance)):
+        # img=os.path.join(classpath,instance)
+        # if 'diffusion' in img:
+        #     files = files+sorted([os.path.join(img, idd) for idd in os.listdir(img) if idd.endswith('ddpm.png')])
+        # else:
+
+        '''
+        for obj_folder in sorted(os.listdir(path)):
+            if not os.path.isdir(os.path.join(path, obj_folder)):
+                continue
+            # for idx in os.listdir(os.path.join(path, obj_folder)):
+            # for idx in range(0,25,5):
+            for idx in [0]:
+                for i in range(10):
+                    # img=os.path.join(path,obj_folder, str(idx),f'{i}.jpg')
+
+                    if 'GA' in path:
+                        img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+                    else:
+                        img=os.path.join(path,obj_folder, str(idx),f'{i}.jpg')
+
+                    files.append(img) 
+        '''
+
+        # ! objv
+        for obj_folder in tqdm(all_objs):
+            obj_folder = '/'.join(obj_folder.split('/')[1:])
+            for idx in range(24):
+                # files.append(os.path.join(path, obj_folder, f'{idx}.jpg'))
+                if 'Lara' in path:
+                    files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', f'{idx}.jpg'))
+                elif 'GA' in path:
+                    files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'sample-0-{idx}.jpg'))
+                elif 'scale3d' in path:
+                    files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '1', f'{idx}.png'))
+                elif 'LRM' in path:
+                    files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))
+                else:
+                    files.append(os.path.join(path, obj_folder, '0', f'{idx}.jpg'))
+
+        # ! gso
+        # for obj_folder in sorted(os.listdir(path)):
+
+        #     if obj_folder == 'runs':
+        #         continue
+
+        #     if not os.path.isdir(os.path.join(path, obj_folder)):
+        #         continue
+
+        #     for idx in [0]:
+        #         for i in range(24):
+        #             if 'GA' in path:
+        #                 img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+        #             else:
+        #                 img=os.path.join(path,obj_folder, str(idx),f'{i}.jpg')
+        #                 # ipdb.set_trace()
+        #             files.append(img) 
+
+
+
+        # for name in os.listdir(path):
+        #     #ipdb.set_trace()
+        #     # if os.path.isdir(os.path.join(path,name)): # ! no cls
+            
+        #     img=os.path.join(path,name)
+        #     files.append(img) # ! directly append
+
+            # files = files+sorted([os.path.join(img, idd) for idd in os.listdir(img) if idd.endswith('.png')])
+    
+    
+    # ipdb.set_trace()
+    files=files[:50000]
+    
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+
+    #ipdb.set_trace()
+    if model_type == 'inception':
+        if os.path.exists(os.path.join(basepath,sample_name+str(reso)+'kid.npy')):
+            act=np.load(os.path.join(basepath,sample_name+str(reso)+'kid.npy'))
+            print('load_sample')
+        else:
+            act = get_activations(files, model, batch_size, dims, cuda,reso=reso)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'kid'),act)
+    elif model_type == 'lenet':
+        act = extract_lenet_features(files, model)
+    #ipdb.set_trace()
+    return act
+
+def calculate_kid_given_paths(paths, batch_size, cuda, dims, model_type='inception',reso=128,dataset='omni'):
+    """Calculates the KID of two paths"""
+    pths = []
+    for p in paths:
+        if not os.path.exists(p):
+            raise RuntimeError('Invalid path: %s' % p)
+        if os.path.isdir(p):
+            pths.append(p)
+        # elif p.endswith('.npy'):
+        #     np_imgs = np.load(p)
+        #     if np_imgs.shape[0] > 50000: np_imgs = np_imgs[np.random.permutation(np.arange(np_imgs.shape[0]))][:50000]
+        #     pths.append(np_imgs)
+
+    if model_type == 'inception':
+        block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+        model = InceptionV3([block_idx])
+    elif model_type == 'lenet':
+        model = LeNet5()
+        model.load_state_dict(torch.load('./models/lenet.pth'))
+    if cuda:
+       model.cuda()
+
+    act_true = _compute_activations(pths[0], model, batch_size, dims, cuda, model_type,reso,dataset)
+    pths = pths[1:]
+    results = []
+    #ipdb.set_trace()
+    for j, pth in enumerate(pths):
+        print(paths[j+1])
+        actj = _compute_activations_new(pth, model, batch_size, dims, cuda, model_type,reso,dataset)
+        #ipdb.set_trace()
+        kid_values = polynomial_mmd_averages(act_true, actj, n_subsets=100)
+        results.append((paths[j+1], kid_values[0].mean(), kid_values[0].std()))
+    return results
+
+def _sqn(arr):
+    flat = np.ravel(arr)
+    return flat.dot(flat)
+
+
+def polynomial_mmd_averages(codes_g, codes_r, n_subsets=50, subset_size=1000,
+                            ret_var=True, output=sys.stdout, **kernel_args):
+    m = min(codes_g.shape[0], codes_r.shape[0])
+    mmds = np.zeros(n_subsets)
+    if ret_var:
+        vars = np.zeros(n_subsets)
+    choice = np.random.choice
+    #ipdb.set_trace()
+
+    with tqdm(range(n_subsets), desc='MMD', file=output) as bar:
+        for i in bar:
+            
+            g = codes_g[choice(len(codes_g), subset_size, replace=False)]
+            r = codes_r[choice(len(codes_r), subset_size, replace=False)]
+            o = polynomial_mmd(g, r, **kernel_args, var_at_m=m, ret_var=ret_var)
+            if ret_var:
+                mmds[i], vars[i] = o
+            else:
+                mmds[i] = o
+            bar.set_postfix({'mean': mmds[:i+1].mean()})
+    return (mmds, vars) if ret_var else mmds
+
+
+def polynomial_mmd(codes_g, codes_r, degree=3, gamma=None, coef0=1,
+                   var_at_m=None, ret_var=True):
+    # use  k(x, y) = (gamma <x, y> + coef0)^degree
+    # default gamma is 1 / dim
+    X = codes_g
+    Y = codes_r
+
+    K_XX = polynomial_kernel(X, degree=degree, gamma=gamma, coef0=coef0)
+    K_YY = polynomial_kernel(Y, degree=degree, gamma=gamma, coef0=coef0)
+    K_XY = polynomial_kernel(X, Y, degree=degree, gamma=gamma, coef0=coef0)
+
+    return _mmd2_and_variance(K_XX, K_XY, K_YY,
+                              var_at_m=var_at_m, ret_var=ret_var)
+
+def _mmd2_and_variance(K_XX, K_XY, K_YY, unit_diagonal=False,
+                       mmd_est='unbiased', block_size=1024,
+                       var_at_m=None, ret_var=True):
+    # based on
+    # https://github.com/dougalsutherland/opt-mmd/blob/master/two_sample/mmd.py
+    # but changed to not compute the full kernel matrix at once
+    m = K_XX.shape[0]
+    assert K_XX.shape == (m, m)
+    assert K_XY.shape == (m, m)
+    assert K_YY.shape == (m, m)
+    if var_at_m is None:
+        var_at_m = m
+
+    # Get the various sums of kernels that we'll use
+    # Kts drop the diagonal, but we don't need to compute them explicitly
+    if unit_diagonal:
+        diag_X = diag_Y = 1
+        sum_diag_X = sum_diag_Y = m
+        sum_diag2_X = sum_diag2_Y = m
+    else:
+        diag_X = np.diagonal(K_XX)
+        diag_Y = np.diagonal(K_YY)
+
+        sum_diag_X = diag_X.sum()
+        sum_diag_Y = diag_Y.sum()
+
+        sum_diag2_X = _sqn(diag_X)
+        sum_diag2_Y = _sqn(diag_Y)
+
+    Kt_XX_sums = K_XX.sum(axis=1) - diag_X
+    Kt_YY_sums = K_YY.sum(axis=1) - diag_Y
+    K_XY_sums_0 = K_XY.sum(axis=0)
+    K_XY_sums_1 = K_XY.sum(axis=1)
+
+    Kt_XX_sum = Kt_XX_sums.sum()
+    Kt_YY_sum = Kt_YY_sums.sum()
+    K_XY_sum = K_XY_sums_0.sum()
+
+    if mmd_est == 'biased':
+        mmd2 = ((Kt_XX_sum + sum_diag_X) / (m * m)
+                + (Kt_YY_sum + sum_diag_Y) / (m * m)
+                - 2 * K_XY_sum / (m * m))
+    else:
+        assert mmd_est in {'unbiased', 'u-statistic'}
+        mmd2 = (Kt_XX_sum + Kt_YY_sum) / (m * (m-1))
+        if mmd_est == 'unbiased':
+            mmd2 -= 2 * K_XY_sum / (m * m)
+        else:
+            mmd2 -= 2 * (K_XY_sum - np.trace(K_XY)) / (m * (m-1))
+
+    if not ret_var:
+        return mmd2
+
+    Kt_XX_2_sum = _sqn(K_XX) - sum_diag2_X
+    Kt_YY_2_sum = _sqn(K_YY) - sum_diag2_Y
+    K_XY_2_sum = _sqn(K_XY)
+
+    dot_XX_XY = Kt_XX_sums.dot(K_XY_sums_1)
+    dot_YY_YX = Kt_YY_sums.dot(K_XY_sums_0)
+
+    m1 = m - 1
+    m2 = m - 2
+    zeta1_est = (
+        1 / (m * m1 * m2) * (
+            _sqn(Kt_XX_sums) - Kt_XX_2_sum + _sqn(Kt_YY_sums) - Kt_YY_2_sum)
+        - 1 / (m * m1)**2 * (Kt_XX_sum**2 + Kt_YY_sum**2)
+        + 1 / (m * m * m1) * (
+            _sqn(K_XY_sums_1) + _sqn(K_XY_sums_0) - 2 * K_XY_2_sum)
+        - 2 / m**4 * K_XY_sum**2
+        - 2 / (m * m * m1) * (dot_XX_XY + dot_YY_YX)
+        + 2 / (m**3 * m1) * (Kt_XX_sum + Kt_YY_sum) * K_XY_sum
+    )
+    zeta2_est = (
+        1 / (m * m1) * (Kt_XX_2_sum + Kt_YY_2_sum)
+        - 1 / (m * m1)**2 * (Kt_XX_sum**2 + Kt_YY_sum**2)
+        + 2 / (m * m) * K_XY_2_sum
+        - 2 / m**4 * K_XY_sum**2
+        - 4 / (m * m * m1) * (dot_XX_XY + dot_YY_YX)
+        + 4 / (m**3 * m1) * (Kt_XX_sum + Kt_YY_sum) * K_XY_sum
+    )
+    var_est = (4 * (var_at_m - 2) / (var_at_m * (var_at_m - 1)) * zeta1_est
+               + 2 / (var_at_m * (var_at_m - 1)) * zeta2_est)
+
+    return mmd2, var_est
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--true', type=str, required=True,
+                        help=('Path to the true images'))
+    parser.add_argument('--fake', type=str, nargs='+', required=True,
+                        help=('Path to the generated images'))
+    parser.add_argument('--batch-size', type=int, default=100,
+                        help='Batch size to use')
+    parser.add_argument('--reso', type=int, default=128,
+                        help='Batch size to use')
+    parser.add_argument('--dims', type=int, default=2048,
+                        choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
+                        help=('Dimensionality of Inception features to use. '
+                              'By default, uses pool3 features'))
+    parser.add_argument('-c', '--gpu', default='0', type=str,
+                        help='GPU to use (leave blank for CPU only)')
+    parser.add_argument('--model', default='inception', type=str,
+                        help='inception or lenet')
+    parser.add_argument('--dataset', default='omni', type=str,
+                        help='inception or lenet')
+    args = parser.parse_args()
+    print(args)
+    #ipdb.set_trace()
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+    paths = [args.true] + args.fake
+    
+
+    results = calculate_kid_given_paths(paths, args.batch_size,True, args.dims, model_type=args.model,reso=args.reso,dataset=args.dataset)
+    for p, m, s in results:
+        print('KID (%s): %.6f (%.6f)' % (p, m, s))
diff --git a/evaluations/fidkid-pytorch/models/__pycache__/inception.cpython-39.pyc b/evaluations/fidkid-pytorch/models/__pycache__/inception.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..36e8eb769d7138fc13ad8fc5fc6d54c4c3cda49f
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diff --git a/evaluations/fidkid-pytorch/models/__pycache__/lenet.cpython-39.pyc b/evaluations/fidkid-pytorch/models/__pycache__/lenet.cpython-39.pyc
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diff --git a/evaluations/fidkid-pytorch/models/inception.py b/evaluations/fidkid-pytorch/models/inception.py
new file mode 100644
index 0000000000000000000000000000000000000000..6da57bf4de182c4ddeb93f0acf948055bc369efb
--- /dev/null
+++ b/evaluations/fidkid-pytorch/models/inception.py
@@ -0,0 +1,141 @@
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import models
+
+
+class InceptionV3(nn.Module):
+    """Pretrained InceptionV3 network returning feature maps"""
+
+    # Index of default block of inception to return,
+    # corresponds to output of final average pooling
+    DEFAULT_BLOCK_INDEX = 3
+
+    # Maps feature dimensionality to their output blocks indices
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,   # First max pooling features
+        192: 1,  # Second max pooling featurs
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self,
+                 output_blocks=[DEFAULT_BLOCK_INDEX],
+                 resize_input=True,
+                 normalize_input=True,
+                 requires_grad=False):
+        """Build pretrained InceptionV3
+
+        Parameters
+        ----------
+        output_blocks : list of int
+            Indices of blocks to return features of. Possible values are:
+                - 0: corresponds to output of first max pooling
+                - 1: corresponds to output of second max pooling
+                - 2: corresponds to output which is fed to aux classifier
+                - 3: corresponds to output of final average pooling
+        resize_input : bool
+            If true, bilinearly resizes input to width and height 299 before
+            feeding input to model. As the network without fully connected
+            layers is fully convolutional, it should be able to handle inputs
+            of arbitrary size, so resizing might not be strictly needed
+        normalize_input : bool
+            If true, scales the input from range (0, 1) to the range the
+            pretrained Inception network expects, namely (-1, 1)
+        requires_grad : bool
+            If true, parameters of the model require gradient. Possibly useful
+            for finetuning the network
+        """
+        super(InceptionV3, self).__init__()
+
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, \
+            'Last possible output block index is 3'
+
+        self.blocks = nn.ModuleList()
+
+        inception = models.inception_v3(pretrained=True)
+
+        # Block 0: input to maxpool1
+        block0 = [
+            inception.Conv2d_1a_3x3,
+            inception.Conv2d_2a_3x3,
+            inception.Conv2d_2b_3x3,
+            nn.MaxPool2d(kernel_size=3, stride=2)
+        ]
+        self.blocks.append(nn.Sequential(*block0))
+
+        # Block 1: maxpool1 to maxpool2
+        if self.last_needed_block >= 1:
+            block1 = [
+                inception.Conv2d_3b_1x1,
+                inception.Conv2d_4a_3x3,
+                nn.MaxPool2d(kernel_size=3, stride=2)
+            ]
+            self.blocks.append(nn.Sequential(*block1))
+
+        # Block 2: maxpool2 to aux classifier
+        if self.last_needed_block >= 2:
+            block2 = [
+                inception.Mixed_5b,
+                inception.Mixed_5c,
+                inception.Mixed_5d,
+                inception.Mixed_6a,
+                inception.Mixed_6b,
+                inception.Mixed_6c,
+                inception.Mixed_6d,
+                inception.Mixed_6e,
+            ]
+            self.blocks.append(nn.Sequential(*block2))
+
+        # Block 3: aux classifier to final avgpool
+        if self.last_needed_block >= 3:
+            block3 = [
+                inception.Mixed_7a,
+                inception.Mixed_7b,
+                inception.Mixed_7c,
+                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+            ]
+            self.blocks.append(nn.Sequential(*block3))
+
+        for param in self.parameters():
+            param.requires_grad = requires_grad
+
+    def forward(self, inp):
+        """Get Inception feature maps
+
+        Parameters
+        ----------
+        inp : torch.autograd.Variable
+            Input tensor of shape Bx3xHxW. Values are expected to be in
+            range (0.0, 1.0)
+
+        Returns
+        -------
+        List of torch.autograd.Variable, corresponding to the selected output
+        block, sorted ascending by index
+        """
+        outp = []
+        x = inp
+
+        if self.resize_input:
+            x = F.interpolate(x,
+                              size=(299, 299),
+                              mode='bilinear',
+                              align_corners=False)
+
+        if self.normalize_input:
+            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if idx in self.output_blocks:
+                outp.append(x)
+
+            if idx == self.last_needed_block:
+                break
+
+        return outp
diff --git a/evaluations/fidkid-pytorch/models/lenet.pth b/evaluations/fidkid-pytorch/models/lenet.pth
new file mode 100644
index 0000000000000000000000000000000000000000..29f3b245cad59581f6ea12005062a9ea225ac6a4
--- /dev/null
+++ b/evaluations/fidkid-pytorch/models/lenet.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4bed3817dd7723c605580e7b7393538a5dfc3508aacc4890b07e7f07c16bebf
+size 248652
diff --git a/evaluations/fidkid-pytorch/models/lenet.py b/evaluations/fidkid-pytorch/models/lenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab495e46bd9125c7534ec7fcc545ea7f8f213e8b
--- /dev/null
+++ b/evaluations/fidkid-pytorch/models/lenet.py
@@ -0,0 +1,50 @@
+import torch.nn as nn
+from collections import OrderedDict
+
+
+class LeNet5(nn.Module):
+    """
+    Input - 1x32x32
+    C1 - 6@28x28 (5x5 kernel)
+    tanh
+    S2 - 6@14x14 (2x2 kernel, stride 2) Subsampling
+    C3 - 16@10x10 (5x5 kernel, complicated shit)
+    tanh
+    S4 - 16@5x5 (2x2 kernel, stride 2) Subsampling
+    C5 - 120@1x1 (5x5 kernel)
+    F6 - 84
+    tanh
+    F7 - 10 (Output)
+    """
+    def __init__(self):
+        super(LeNet5, self).__init__()
+
+        self.convnet = nn.Sequential(OrderedDict([
+            ('c1', nn.Conv2d(1, 6, kernel_size=(5, 5))),
+            ('tanh1', nn.Tanh()),
+            ('s2', nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=1)),
+            ('c3', nn.Conv2d(6, 16, kernel_size=(5, 5))),
+            ('tanh3', nn.Tanh()),
+            ('s4', nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=1)),
+            ('c5', nn.Conv2d(16, 120, kernel_size=(5, 5))),
+            ('tanh5', nn.Tanh())
+        ]))
+
+        self.fc = nn.Sequential(OrderedDict([
+            ('f6', nn.Linear(120, 84)),
+            ('tanh6', nn.Tanh()),
+            ('f7', nn.Linear(84, 10)),
+            ('sig7', nn.LogSoftmax(dim=-1))
+        ]))
+
+    def forward(self, img):
+        output = self.convnet(img)
+        output = output.view(img.size(0), -1)
+        output = self.fc(output)
+        return output
+
+    def extract_features(self, img):
+        output = self.convnet(img.float())
+        output = output.view(img.size(0), -1)
+        output = self.fc[1](self.fc[0](output))
+        return output
diff --git a/evaluations/fidkid-pytorch/musiq_fid_gen3d_baseline.sh b/evaluations/fidkid-pytorch/musiq_fid_gen3d_baseline.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cc6afb5390c0cf7519169210de19c3b6e31dd090
--- /dev/null
+++ b/evaluations/fidkid-pytorch/musiq_fid_gen3d_baseline.sh
@@ -0,0 +1,65 @@
+set -x 
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/gso-rendering"
+# gso_rendering="gso-rendering"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir
+
+
+# ! gso stuffs
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/Obajverse/Objv-animals-for-FID_eval-2K"
+gt_rendering="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/lint-dir/objv-gt"
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/lint-dir
+output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/
+
+# method_name=LGM
+# for method_name in CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in CRM Lara ln3diff One-2-3-45 OpenLRM shape-e splatter-img
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD
+# for method_name in CRM/Animals ln3diff Lara
+# Lara 
+
+# for method_name in Lara ln3diff/Animals 
+# for method_name in Lara GA/stage-2/dino_img/ditl-fromditlPCD ln3diff/Animals 
+# for method_name in CRM/Animals
+# for method_name in ln3diff-lite/Animals 
+# for method_name in GA/stage-2/dino_img/ditl-fromditxlPCD 
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD LGM_fixpose/Animals 
+# for method_name in GA/stage-2/dino_img/ditl-fromditxlPCD-47w
+
+
+
+
+# ! GSO
+
+# output_path=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d
+# gso_rendering="/mnt/sfs-common/yslan/Dataset/GSO/google_scanned_blender_25_w2c"
+
+# for method_name in ln3diff-fixpose_192 CRM LGM_fixpose
+# for method_name in GA-fixpose
+# for method_name in LGM_fixpose_r=1.8/Animals
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD LGM_fixpose/Animals
+
+
+# for method_name in Lara ln3diff/Animals OpenLRM/Animals One-2-3-45/Animals shape-e/Animals splatter-img/Animals CRM/Animals
+# for method_name in Lara 
+# for method_name in Lara 
+
+for method_name in scale3d/eval/eval_nerf/Animals scale3d/eval/eval_mesh/Animals
+
+do
+
+python musiq_score_gso.py $gt_rendering ${output_path}/${method_name} \
+    --dataset ${method_name} \
+    --num-workers 4 \
+    --reso 512 \
+    # --save-stats \
+
+done
\ No newline at end of file
diff --git a/evaluations/fidkid-pytorch/musiq_score_gso.py b/evaluations/fidkid-pytorch/musiq_score_gso.py
new file mode 100644
index 0000000000000000000000000000000000000000..a83405e9024543d8efafedd130d04c80c6bce592
--- /dev/null
+++ b/evaluations/fidkid-pytorch/musiq_score_gso.py
@@ -0,0 +1,555 @@
+"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
+
+The FID metric calculates the distance between two distributions of images.
+Typically, we have summary statistics (mean & covariance matrix) of one
+of these distributions, while the 2nd distribution is given by a GAN.
+
+When run as a stand-alone program, it compares the distribution of
+images that are stored as PNG/JPEG at a specified location with a
+distribution given by summary statistics (in pickle format).
+
+The FID is calculated by assuming that X_1 and X_2 are the activations of
+the pool_3 layer of the inception net for generated samples and real world
+samples respectively.
+
+See --help to see further details.
+
+Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
+of Tensorflow
+
+Copyright 2018 Institute of Bioinformatics, JKU Linz
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import ipdb
+import os
+from pathlib import Path
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+import pyiqa
+
+from pdb import set_trace as st
+
+import json
+import numpy as np
+import torch
+import torchvision.transforms as TF
+from PIL import Image
+from scipy import linalg
+from torch.nn.functional import adaptive_avg_pool2d
+import cv2
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If tqdm is not available, provide a mock version of it
+    def tqdm(x):
+        return x
+
+from pytorch_fid.inception import InceptionV3
+
+parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+parser.add_argument('--batch-size', type=int, default=100,
+                    help='Batch size to use')
+parser.add_argument('--reso', type=int, default=128,
+                    help='Batch size to use')
+parser.add_argument('--num-workers', type=int, default=8,
+                    help=('Number of processes to use for data loading. '
+                          'Defaults to `min(8, num_cpus)`'))
+parser.add_argument('--device', type=str, default=None,
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+parser.add_argument('--dataset', type=str, default='omni',
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+parser.add_argument('--dims', type=int, default=2048,
+                    choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
+                    help=('Dimensionality of Inception features to use. '
+                          'By default, uses pool3 features'))
+parser.add_argument('--save-stats', action='store_true',
+                    help=('Generate an npz archive from a directory of samples. '
+                          'The first path is used as input and the second as output.'))
+parser.add_argument('path', type=str, nargs=2,
+                    help=('Paths to the generated images or '
+                          'to .npz statistic files'))
+
+IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm',
+                    'tif', 'tiff', 'webp'}
+
+
+class ImagePathDataset(torch.utils.data.Dataset):
+    def __init__(self, files, reso,transforms=None):
+        self.files = files
+        self.transforms = transforms
+        self.reso=reso
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, i):
+        path = self.files[i]
+        #ipdb.set_trace()
+        try:
+            img=cv2.imread(path)
+            #if img.mean(-1)>254.9:
+            #img[np.where(img.mean(-1)>254.9)]=0
+            img=cv2.resize(img,(self.reso,self.reso),interpolation=cv2.INTER_CUBIC)
+            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+        except:
+            img=cv2.imread(self.files[0])
+            #if img.mean(-1)>254.9:
+            #img[np.where(img.mean(-1)>254.9)]=0
+            img=cv2.resize(img,(self.reso,self.reso),interpolation=cv2.INTER_CUBIC)
+            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+            print(path)
+        
+
+        #img = Image.open(path).convert('RGB')
+        if self.transforms is not None:
+            img = self.transforms(img)
+        #ipdb.set_trace()
+        
+        return img
+
+
+def get_activations(files, model, batch_size=50, dims=2048, device='cpu',
+                    num_workers=16,reso=128):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : Batch size of images for the model to process at once.
+                     Make sure that the number of samples is a multiple of
+                     the batch size, otherwise some samples are ignored. This
+                     behavior is retained to match the original FID score
+                     implementation.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- A numpy array of dimension (num images, dims) that contains the
+       activations of the given tensor when feeding inception with the
+       query tensor.
+    """
+    model.eval()
+
+    if batch_size > len(files):
+        print(('Warning: batch size is bigger than the data size. '
+               'Setting batch size to data size'))
+        batch_size = len(files)
+
+    dataset = ImagePathDataset(files, reso,transforms=TF.ToTensor())
+    dataloader = torch.utils.data.DataLoader(dataset,
+                                             batch_size=batch_size,
+                                             shuffle=False,
+                                             drop_last=False,
+                                             num_workers=num_workers)
+
+    pred_arr = np.empty((len(files), dims))
+
+    start_idx = 0
+
+    for batch in tqdm(dataloader):
+        batch = batch.to(device)
+        #ipdb.set_trace()
+
+        with torch.no_grad():
+            pred = model(batch)[0]
+
+        # If model output is not scalar, apply global spatial average pooling.
+        # This happens if you choose a dimensionality not equal 2048.
+        if pred.size(2) != 1 or pred.size(3) != 1:
+            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
+
+        #ipdb.set_trace()
+
+        pred = pred.squeeze(3).squeeze(2).cpu().numpy()
+
+        pred_arr[start_idx:start_idx + pred.shape[0]] = pred
+
+        start_idx = start_idx + pred.shape[0]
+
+    return pred_arr
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+
+    Stable version by Dougal J. Sutherland.
+
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representative data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representative data set.
+
+    Returns:
+    --   : The Frechet Distance.
+    """
+    #ipdb.set_trace()
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1)
+            + np.trace(sigma2) - 2 * tr_covmean)
+
+
+def calculate_activation_statistics(files, model, batch_size=50, dims=2048,
+                                    device='cpu', num_workers=1,reso=128):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : The images numpy array is split into batches with
+                     batch size batch_size. A reasonable batch size
+                     depends on the hardware.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    act = get_activations(files, model, batch_size, dims, device, num_workers,reso=reso)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def compute_statistics_of_path(path, model, batch_size, dims, device,
+                               num_workers=1,reso=512,dataset='gso'):
+    basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-withtop/gso_gt"
+
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid-withtop/gso_gt"
+    # basepath="/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-/metrics/fid-withtop/gso_gt"
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600][:]
+    # all_objs = all_objs[100:600]
+    # all_objs = all_objs[:500]
+
+
+    # if 'shapenet' in dataset:
+    # if 'shapenet' in dataset:
+
+    try:
+        try:
+            
+            m=np.load(os.path.join(basepath,path.split('/')[-1]+str(reso)+'mean.npy'))
+            s=np.load(os.path.join(basepath,path.split('/')[-1]+str(reso)+'std.npy'))
+            print('loading_dataset',dataset)
+        except:
+            files=[]
+            # ! load instances for I23D inference
+            # for obj_folder in tqdm(sorted(os.listdir(path))):
+            #     for idx in range(0,25):
+            #         img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+            #         files.append(img_name)
+                    
+            # ! free3d rendering
+            # for obj_folder in tqdm(sorted(os.listdir(path))):
+            #     for idx in range(0,25):
+            #         # img_name = os.path.join(path, obj_folder, 'rgba', f'{idx:03}.png')
+            #         img_name = os.path.join(path, obj_folder, 'render_mvs_25', 'model', f'{idx:03}.png')
+            #         files.append(img_name)
+
+            # ! objv loading
+            for obj_folder in tqdm(all_objs):
+                obj_folder = obj_folder[:-2] # to load 3 chunks
+                for batch in range(1,4):
+                    for idx in range(8):
+                        files.append(os.path.join(path, obj_folder, str(batch), f'{idx}.jpg'))
+
+            # for name in os.listdir(path):
+            #     #ipdb.set_trace()
+            #     # if name not in false1: #and name not in false2 and name not in false3:
+            #     if name in false1: #and name not in false2 and name not in false3:
+            #         img=os.path.join(path,name,'rgb')
+            #         #ipdb.set_trace()
+            #         files = files+sorted([os.path.join(img, idd) for idd in os.listdir(img) if idd.endswith('.png')])
+
+                if len(files) > 50000:
+                    files = files[:50000]
+                    break
+
+            #files=files[:5]
+            m, s = calculate_activation_statistics(files, model, batch_size,
+                                                    dims, device, num_workers,reso=reso)
+            path = Path(path)
+            # ipdb.set_trace()
+            np.save(os.path.join(basepath,path.name+str(reso)+'mean'), m)
+            np.save(os.path.join(basepath,path.name+str(reso)+'std'), s)
+    except Exception as e:
+        print(f'{dataset} failed, ', e)
+
+
+    return m, s
+
+
+def compute_statistics_of_path_new(path, model, batch_size, dims, device,
+                               num_workers=1,reso=128,dataset='omni'):
+    # basepath='/mnt/lustre/yslan/logs/nips23/LSGM/cldm/cmetric/shapenet-outs/fid'+str(reso)+'test'+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/metrics/fid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-subset/'+str(reso)+dataset
+
+    basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid/'+str(reso)+dataset
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/metrics/fid-withtop/'+str(reso)+dataset
+
+    # basepath='/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-free3d/metrics/fid/'+str(reso)+dataset
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    os.makedirs(os.path.join(basepath), exist_ok=True)
+    sample_name=path.split('/')[-1]
+
+    try:
+        try:
+            # ipdb.set_trace()
+            m=np.load(os.path.join(basepath,sample_name+str(reso)+'mean.npy'))
+            s=np.load(os.path.join(basepath,sample_name+str(reso)+'std.npy'))
+            print('loading_sample')
+        except:
+            files=[]
+
+            # for name in os.listdir(path):
+            #     img=os.path.join(path,name)
+            #     files.append(img) # ! directly append
+
+            # for loading gso-like folder
+            # st()
+            # for obj_folder in sorted(os.listdir(path)):
+
+            #     if obj_folder == 'runs':
+            #         continue
+
+            #     if not os.path.isdir(os.path.join(path, obj_folder)):
+            #         continue
+
+            #     for idx in [0]:
+            #         for i in range(24):
+            #             if 'GA' in path:
+            #                 img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+            #             else:
+            #                 img=os.path.join(path,obj_folder, str(idx),f'{i}.jpg')
+            #                 # ipdb.set_trace()
+            #             files.append(img) 
+
+            for obj_folder in tqdm(all_objs):
+                obj_folder = '/'.join(obj_folder.split('/')[1:])
+                for idx in range(24):
+                    # files.append(os.path.join(path, obj_folder, f'{idx}.jpg'))
+                    if 'Lara' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', f'{idx}.jpg'))
+                    elif 'GA' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'sample-0-{idx}.jpg'))
+                    elif 'scale3d' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '1', f'{idx}.png'))
+                    elif 'LRM' in path:
+                        files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))
+                    else:
+                        files.append(os.path.join(path, obj_folder, '0', f'{idx}.jpg'))
+
+
+            files=files[:50000]
+            m, s = calculate_activation_statistics(files, model, batch_size,
+                                                    dims, device, num_workers,reso=reso)
+            path = Path(path)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'mean'), m)
+            np.save(os.path.join(basepath,sample_name+str(reso)+'std'), s)
+    except Exception as e:
+        print('error sample image', e)
+
+    #ipdb.set_trace()
+
+    return m, s
+
+musiq_metric = pyiqa.create_metric('musiq')
+
+def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1,reso=128,dataset='omni'):
+    """Calculates the FID of two paths"""
+    # for p in paths:
+    #     if not os.path.exists(p):
+    #         raise RuntimeError('Invalid path: %s' % p)
+
+    # block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    # model = InceptionV3([block_idx]).to(device)
+
+    # fid_metric = pyiqa.create_metric('fid')
+    # fid_value = fid_metric(paths[0], paths[1])
+
+    all_musiq = []
+
+
+    path = paths[1]
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    sample_name=path.split('/')[-1]
+
+    files=[]
+
+    for obj_folder in tqdm(all_objs):
+        obj_folder = '/'.join(obj_folder.split('/')[1:])
+        for idx in range(24):
+            # files.append(os.path.join(path, obj_folder, f'{idx}.jpg'))
+            if 'Lara' in path:
+                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', f'{idx}.jpg'))
+            elif 'GA' in path:
+                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'sample-0-{idx}.jpg'))
+            elif 'LRM' in path:
+                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg'))
+            elif 'scale3d' in path:
+                files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '1', f'{idx}.png'))
+            else:
+                files.append(os.path.join(path, obj_folder, '0', f'{idx}.jpg'))
+
+    # for file in tqdm(os.listdir(str(paths[1]))[:]):
+    for file in tqdm(files):
+        if os.path.exists(file):
+        # musiq_value = musiq_metric(os.path.join(paths[1], file))
+            musiq_value = musiq_metric(os.path.join(file))
+            all_musiq.append(musiq_value)
+
+    musiq_value = sum(all_musiq) / len(all_musiq)
+
+    
+
+    # m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, # ! GT data
+    #                                     dims, device, num_workers,reso=reso,dataset=dataset)
+    # # ipdb.set_trace()
+    # m2, s2 = compute_statistics_of_path_new(paths[1], model, batch_size, # ! generated data
+    #                                     dims, device, num_workers,reso=reso,dataset=dataset)
+    # fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+
+    # return fid_value
+    return musiq_value
+
+
+def save_fid_stats(paths, batch_size, device, dims, num_workers=1):
+    """Calculates the FID of two paths"""
+    # if not os.path.exists(paths[0]):
+    #     raise RuntimeError('Invalid path: %s' % paths[0])
+
+    # if os.path.exists(paths[1]):
+    #     raise RuntimeError('Existing output file: %s' % paths[1])
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    print(f"Saving statistics for {paths[0]}")
+
+    m1, s1 = compute_statistics_of_path(paths[0], model, batch_size,
+                                        dims, device, num_workers)
+
+    np.savez_compressed(paths[1], mu=m1, sigma=s1)
+
+
+def main():
+    args = parser.parse_args()
+
+    if args.device is None:
+        device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
+    else:
+        device = torch.device(args.device)
+
+    if args.num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = args.num_workers
+
+    if args.save_stats:
+        save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers)
+        return
+    #ipdb.set_trace()
+    fid_value = calculate_fid_given_paths(args.path,
+                                          args.batch_size,
+                                          device,
+                                          args.dims,
+                                          num_workers,args.reso,args.dataset)
+    print(f'{args.dataset} FID: ', fid_value)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/evaluations/fidkid-pytorch/sample_pcd4096.sh b/evaluations/fidkid-pytorch/sample_pcd4096.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ee7d810fba743eea3244798c1af008e09c111c87
--- /dev/null
+++ b/evaluations/fidkid-pytorch/sample_pcd4096.sh
@@ -0,0 +1,98 @@
+set -x
+
+# n_proc=4
+# n_points=4096
+focal=525
+
+# pre-requisites: kaolin and nvdiffrast
+# pip install kaolin==0.16.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.3.0_cu118.html
+
+n_proc=1
+# n_points=4096
+# n_points=35000 # will fps later
+n_points=16384 # will fps later
+# focal=24 # as in blender config 
+
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/02958343/fffb1660a38af30ba4cf3601fb6b2442/models/
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/02958343/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd/car
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/03001627/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd-full/chair
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/02691156/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd/plane
+
+# shape_root=/mnt/lustre/share/fzhong/shapenet/ShapeNetCore.v2/03001627/
+# save_root=/mnt/lustre/yslan/3D_Dataset/shapenet/pcd-full/chair
+
+# ! render gt
+# shape_root=/mnt/sfs-common/yslan/Dataset/GSO/gso-unzip/gso-unzip
+
+# save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics/gso
+
+# ! use FPS later:
+# splatter-img LGM
+
+# for method_name in ln3diff One-2-3-45 OpenLRM shape-e Lara CRM splatter-img
+# for method_name in ln3diff One-2-3-45 OpenLRM
+# for method_name in shape-e Lara CRM
+# for method_name in gso
+
+# for method_name in CRM splatter-img
+
+
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh-ditxlPCD
+
+# '''
+# for method_name in GA/stage-2/dino_img/ditl-fromditlPCD-fixPose-tomesh 
+
+# do
+
+# shape_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/${method_name}
+# # save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics/${method_name}
+
+# save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir/3D-metrics-fps/${method_name}
+
+# python sample_surface_gso.py \
+#     --shape_root ${shape_root} \
+#     --save_root ${save_root} \
+#     --n_proc ${n_proc} \
+#     --n_points ${n_points} \
+#     --image_height 512 \
+#     --image_width 512 \
+#     --focal_length_x ${focal} \
+#     --focal_length_y ${focal} \
+#     --principal_point_x 256 \
+#     --principal_point_y 256 \
+
+# done
+# '''
+
+
+# for method_name in Lara CRM/Animals ln3diff-lite/Animals
+
+# for method_name in Lara CRM/Animals ln3diff-lite/Animals
+# for method_name in OpenLRM/Animals One-2-3-45/Animals shape-e/Animals 
+
+for method_name in scale3d/eval/eval_nerf/Animals scale3d/eval/eval_mesh/Animals
+
+do
+
+shape_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/${method_name}
+save_root=/mnt/sfs-common/yslan/Repo/3dgen/FID-KID-Outputdir-objv/3D-metrics-fps/${method_name}
+
+python sample_surface_objv.py \
+    --shape_root ${shape_root} \
+    --save_root ${save_root} \
+    --n_proc ${n_proc} \
+    --n_points ${n_points} \
+    --image_height 512 \
+    --image_width 512 \
+    --focal_length_x ${focal} \
+    --focal_length_y ${focal} \
+    --principal_point_x 256 \
+    --principal_point_y 256 \
+
+done
diff --git a/evaluations/fidkid-pytorch/sample_surface_gso.py b/evaluations/fidkid-pytorch/sample_surface_gso.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c07a541f6687bf1a07caa61e2c05313d7d99ecd
--- /dev/null
+++ b/evaluations/fidkid-pytorch/sample_surface_gso.py
@@ -0,0 +1,409 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import math
+import pytorch3d.ops
+import numpy as np
+import os
+import argparse
+import multiprocessing as mp
+from multiprocessing import Pool
+import trimesh
+import tqdm
+import torch
+import nvdiffrast.torch as dr
+import kaolin as kal
+import glob
+import ipdb
+
+parser = argparse.ArgumentParser(description='sample surface points from mesh')
+parser.add_argument(
+    '--n_proc', type=int, default=8,
+    help='Number of processes to run in parallel'
+         '(0 means sequential execution).')
+parser.add_argument(
+    '--n_points', type=int, default=4096,
+    help='Number of points to sample per model.')
+parser.add_argument(
+    '--n_views', type=int, default=100,
+    help='Number of views per model.')
+parser.add_argument(
+    '--image_height', type=int, default=640,
+    help='Depth image height.')
+parser.add_argument(
+    '--image_width', type=int, default=640,
+    help='Depth image width.')
+parser.add_argument(
+    '--focal_length_x', type=float, default=640,
+    help='Focal length in x direction.')
+parser.add_argument(
+    '--focal_length_y', type=float, default=640,
+    help='Focal length in y direction.')
+parser.add_argument(
+    '--principal_point_x', type=float, default=320,
+    help='Principal point location in x direction.')
+parser.add_argument(
+    '--principal_point_y', type=float, default=320,
+    help='Principal point location in y direction.')
+parser.add_argument("--shape_root", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel27_omni_ablation2/ddpm_5000/test', help="path to the save resules shapenet dataset")
+parser.add_argument("--save_root", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel27_omni_ablation2/ddpm_vis_ab2surface', help="path to the split shapenet dataset")
+
+options = parser.parse_args()
+
+# create array for inverse mapping
+coordspx2 = np.stack(np.nonzero(np.ones((options.image_height, options.image_width))), -1).astype(np.float32)
+coordspx2 = coordspx2[:, ::-1]
+fusion_intrisics = np.array(
+    [
+        [options.focal_length_x, 0, options.principal_point_x],
+        [0, options.focal_length_y, options.principal_point_y],
+        [0, 0, 1]
+    ])
+# glctx = dr.RasterizeGLContext() # EGL/egl.h: No such file or directory
+glctx = dr.RasterizeCudaContext()
+
+
+def CalcLinearZ(depth):
+    # depth = depth * 2 - 1
+    zFar = 100.0
+    zNear = 0.1
+    linear = zNear / (zFar - depth * (zFar - zNear)) * zFar
+    return linear
+
+
+def projection_cv_new(fx, fy, cx, cy, width, height, n=1.0, f=50.0):
+    return np.array(
+        [[-2 * fx / width, 0.0, (width - 2 * cx) / width, 0.0],
+         [0.0, -2 * fy / height, (height - 2 * cy) / height, 0.0],
+         [0.0, 0.0, (-f - n) / (f - n), -2.0 * f * n / (f - n)],
+         [0.0, 0.0, -1.0, 0.0]])
+
+
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    return dr.interpolate(
+        attr.contiguous(), rast, attr_idx, rast_db=rast_db,
+        diff_attrs=None if rast_db is None else 'all')
+
+
+def render_nvdiffrast(v_pos, tris, T_bx4x4):
+    # T_bx4x4 - world to cam
+    proj = projection_cv_new(
+        fx=options.focal_length_x, fy=options.focal_length_y, cx=options.principal_point_x,
+        cy=options.principal_point_y,
+        width=options.image_width, height=options.image_height, n=0.1, f=100.0)
+
+    fix = torch.eye(4, dtype=torch.float32, device='cuda')
+    fix[2, 2] = -1
+    fix[1, 1] = -1
+    fix[0, 0] = -1
+    fix = fix.unsqueeze(0).repeat(T_bx4x4.shape[0], 1, 1)
+
+    proj = torch.tensor(proj, dtype=torch.float32, device='cuda').unsqueeze(0).repeat(T_bx4x4.shape[0], 1, 1)
+    T_world_cam_bx4x4 = torch.bmm(fix, T_bx4x4)
+    mvp = torch.bmm(proj, T_world_cam_bx4x4)
+    v_pos_clip = torch.matmul(
+        torch.nn.functional.pad(v_pos, pad=(0, 1), mode='constant', value=1.0),
+        torch.transpose(mvp, 1, 2))
+    rast, db = dr.rasterize(
+        glctx, torch.tensor(v_pos_clip, dtype=torch.float32, device='cuda'), tris.int(),
+        (options.image_height, options.image_width))
+
+    v_pos_cam = torch.matmul(
+        torch.nn.functional.pad(v_pos, pad=(0, 1), mode='constant', value=1.0),
+        torch.transpose(T_world_cam_bx4x4, 1, 2))
+    gb_pos_cam, _ = interpolate(v_pos_cam, rast, tris.int())
+    depth_maps = gb_pos_cam[..., 2].abs()
+    return depth_maps
+
+
+def as_mesh(scene_or_mesh):
+    """
+    Convert a possible scene to a mesh.
+
+    If conversion occurs, the returned mesh has only vertex and face data.
+    """
+    if isinstance(scene_or_mesh, trimesh.Scene):
+        if len(scene_or_mesh.geometry) == 0:
+            mesh = None  # empty scene
+        else:
+            # we lose texture information here
+            mesh = trimesh.util.concatenate(
+                tuple(
+                    trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
+                    for g in scene_or_mesh.geometry.values()))
+    else:
+        assert (isinstance(scene_or_mesh, trimesh.Trimesh))
+        mesh = scene_or_mesh
+    return mesh
+
+
+def render(mesh_v, mesh_f, Rs):
+    """
+    Render the given mesh using the generated views.
+
+    :param base_mesh: mesh to render
+    :type base_mesh: mesh.Mesh
+    :param Rs: rotation matrices
+    :type Rs: [numpy.ndarray]
+    :return: depth maps
+    :rtype: numpy.ndarray
+    """
+    T_bx4x4 = torch.zeros((options.n_views, 4, 4), dtype=torch.float32, device='cuda')
+    T_bx4x4[:, 3, 3] = 1
+    T_bx4x4[:, 2, 3] = 1
+    T_bx4x4[:, :3, :3] = torch.tensor(Rs, dtype=torch.float32, device='cuda')
+    depthmaps = render_nvdiffrast(
+        mesh_v,
+        mesh_f, T_bx4x4)
+    return depthmaps
+
+
+def get_points():
+    """
+    :param n_points: number of points
+    :type n_points: int
+    :return: list of points
+    :rtype: numpy.ndarray
+    """
+
+    rnd = 1.
+    points = []
+    offset = 2. / options.n_views
+    increment = math.pi * (3. - math.sqrt(5.))
+
+    for i in range(options.n_views):
+        y = ((i * offset) - 1) + (offset / 2)
+        r = math.sqrt(1 - pow(y, 2))
+
+        phi = ((i + rnd) % options.n_views) * increment
+
+        x = math.cos(phi) * r
+        z = math.sin(phi) * r
+
+        points.append([x, y, z])
+    return np.array(points)
+
+
+def get_views(semi_sphere=False):
+    """
+    Generate a set of views to generate depth maps from.
+
+    :param n_views: number of views per axis
+    :type n_views: int
+    :return: rotation matrices
+    :rtype: [numpy.ndarray]
+    """
+
+    Rs = []
+    points = get_points()
+    if semi_sphere:
+        points[:, 2] = -np.abs(points[:, 2]) - 0.1
+
+    for i in range(points.shape[0]):
+        longitude = - math.atan2(points[i, 0], points[i, 1])
+        latitude = math.atan2(points[i, 2], math.sqrt(points[i, 0] ** 2 + points[i, 1] ** 2))
+
+        R_x = np.array(
+            [[1, 0, 0],
+             [0, math.cos(latitude), -math.sin(latitude)],
+             [0, math.sin(latitude), math.cos(latitude)]])
+        R_y = np.array(
+            [[math.cos(longitude), 0, math.sin(longitude)],
+             [0, 1, 0],
+             [-math.sin(longitude), 0, math.cos(longitude)]])
+        R = R_x @ R_y
+        Rs.append(R)
+
+    return Rs
+
+
+def fusion(depthmaps, Rs):
+    """
+    Fuse the rendered depth maps.
+
+    :param depthmaps: depth maps
+    :type depthmaps: numpy.ndarray
+    :param Rs: rotation matrices corresponding to views
+    :type Rs: [numpy.ndarray]
+    :return: (T)SDF
+    :rtype: numpy.ndarray
+    """
+
+    # sample points inside mask
+    sample_per_view = options.n_points // options.n_views
+    sample_bxn = torch.zeros((options.n_views, sample_per_view), device='cuda', dtype=torch.long)
+    for i in range(len(Rs)):
+        mask = depthmaps[i] > 0
+        valid_idx = torch.nonzero(mask.reshape(-1)).squeeze(-1)
+        idx = list(range(valid_idx.shape[0]))
+        np.random.shuffle(idx)
+        idx = idx[:sample_per_view]
+        sample_bxn[i] = torch.tensor(valid_idx[idx])
+
+    depthmaps = torch.gather(depthmaps.reshape(options.n_views, -1), 1, sample_bxn)
+
+    inv_Ks_bx3x3 = torch.tensor(np.linalg.inv(fusion_intrisics), dtype=torch.float32, device='cuda').unsqueeze(
+        0).repeat(options.n_views, 1, 1)
+    T_bx4x4 = torch.zeros((options.n_views, 4, 4), dtype=torch.float32, device='cuda')
+    T_bx4x4[:, 3, 3] = 1
+    T_bx4x4[:, 2, 3] = 1
+    T_bx4x4[:, :3, :3] = torch.tensor(Rs, dtype=torch.float32, device='cuda')
+    inv_T_bx4x4 = torch.inverse(T_bx4x4)
+
+    tf_coords_bxpx2 = torch.tensor(coordspx2.copy(), dtype=torch.float32, device='cuda').unsqueeze(0).repeat(
+        options.n_views, 1, 1)
+    tf_coords_bxpx2 = torch.gather(tf_coords_bxpx2, 1, sample_bxn.unsqueeze(-1).repeat(1, 1, 2))
+
+    tf_coords_bxpx3 = torch.cat([tf_coords_bxpx2, torch.ones_like(tf_coords_bxpx2[..., :1])], -1)
+    tf_coords_bxpx3 *= depthmaps.reshape(options.n_views, -1, 1)
+    tf_cam_bxpx3 = torch.bmm(inv_Ks_bx3x3, tf_coords_bxpx3.transpose(1, 2)).transpose(1, 2)
+    tf_cam_bxpx4 = torch.cat([tf_cam_bxpx3, torch.ones_like(tf_cam_bxpx3[..., :1])], -1)
+    tf_world_bxpx3 = torch.bmm(inv_T_bx4x4, tf_cam_bxpx4.transpose(1, 2)).transpose(1, 2)[..., :3]
+
+    return tf_world_bxpx3.reshape(-1, 3)
+
+
+def normalize(vertices, faces, normalized_scale=0.9, rotate_x=False, center_xyz=False):
+    vertices = vertices.cuda()
+
+    if center_xyz: # some mesh's center is not origin
+        vertices = vertices - vertices.mean(0, keepdim=True)
+
+    if rotate_x: # rotate along x axis for 90 degrees to match the two coordiantes
+        rot_mat = torch.eye(n=3, device='cuda')
+        theta = np.pi / 90  # degree
+        rot_mat[1,1] = np.cos(theta)
+        rot_mat[2,2] = np.cos(theta)
+        rot_mat[1,2] =-np.sin(theta)
+        rot_mat[2,1] = np.sin(theta)
+        # ipdb.set_trace()
+        vertices = rot_mat @ vertices.transpose(0,1)
+        vertices = vertices.transpose(0,1)
+
+    scale = (vertices.max(dim=0)[0] - vertices.min(dim=0)[0]).max()
+    mesh_v1 = vertices / scale * normalized_scale
+    mesh_f1 = faces.long().cuda()
+    return mesh_v1, mesh_f1
+
+
+
+def sample_surface_pts(path):
+    # ipdb.set_trace()
+    try:
+        mesh_path, output_pth, debug = path
+        # mesh = kal.io.obj.import_mesh(mesh_path)
+        # ipdb.set_trace()
+        mesh = trimesh.load(mesh_path) # fail to load ply?
+        #ipdb.set_trace()
+        if mesh.vertices.shape[0] == 0:
+            return
+
+        mesh_v = torch.Tensor(mesh.vertices)
+        # mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=0.9, rotate_x=True)
+        # mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=1.0, rotate_x=True, center_xyz=True)
+        mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=1.0, rotate_x=False, center_xyz=True)
+
+        # generate camera matrices
+        # Rs = get_views()
+        # Rs = get_views(semi_sphere=True)
+        Rs = get_views(semi_sphere=False)
+        # get depth images
+        depths = render(mesh_v, mesh_f, Rs)
+        # project to world space
+        try:
+            pcd = fusion(depths, Rs)
+        except:
+            return
+
+        # fps again
+
+        pcd, fps_idx = pytorch3d.ops.sample_farthest_points(
+            # torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4096,
+            # torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4000,
+            pcd.unsqueeze(0).cuda(), K=4000,
+            random_start_point=True)  # B self.latent_num
+        pcd = pcd[0]
+
+        pcd = pcd.cpu().numpy()
+
+        #np.savez(output_pth, pcd=pcd)
+        #ipdb.set_trace()
+        #if debug:
+        pcd = trimesh.points.PointCloud(pcd)
+        pcd.export(output_pth.replace('.npz', '.obj'))
+    except Exception as e:
+        # print('error')
+        print(e, flush=True)
+
+
+if __name__ == '__main__':
+    mp.set_start_method('spawn')
+
+    shapenet_root = options.shape_root
+    save_root = options.save_root
+
+    debug = True
+    #model_list = sorted(os.listdir(shapenet_root))[:7500]
+
+    # model_list=glob.glob(os.path.join(shapenet_root, '*.obj'))
+    # os.makedirs(save_root, exist_ok=True)
+
+    # cmds = [(os.path.join(shapenet_root, id.split('/')[-1]), os.path.join(save_root, id.split('/')[-1]), debug) for id in model_list]
+
+    # cmds = [(os.path.join(shapenet_root, id.split('/')[-1]), os.path.join(save_root, 'pcd_4096.ply'), debug) for id in model_list]
+
+    # cmds += [(os.path.join(shapenet_root, id.split('/')[-1]), os.path.join(save_root, 'test.obj'), debug) for id in model_list]
+
+    cmds = [] 
+
+    # for instance_name in os.listdir(shapenet_root)[:]:
+    #     cmds += [(os.path.join(shapenet_root, instance_name), os.path.join(save_root, f'{instance_name.split(".")[0]}_pcd_4096.ply'), debug)]
+
+    # ! for gt
+    # for obj_folder in sorted(os.listdir(shapenet_root)):
+    #     cmds += [(os.path.join(shapenet_root, obj_folder, 'meshes/model.obj'), os.path.join(save_root, f'{obj_folder}_pcd_4096.ply'), debug)]
+
+    # ! for baseline samples
+
+    os.makedirs(save_root, exist_ok=True)
+
+    for obj_folder in sorted(os.listdir(shapenet_root)):
+        if not os.path.isdir(os.path.join(shapenet_root, obj_folder)):
+            continue
+        for idx in [0]:
+            ipdb.set_trace()
+
+            # for i in range(10):
+            #     img=os.path.join(shapenet_root,obj_folder, str(idx),f'{i}.jpg')
+                # img=os.path.join(path,obj_folder, str(idx),f'sample-0-{i}.jpg')
+                # files.append(img) 
+
+            if 'CRM' in shapenet_root:
+                # ipdb.set_trace()
+                mesh_path = glob.glob(os.path.join(shapenet_root, obj_folder, f'{idx}', '*.obj'))[0]
+            elif 'Lara' in shapenet_root:
+                mesh_path = glob.glob(os.path.join(shapenet_root, '/'.join(obj_folder.split('/')[:-1]), '0.jpg', '*.obj'))[0]
+
+            elif 'LRM' in shapenet_root:
+                mesh_path = glob.glob(os.path.join(shapenet_root, '/'.join(obj_folder.split('/')[:-1]), '0', f'{idx}.jpg', '*.obj'))[0]
+            else:
+                if os.path.exists((os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.obj'))):
+                    mesh_path = os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.obj')
+                else:
+                    mesh_path = os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.ply')
+
+            cmds += [(mesh_path, os.path.join(save_root, f'{obj_folder}_pcd_4096.ply'), debug)]
+
+
+    if options.n_proc == 0:
+        for filepath in tqdm.tqdm(cmds):
+            sample_surface_pts(filepath)
+    else:
+        with Pool(options.n_proc) as p:
+            list(tqdm.tqdm(p.imap(sample_surface_pts, cmds), total=len(cmds)))
diff --git a/evaluations/fidkid-pytorch/sample_surface_objv.py b/evaluations/fidkid-pytorch/sample_surface_objv.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb61b87a8200912cf2955e834bea89dea244b902
--- /dev/null
+++ b/evaluations/fidkid-pytorch/sample_surface_objv.py
@@ -0,0 +1,412 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+from pdb import set_trace as st
+import json
+import math
+import pytorch3d.ops
+import numpy as np
+import os
+import argparse
+import multiprocessing as mp
+from multiprocessing import Pool
+import trimesh
+import tqdm
+import torch
+import nvdiffrast.torch as dr
+import kaolin as kal
+import glob
+import ipdb
+
+parser = argparse.ArgumentParser(description='sample surface points from mesh')
+parser.add_argument(
+    '--n_proc', type=int, default=8,
+    help='Number of processes to run in parallel'
+         '(0 means sequential execution).')
+parser.add_argument(
+    '--n_points', type=int, default=4096,
+    help='Number of points to sample per model.')
+parser.add_argument(
+    '--n_views', type=int, default=100,
+    help='Number of views per model.')
+parser.add_argument(
+    '--image_height', type=int, default=640,
+    help='Depth image height.')
+parser.add_argument(
+    '--image_width', type=int, default=640,
+    help='Depth image width.')
+parser.add_argument(
+    '--focal_length_x', type=float, default=640,
+    help='Focal length in x direction.')
+parser.add_argument(
+    '--focal_length_y', type=float, default=640,
+    help='Focal length in y direction.')
+parser.add_argument(
+    '--principal_point_x', type=float, default=320,
+    help='Principal point location in x direction.')
+parser.add_argument(
+    '--principal_point_y', type=float, default=320,
+    help='Principal point location in y direction.')
+parser.add_argument("--shape_root", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel27_omni_ablation2/ddpm_5000/test', help="path to the save resules shapenet dataset")
+parser.add_argument("--save_root", type=str, default='/mnt/petrelfs/caoziang/3D_generation/Checkpoint_all/diffusion_shapenet_testmodel27_omni_ablation2/ddpm_vis_ab2surface', help="path to the split shapenet dataset")
+
+options = parser.parse_args()
+
+# create array for inverse mapping
+coordspx2 = np.stack(np.nonzero(np.ones((options.image_height, options.image_width))), -1).astype(np.float32)
+coordspx2 = coordspx2[:, ::-1]
+fusion_intrisics = np.array(
+    [
+        [options.focal_length_x, 0, options.principal_point_x],
+        [0, options.focal_length_y, options.principal_point_y],
+        [0, 0, 1]
+    ])
+# glctx = dr.RasterizeGLContext() # EGL/egl.h: No such file or directory
+glctx = dr.RasterizeCudaContext()
+
+
+def CalcLinearZ(depth):
+    # depth = depth * 2 - 1
+    zFar = 100.0
+    zNear = 0.1
+    linear = zNear / (zFar - depth * (zFar - zNear)) * zFar
+    return linear
+
+
+def projection_cv_new(fx, fy, cx, cy, width, height, n=1.0, f=50.0):
+    return np.array(
+        [[-2 * fx / width, 0.0, (width - 2 * cx) / width, 0.0],
+         [0.0, -2 * fy / height, (height - 2 * cy) / height, 0.0],
+         [0.0, 0.0, (-f - n) / (f - n), -2.0 * f * n / (f - n)],
+         [0.0, 0.0, -1.0, 0.0]])
+
+
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    return dr.interpolate(
+        attr.contiguous(), rast, attr_idx, rast_db=rast_db,
+        diff_attrs=None if rast_db is None else 'all')
+
+
+def render_nvdiffrast(v_pos, tris, T_bx4x4):
+    # T_bx4x4 - world to cam
+    proj = projection_cv_new(
+        fx=options.focal_length_x, fy=options.focal_length_y, cx=options.principal_point_x,
+        cy=options.principal_point_y,
+        width=options.image_width, height=options.image_height, n=0.1, f=100.0)
+
+    fix = torch.eye(4, dtype=torch.float32, device='cuda')
+    fix[2, 2] = -1
+    fix[1, 1] = -1
+    fix[0, 0] = -1
+    fix = fix.unsqueeze(0).repeat(T_bx4x4.shape[0], 1, 1)
+
+    proj = torch.tensor(proj, dtype=torch.float32, device='cuda').unsqueeze(0).repeat(T_bx4x4.shape[0], 1, 1)
+    T_world_cam_bx4x4 = torch.bmm(fix, T_bx4x4)
+    mvp = torch.bmm(proj, T_world_cam_bx4x4)
+    v_pos_clip = torch.matmul(
+        torch.nn.functional.pad(v_pos, pad=(0, 1), mode='constant', value=1.0),
+        torch.transpose(mvp, 1, 2))
+    rast, db = dr.rasterize(
+        glctx, torch.tensor(v_pos_clip, dtype=torch.float32, device='cuda'), tris.int(),
+        (options.image_height, options.image_width))
+
+    v_pos_cam = torch.matmul(
+        torch.nn.functional.pad(v_pos, pad=(0, 1), mode='constant', value=1.0),
+        torch.transpose(T_world_cam_bx4x4, 1, 2))
+    gb_pos_cam, _ = interpolate(v_pos_cam, rast, tris.int())
+    depth_maps = gb_pos_cam[..., 2].abs()
+    return depth_maps
+
+
+def as_mesh(scene_or_mesh):
+    """
+    Convert a possible scene to a mesh.
+
+    If conversion occurs, the returned mesh has only vertex and face data.
+    """
+    if isinstance(scene_or_mesh, trimesh.Scene):
+        if len(scene_or_mesh.geometry) == 0:
+            mesh = None  # empty scene
+        else:
+            # we lose texture information here
+            mesh = trimesh.util.concatenate(
+                tuple(
+                    trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
+                    for g in scene_or_mesh.geometry.values()))
+    else:
+        assert (isinstance(scene_or_mesh, trimesh.Trimesh))
+        mesh = scene_or_mesh
+    return mesh
+
+
+def render(mesh_v, mesh_f, Rs):
+    """
+    Render the given mesh using the generated views.
+
+    :param base_mesh: mesh to render
+    :type base_mesh: mesh.Mesh
+    :param Rs: rotation matrices
+    :type Rs: [numpy.ndarray]
+    :return: depth maps
+    :rtype: numpy.ndarray
+    """
+    T_bx4x4 = torch.zeros((options.n_views, 4, 4), dtype=torch.float32, device='cuda')
+    T_bx4x4[:, 3, 3] = 1
+    T_bx4x4[:, 2, 3] = 1
+    T_bx4x4[:, :3, :3] = torch.tensor(Rs, dtype=torch.float32, device='cuda')
+    depthmaps = render_nvdiffrast(
+        mesh_v,
+        mesh_f, T_bx4x4)
+    return depthmaps
+
+
+def get_points():
+    """
+    :param n_points: number of points
+    :type n_points: int
+    :return: list of points
+    :rtype: numpy.ndarray
+    """
+
+    rnd = 1.
+    points = []
+    offset = 2. / options.n_views
+    increment = math.pi * (3. - math.sqrt(5.))
+
+    for i in range(options.n_views):
+        y = ((i * offset) - 1) + (offset / 2)
+        r = math.sqrt(1 - pow(y, 2))
+
+        phi = ((i + rnd) % options.n_views) * increment
+
+        x = math.cos(phi) * r
+        z = math.sin(phi) * r
+
+        points.append([x, y, z])
+    return np.array(points)
+
+
+def get_views(semi_sphere=False):
+    """
+    Generate a set of views to generate depth maps from.
+
+    :param n_views: number of views per axis
+    :type n_views: int
+    :return: rotation matrices
+    :rtype: [numpy.ndarray]
+    """
+
+    Rs = []
+    points = get_points()
+    if semi_sphere:
+        points[:, 2] = -np.abs(points[:, 2]) - 0.1
+
+    for i in range(points.shape[0]):
+        longitude = - math.atan2(points[i, 0], points[i, 1])
+        latitude = math.atan2(points[i, 2], math.sqrt(points[i, 0] ** 2 + points[i, 1] ** 2))
+
+        R_x = np.array(
+            [[1, 0, 0],
+             [0, math.cos(latitude), -math.sin(latitude)],
+             [0, math.sin(latitude), math.cos(latitude)]])
+        R_y = np.array(
+            [[math.cos(longitude), 0, math.sin(longitude)],
+             [0, 1, 0],
+             [-math.sin(longitude), 0, math.cos(longitude)]])
+        R = R_x @ R_y
+        Rs.append(R)
+
+    return Rs
+
+
+def fusion(depthmaps, Rs):
+    """
+    Fuse the rendered depth maps.
+
+    :param depthmaps: depth maps
+    :type depthmaps: numpy.ndarray
+    :param Rs: rotation matrices corresponding to views
+    :type Rs: [numpy.ndarray]
+    :return: (T)SDF
+    :rtype: numpy.ndarray
+    """
+
+    # sample points inside mask
+    sample_per_view = options.n_points // options.n_views
+    sample_bxn = torch.zeros((options.n_views, sample_per_view), device='cuda', dtype=torch.long)
+    for i in range(len(Rs)):
+        mask = depthmaps[i] > 0
+        valid_idx = torch.nonzero(mask.reshape(-1)).squeeze(-1)
+        idx = list(range(valid_idx.shape[0]))
+        np.random.shuffle(idx)
+        idx = idx[:sample_per_view]
+        sample_bxn[i] = torch.tensor(valid_idx[idx])
+
+    depthmaps = torch.gather(depthmaps.reshape(options.n_views, -1), 1, sample_bxn)
+
+    inv_Ks_bx3x3 = torch.tensor(np.linalg.inv(fusion_intrisics), dtype=torch.float32, device='cuda').unsqueeze(
+        0).repeat(options.n_views, 1, 1)
+    T_bx4x4 = torch.zeros((options.n_views, 4, 4), dtype=torch.float32, device='cuda')
+    T_bx4x4[:, 3, 3] = 1
+    T_bx4x4[:, 2, 3] = 1
+    T_bx4x4[:, :3, :3] = torch.tensor(Rs, dtype=torch.float32, device='cuda')
+    inv_T_bx4x4 = torch.inverse(T_bx4x4)
+
+    tf_coords_bxpx2 = torch.tensor(coordspx2.copy(), dtype=torch.float32, device='cuda').unsqueeze(0).repeat(
+        options.n_views, 1, 1)
+    tf_coords_bxpx2 = torch.gather(tf_coords_bxpx2, 1, sample_bxn.unsqueeze(-1).repeat(1, 1, 2))
+
+    tf_coords_bxpx3 = torch.cat([tf_coords_bxpx2, torch.ones_like(tf_coords_bxpx2[..., :1])], -1)
+    tf_coords_bxpx3 *= depthmaps.reshape(options.n_views, -1, 1)
+    tf_cam_bxpx3 = torch.bmm(inv_Ks_bx3x3, tf_coords_bxpx3.transpose(1, 2)).transpose(1, 2)
+    tf_cam_bxpx4 = torch.cat([tf_cam_bxpx3, torch.ones_like(tf_cam_bxpx3[..., :1])], -1)
+    tf_world_bxpx3 = torch.bmm(inv_T_bx4x4, tf_cam_bxpx4.transpose(1, 2)).transpose(1, 2)[..., :3]
+
+    return tf_world_bxpx3.reshape(-1, 3)
+
+
+def normalize(vertices, faces, normalized_scale=0.9, rotate_x=False, center_xyz=False):
+    vertices = vertices.cuda()
+
+    if center_xyz: # some mesh's center is not origin
+        vertices = vertices - vertices.mean(0, keepdim=True)
+
+    if rotate_x: # rotate along x axis for 90 degrees to match the two coordiantes
+        rot_mat = torch.eye(n=3, device='cuda')
+        theta = np.pi / 90  # degree
+        rot_mat[1,1] = np.cos(theta)
+        rot_mat[2,2] = np.cos(theta)
+        rot_mat[1,2] =-np.sin(theta)
+        rot_mat[2,1] = np.sin(theta)
+        # ipdb.set_trace()
+        vertices = rot_mat @ vertices.transpose(0,1)
+        vertices = vertices.transpose(0,1)
+
+    scale = (vertices.max(dim=0)[0] - vertices.min(dim=0)[0]).max()
+    mesh_v1 = vertices / scale * normalized_scale
+    mesh_f1 = faces.long().cuda()
+    return mesh_v1, mesh_f1
+
+
+
+def sample_surface_pts(path):
+    # ipdb.set_trace()
+    try:
+        mesh_path, output_pth, debug = path
+        # mesh = kal.io.obj.import_mesh(mesh_path)
+        # ipdb.set_trace()
+        mesh = trimesh.load(mesh_path) # fail to load ply?
+        #ipdb.set_trace()
+        if mesh.vertices.shape[0] == 0:
+            return
+
+        mesh_v = torch.Tensor(mesh.vertices)
+        # mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=0.9, rotate_x=True)
+        # mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=1.0, rotate_x=True, center_xyz=True)
+        mesh_v, mesh_f = normalize(mesh_v, torch.Tensor(mesh.faces), normalized_scale=1.0, rotate_x=False, center_xyz=True)
+
+        # generate camera matrices
+        # Rs = get_views()
+        # Rs = get_views(semi_sphere=True)
+        Rs = get_views(semi_sphere=False)
+        # get depth images
+        depths = render(mesh_v, mesh_f, Rs)
+        # project to world space
+        try:
+            pcd = fusion(depths, Rs)
+        except:
+            return
+
+        # fps again
+
+        pcd, fps_idx = pytorch3d.ops.sample_farthest_points(
+            # torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4096,
+            # torch.from_numpy(pcd).unsqueeze(0).cuda(), K=4000,
+            pcd.unsqueeze(0).cuda(), K=4096,
+            random_start_point=True)  # B self.latent_num
+        pcd = pcd[0]
+
+        pcd = pcd.cpu().numpy()
+
+        #np.savez(output_pth, pcd=pcd)
+        #ipdb.set_trace()
+        #if debug:
+        pcd = trimesh.points.PointCloud(pcd)
+        pcd.export(output_pth.replace('.npz', '.obj'))
+    except Exception as e:
+        # print('error')
+        print(e, flush=True)
+
+
+if __name__ == '__main__':
+    mp.set_start_method('spawn')
+
+    shapenet_root = options.shape_root
+    save_root = options.save_root
+
+    debug = True
+
+    cmds = [] 
+
+    # ! for baseline samples
+
+    objv_dataset = '/mnt/sfs-common/yslan/Dataset/Obajverse/chunk-jpeg-normal/bs_16_fixsave3/170K/512/'
+    dataset_json = os.path.join(objv_dataset, 'dataset.json')
+    with open(dataset_json, 'r') as f:
+        dataset_json = json.load(f)
+
+    # all_objs = dataset_json['Animals'][::3][:6250]
+    all_objs = dataset_json['Animals'][::3][1100:2200]
+    all_objs = all_objs[:600]
+
+    os.makedirs(save_root, exist_ok=True)
+
+
+    for obj_folder in tqdm.tqdm(all_objs):
+        obj_folder = '/'.join(obj_folder.split('/')[1:])
+        for idx in [0]:
+
+            if 'CRM' in shapenet_root:
+                # ipdb.set_trace()
+                mesh_path = glob.glob(os.path.join(shapenet_root, obj_folder, f'{idx}', '*.obj'))[0]
+            elif 'Lara' in shapenet_root:
+                try:
+                    mesh_path = glob.glob(os.path.join(shapenet_root, '/'.join(obj_folder.split('/')[:-1]), f'{idx}.jpg', '*.obj'))[0]
+                except:
+                    continue
+            
+            elif 'scale3d' in shapenet_root:
+                if 'eval_nerf' in shapenet_root:
+                    mesh_path = glob.glob(os.path.join(shapenet_root, '/'.join(obj_folder.split('/')[:-1]), '1', 'mesh', '*.ply'))[0]
+                elif 'eval_mesh' in shapenet_root:
+                    mesh_path = glob.glob(os.path.join(shapenet_root, '/'.join(obj_folder.split('/')[:-1]), '1', 'mesh', '*.obj'))[0]
+
+                # files.append(os.path.join(path, '/'.join(obj_folder.split('/')[:-1]), '1', f'{idx}.png'))
+
+            elif 'LRM' in shapenet_root:
+                mesh_path = glob.glob(os.path.join(shapenet_root, '/'.join(obj_folder.split('/')[:-1]), '0', '*.ply'))[0]
+
+            elif 'ln3diff' in shapenet_root:
+                mesh_path = glob.glob(os.path.join(shapenet_root, obj_folder, '0', 'mesh.obj'))[0]
+            else:
+                if os.path.exists((os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.obj'))):
+                    mesh_path = os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.obj')
+                else:
+                    mesh_path = os.path.join(shapenet_root, obj_folder, f'{idx}/mesh.ply')
+
+            save_name = '-'.join(obj_folder.split('/'))
+            cmds += [(mesh_path, os.path.join(save_root, f'{save_name}_pcd_4096.ply'), debug)]
+
+
+    if options.n_proc == 0:
+        for filepath in tqdm.tqdm(cmds):
+            sample_surface_pts(filepath)
+    else:
+        with Pool(options.n_proc) as p:
+            list(tqdm.tqdm(p.imap(sample_surface_pts, cmds), total=len(cmds)))
diff --git a/evaluations/requirements.txt b/evaluations/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fc6df305a4169b13bcfab5e238e4ff1c97b6baaa
--- /dev/null
+++ b/evaluations/requirements.txt
@@ -0,0 +1,4 @@
+tensorflow-gpu>=2.0
+scipy
+requests
+tqdm
\ No newline at end of file
diff --git a/guided_diffusion/__init__.py b/guided_diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d018714d642ef967ed0152f418538c4019b2340
--- /dev/null
+++ b/guided_diffusion/__init__.py
@@ -0,0 +1,4 @@
+"""
+Codebase for "Improved Denoising Diffusion Probabilistic Models".
+Also merged continuous_diffusion.py from LSGM: https://github.com/NVlabs/LSGM
+"""
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diff --git a/guided_diffusion/continuous_diffusion.py b/guided_diffusion/continuous_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..775733d8f9bb47c909368c17ace82d415c98ac39
--- /dev/null
+++ b/guided_diffusion/continuous_diffusion.py
@@ -0,0 +1,795 @@
+# ---------------------------------------------------------------
+# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the NVIDIA Source Code License
+# for LSGM. To view a copy of this license, see the LICENSE file.
+# ---------------------------------------------------------------
+
+from pdb import set_trace as st
+from abc import ABC, abstractmethod
+import numpy as np
+import torch
+import gc
+from .continuous_distributions import log_p_standard_normal, log_p_var_normal
+from .continuous_diffusion_utils import trace_df_dx_hutchinson, sample_gaussian_like, sample_rademacher_like, get_mixed_prediction
+from torchdiffeq import odeint
+from torch.cuda.amp import autocast
+from timeit import default_timer as timer
+
+from guided_diffusion import dist_util, logger
+
+
+def make_diffusion(args):
+    """ simple diffusion factory function to return diffusion instances. Only use this to create continuous diffusions """
+    if args.sde_sde_type == 'geometric_sde':
+        return DiffusionGeometric(args)
+    elif args.sde_sde_type == 'vpsde':
+        return DiffusionVPSDE(args)
+    elif args.sde_sde_type == 'sub_vpsde':
+        return DiffusionSubVPSDE(args)
+    elif args.sde_sde_type == 'vesde':
+        return DiffusionVESDE(args)
+    else:
+        raise ValueError("Unrecognized sde type: {}".format(args.sde_sde_type))
+
+
+class DiffusionBase(ABC):
+    """
+    Abstract base class for all diffusion implementations.
+    """
+
+    def __init__(self, args):
+        super().__init__()
+        self.args = args
+        self.sigma2_0 = args.sde_sigma2_0
+        self.sde_type = args.sde_sde_type
+
+    @abstractmethod
+    def f(self, t):
+        """ returns the drift coefficient at time t: f(t) """
+        pass
+
+    @abstractmethod
+    def g2(self, t):
+        """ returns the squared diffusion coefficient at time t: g^2(t) """
+        pass
+
+    @abstractmethod
+    def var(self, t):
+        """ returns variance at time t, \sigma_t^2"""
+        pass
+
+    @abstractmethod
+    def e2int_f(self, t):
+        """ returns e^{\int_0^t f(s) ds} which corresponds to the coefficient of mean at time t. """
+        pass
+
+    @abstractmethod
+    def inv_var(self, var):
+        """ inverse of the variance function at input variance var. """
+        pass
+
+    @abstractmethod
+    def mixing_component(self, x_noisy, var_t, t, enabled):
+        """ returns mixing component which is the optimal denoising model assuming that q(z_0) is N(0, 1) """
+        pass
+
+    def sample_q(self, x_init, noise, var_t, m_t):
+        """ returns a sample from diffusion process at time t """
+        return m_t * x_init + torch.sqrt(var_t) * noise
+
+    def log_snr(self, m_t, var_t):
+        return torch.log((torch.square(m_t) / var_t))
+
+    def _predict_x0_from_eps(self, z, eps, logsnr):
+        """eps = (z - alpha * x0) / sigma
+        """
+        return torch.sqrt(1 + torch.exp(-logsnr)) * (
+            z - eps * torch.rsqrt(1 + torch.exp(logsnr)))
+
+    def _predict_eps_from_x0(self, z, x0, logsnr):
+        """x = (z - sigma * eps) / alpha
+        """
+        return torch.sqrt(1 + torch.exp(logsnr)) * (
+            z - x0 * torch.rsqrt(1 + torch.exp(-logsnr)))
+    
+    def _predict_eps_from_z_and_v(self, v_t, var_t, z, m_t):
+        # TODO, use logsnr here?
+        return torch.sqrt(var_t) * z + m_t * v_t
+
+    def _predict_x0_from_z_and_v(self, v_t, var_t, z, m_t):
+        return torch.sqrt(var_t) * v_t + m_t * z
+
+    def cross_entropy_const(self, ode_eps):
+        """ returns cross entropy factor with variance according to ode integration cutoff ode_eps """
+        # _, c, h, w = x_init.shape
+        return 0.5 * (1.0 + torch.log(2.0 * np.pi * self.var(
+            t=torch.tensor(ode_eps, device=dist_util.dev()))))
+
+    def compute_ode_nll(self, dae, eps, ode_eps, ode_solver_tol,
+                        enable_autocast, no_autograd, num_samples, report_std):
+        """ calculates NLL based on ODE framework, assuming integration cutoff ode_eps """
+        # ODE solver starts consuming the CPU memory without this on large models
+        # https://github.com/scipy/scipy/issues/10070
+        gc.collect()
+
+        dae.eval()
+
+        def ode_func(t, state):
+            """ the ode function (including log probability integration for NLL calculation) """
+            global nfe_counter
+            nfe_counter = nfe_counter + 1
+
+            x = state[0].detach()
+            x.requires_grad_(True)
+            noise = sample_gaussian_like(
+                x)  # could also use rademacher noise (sample_rademacher_like)
+            with torch.set_grad_enabled(True):
+                with autocast(enabled=enable_autocast):
+                    variance = self.var(t=t)
+                    mixing_component = self.mixing_component(
+                        x_noisy=x,
+                        var_t=variance,
+                        t=t,
+                        enabled=dae.mixed_prediction)
+                    pred_params = dae(x=x, t=t)
+                    params = get_mixed_prediction(dae.mixed_prediction,
+                                                  pred_params,
+                                                  dae.mixing_logit,
+                                                  mixing_component)
+                    dx_dt = self.f(t=t) * x + 0.5 * self.g2(
+                        t=t) * params / torch.sqrt(variance)
+
+                with autocast(enabled=False):
+                    dlogp_x_dt = -trace_df_dx_hutchinson(
+                        dx_dt, x, noise, no_autograd).view(x.shape[0], 1)
+
+            return (dx_dt, dlogp_x_dt)
+
+        # NFE counter
+        global nfe_counter
+
+        nll_all, nfe_all = [], []
+        for i in range(num_samples):
+            # integrated log probability
+            logp_diff_t0 = torch.zeros(eps.shape[0], 1, device=dist_util.dev())
+
+            nfe_counter = 0
+
+            # solve the ODE
+            x_t, logp_diff_t = odeint(
+                ode_func,
+                (eps, logp_diff_t0),
+                torch.tensor([ode_eps, 1.0], device=dist_util.dev()),
+                atol=ode_solver_tol,
+                rtol=ode_solver_tol,
+                method="scipy_solver",
+                options={"solver": 'RK45'},
+            )
+            # last output values
+            x_t0, logp_diff_t0 = x_t[-1], logp_diff_t[-1]
+
+            # prior
+            if self.sde_type == 'vesde':
+                logp_prior = torch.sum(log_p_var_normal(x_t0,
+                                                        var=self.sigma2_max),
+                                       dim=[1, 2, 3])
+            else:
+                logp_prior = torch.sum(log_p_standard_normal(x_t0),
+                                       dim=[1, 2, 3])
+
+            log_likelihood = logp_prior - logp_diff_t0.view(-1)
+
+            nll_all.append(-log_likelihood)
+            nfe_all.append(nfe_counter)
+
+        nfe_mean = np.mean(nfe_all)
+        nll_all = torch.stack(nll_all, dim=1)
+        nll_mean = torch.mean(nll_all, dim=1)
+        if num_samples > 1 and report_std:
+            nll_stddev = torch.std(nll_all, dim=1)
+            nll_stddev_batch = torch.mean(nll_stddev)
+            nll_stderror_batch = nll_stddev_batch / np.sqrt(num_samples)
+        else:
+            nll_stddev_batch = None
+            nll_stderror_batch = None
+        return nll_mean, nfe_mean, nll_stddev_batch, nll_stderror_batch
+
+    def sample_model_ode(self,
+                         dae,
+                         num_samples,
+                         shape,
+                         ode_eps,
+                         ode_solver_tol,
+                         enable_autocast,
+                         temp,
+                         noise=None):
+        """ generates samples using the ODE framework, assuming integration cutoff ode_eps """
+        # ODE solver starts consuming the CPU memory without this on large models
+        # https://github.com/scipy/scipy/issues/10070
+        gc.collect()
+
+        dae.eval()
+
+        def ode_func(t, x):
+            """ the ode function (sampling only, no NLL stuff) """
+            global nfe_counter
+            nfe_counter = nfe_counter + 1
+            with autocast(enabled=enable_autocast):
+                variance = self.var(t=t)
+                mixing_component = self.mixing_component(
+                    x_noisy=x,
+                    var_t=variance,
+                    t=t,
+                    enabled=dae.mixed_prediction)
+                pred_params = dae(x=x, t=t)
+                params = get_mixed_prediction(dae.mixed_prediction,
+                                              pred_params, dae.mixing_logit,
+                                              mixing_component)
+                dx_dt = self.f(t=t) * x + 0.5 * self.g2(
+                    t=t) * params / torch.sqrt(variance)
+
+            return dx_dt
+
+        # the initial noise
+        if noise is None:
+            noise = torch.randn(size=[num_samples] + shape,
+                                device=dist_util.dev())
+
+        if self.sde_type == 'vesde':
+            noise_init = temp * noise * np.sqrt(self.sigma2_max)
+        else:
+            noise_init = temp * noise
+
+        # NFE counter
+        global nfe_counter
+        nfe_counter = 0
+
+        # solve the ODE
+        start = timer()
+        samples_out = odeint(
+            ode_func,
+            noise_init,
+            torch.tensor([1.0, ode_eps], device=dist_util.dev()),
+            atol=ode_solver_tol,
+            rtol=ode_solver_tol,
+            method="scipy_solver",
+            options={"solver": 'RK45'},
+        )
+        end = timer()
+        ode_solve_time = end - start
+
+        return samples_out[-1], nfe_counter, ode_solve_time
+
+    # def iw_quantities(self, size, time_eps, iw_sample_mode, iw_subvp_like_vp_sde):
+    def iw_quantities(self, iw_sample_mode, size=None):
+
+        args = self.args
+        time_eps, iw_subvp_like_vp_sde = args.sde_time_eps, args.iw_subvp_like_vp_sde
+        if size is None:
+            size = args.batch_size
+
+        if self.sde_type in ['geometric_sde', 'vpsde']:
+            return self._iw_quantities_vpsdelike(size, time_eps,
+                                                 iw_sample_mode)
+        elif self.sde_type in ['sub_vpsde']:
+            return self._iw_quantities_subvpsdelike(size, time_eps,
+                                                    iw_sample_mode,
+                                                    iw_subvp_like_vp_sde)
+        elif self.sde_type in ['vesde']:
+            return self._iw_quantities_vesde(size, time_eps, iw_sample_mode)
+        else:
+            raise NotImplementedError
+
+    def _iw_quantities_vpsdelike(self, size, time_eps, iw_sample_mode):
+        """
+        For all SDEs where the underlying SDE is of the form dz = -0.5 * beta(t) * z * dt + sqrt{beta(t)} * dw, like
+        for the VPSDE.
+        """
+        rho = torch.rand(size=[size], device=dist_util.dev())
+
+        # In the following, obj_weight_t corresponds to the weight in front of the l2 loss for the given iw_sample_mode.
+        # obj_weight_t_ll corresponds to the weight that converts the weighting scheme in iw_sample_mode to likelihood
+        # weighting.
+
+        if iw_sample_mode == 'll_uniform':
+            # uniform t sampling - likelihood obj. for both q and p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'll_iw': # ! q-obj
+            # importance sampling for likelihood obj. - likelihood obj. for both q and p
+            ones = torch.ones_like(rho, device=dist_util.dev())
+            sigma2_1, sigma2_eps = self.var(ones), self.var(time_eps * ones)
+            log_sigma2_1, log_sigma2_eps = torch.log(sigma2_1), torch.log(
+                sigma2_eps)
+            var_t = torch.exp(rho * log_sigma2_1 +
+                              (1 - rho) * log_sigma2_eps)  # sigma square
+            t = self.inv_var(var_t)
+            m_t, g2_t = self.e2int_f(t), self.g2(t)  # m_t is alpha_bar
+            obj_weight_t = obj_weight_t_ll = 0.5 * (
+                log_sigma2_1 - log_sigma2_eps) / (1.0 - var_t)
+
+        elif iw_sample_mode == 'drop_all_uniform':
+            # uniform t sampling - likelihood obj. for q, all-prefactors-dropped obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = torch.ones(1, device=dist_util.dev())
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'drop_all_iw':
+            # importance sampling for all-pref.-dropped obj. - likelihood obj. for q, all-pref.-dropped obj. for p
+            assert self.sde_type == 'vpsde', 'Importance sampling for fully unweighted objective is currently only ' \
+                                             'implemented for the regular VPSDE.'
+            t = torch.sqrt(1.0 / self.delta_beta_half) * torch.erfinv(
+                rho * self.const_norm_2 + self.const_erf) - self.beta_frac
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = self.const_norm / (1.0 - var_t)
+            obj_weight_t_ll = obj_weight_t * g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'drop_sigma2t_iw': # ! default mode for p
+            # importance sampling for inv_sigma2_t-dropped obj. - likelihood obj. for q, inv_sigma2_t-dropped obj. for p
+            ones = torch.ones_like(rho, device=dist_util.dev())
+            sigma2_1, sigma2_eps = self.var(ones), self.var(time_eps * ones)
+            var_t = rho * sigma2_1 + (1 - rho) * sigma2_eps # ! sigma square
+            t = self.inv_var(var_t)
+            m_t, g2_t = self.e2int_f(t), self.g2(t) # ! m_t: alpha_bar sqrt
+            obj_weight_t = 0.5 * (sigma2_1 - sigma2_eps) / (1.0 - var_t)
+            obj_weight_t_ll = obj_weight_t / var_t
+
+        elif iw_sample_mode == 'drop_sigma2t_uniform':
+            # uniform sampling for inv_sigma2_t-dropped obj. - likelihood obj. for q, inv_sigma2_t-dropped obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = g2_t / 2.0
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'rescale_iw':
+            # importance sampling for 1/(1-sigma2_t) resc. obj. - likelihood obj. for q, 1/(1-sigma2_t) resc. obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = 0.5 / (1.0 - var_t)
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        else:
+            raise ValueError(
+                "Unrecognized importance sampling type: {}".format(
+                    iw_sample_mode))
+
+        return t, var_t.view(-1, 1, 1, 1), m_t.view(-1, 1, 1, 1), obj_weight_t.view(-1, 1, 1, 1), \
+            obj_weight_t_ll.view(-1, 1, 1, 1), g2_t.view(-1, 1, 1, 1)
+
+    def _iw_quantities_subvpsdelike(self, size, time_eps, iw_sample_mode,
+                                    iw_subvp_like_vp_sde):
+        """
+        For all SDEs where the underlying SDE is of the form
+        dz = -0.5 * beta(t) * z * dt + sqrt{beta(t) * (1 - exp[-2 * betaintegral])} * dw, like for the Sub-VPSDE.
+        When iw_subvp_like_vp_sde is True, then we define the importance sampling distributions based on an analogous
+        VPSDE, while stile using the Sub-VPSDE. The motivation is that deriving the correct importance sampling
+        distributions for the Sub-VPSDE itself is hard, but the importance sampling distributions from analogous VPSDEs
+        probably already significantly reduce the variance also for the Sub-VPSDE.
+        """
+        rho = torch.rand(size=[size], device=dist_util.dev())
+
+        # In the following, obj_weight_t corresponds to the weight in front of the l2 loss for the given iw_sample_mode.
+        # obj_weight_t_ll corresponds to the weight that converts the weighting scheme in iw_sample_mode to likelihood
+        # weighting.
+        if iw_sample_mode == 'll_uniform':
+            # uniform t sampling - likelihood obj. for both q and p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'll_iw':
+            if iw_subvp_like_vp_sde:
+                # importance sampling for vpsde likelihood obj. - sub-vpsde likelihood obj. for both q and p
+                ones = torch.ones_like(rho, device=dist_util.dev())
+                sigma2_1, sigma2_eps = self.var_vpsde(ones), self.var_vpsde(
+                    time_eps * ones)
+                log_sigma2_1, log_sigma2_eps = torch.log(sigma2_1), torch.log(
+                    sigma2_eps)
+                var_t_vpsde = torch.exp(rho * log_sigma2_1 +
+                                        (1 - rho) * log_sigma2_eps)
+                t = self.inv_var_vpsde(var_t_vpsde)
+                var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+                obj_weight_t = obj_weight_t_ll = g2_t / (2.0 * var_t) * \
+                    (log_sigma2_1 - log_sigma2_eps) * var_t_vpsde / (1 - var_t_vpsde) / self.beta(t)
+            else:
+                raise NotImplementedError
+
+        elif iw_sample_mode == 'drop_all_uniform':
+            # uniform t sampling - likelihood obj. for q, all-prefactors-dropped obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = torch.ones(1, device=dist_util.dev())
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'drop_all_iw':
+            if iw_subvp_like_vp_sde:
+                # importance sampling for all-pref.-dropped obj. - likelihood obj. for q, all-pref.-dropped obj. for p
+                assert self.sde_type == 'sub_vpsde', 'Importance sampling for fully unweighted objective is ' \
+                                                     'currently only implemented for the Sub-VPSDE.'
+                t = torch.sqrt(1.0 / self.delta_beta_half) * torch.erfinv(
+                    rho * self.const_norm_2 + self.const_erf) - self.beta_frac
+                var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+                obj_weight_t = self.const_norm / (1.0 - self.var_vpsde(t))
+                obj_weight_t_ll = obj_weight_t * g2_t / (2.0 * var_t)
+            else:
+                raise NotImplementedError
+
+        elif iw_sample_mode == 'drop_sigma2t_iw':
+            if iw_subvp_like_vp_sde:
+                # importance sampling for inv_sigma2_t-dropped obj. - likelihood obj. for q, inv_sigma2_t-dropped obj. for p
+                ones = torch.ones_like(rho, device=dist_util.dev())
+                sigma2_1, sigma2_eps = self.var_vpsde(ones), self.var_vpsde(
+                    time_eps * ones)
+                var_t_vpsde = rho * sigma2_1 + (1 - rho) * sigma2_eps
+                t = self.inv_var_vpsde(var_t_vpsde)
+                var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+                obj_weight_t = 0.5 * g2_t / self.beta(t) * (
+                    sigma2_1 - sigma2_eps) / (1.0 - var_t_vpsde)
+                obj_weight_t_ll = obj_weight_t / var_t
+            else:
+                raise NotImplementedError
+
+        elif iw_sample_mode == 'drop_sigma2t_uniform':
+            # uniform sampling for inv_sigma2_t-dropped obj. - likelihood obj. for q, inv_sigma2_t-dropped obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = g2_t / 2.0
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'rescale_iw':
+            # importance sampling for 1/(1-sigma2_t) resc. obj. - likelihood obj. for q, 1/(1-sigma2_t) resc. obj. for p
+            # Note that we use the sub-vpsde variance to scale the p objective! It's not clear what's optimal here!
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = 0.5 / (1.0 - var_t)
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        else:
+            raise ValueError(
+                "Unrecognized importance sampling type: {}".format(
+                    iw_sample_mode))
+
+        return t, var_t.view(-1, 1, 1, 1), m_t.view(-1, 1, 1, 1), obj_weight_t.view(-1, 1, 1, 1), \
+            obj_weight_t_ll.view(-1, 1, 1, 1), g2_t.view(-1, 1, 1, 1)
+
+    def _iw_quantities_vesde(self, size, time_eps, iw_sample_mode):
+        """
+        For the VESDE.
+        """
+        rho = torch.rand(size=[size], device=dist_util.dev())
+
+        # In the following, obj_weight_t corresponds to the weight in front of the l2 loss for the given iw_sample_mode.
+        # obj_weight_t_ll corresponds to the weight that converts the weighting scheme in iw_sample_mode to likelihood
+        # weighting.
+        if iw_sample_mode == 'll_uniform':
+            # uniform t sampling - likelihood obj. for both q and p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'll_iw':
+            # importance sampling for likelihood obj. - likelihood obj. for both q and p
+            ones = torch.ones_like(rho, device=dist_util.dev())
+            nsigma2_1, nsigma2_eps, sigma2_eps = self.var_N(ones), self.var_N(
+                time_eps * ones), self.var(time_eps * ones)
+            log_frac_sigma2_1, log_frac_sigma2_eps = torch.log(
+                self.sigma2_max / nsigma2_1), torch.log(nsigma2_eps /
+                                                        sigma2_eps)
+            var_N_t = (1.0 - self.sigma2_min) / (
+                1.0 - torch.exp(rho *
+                                (log_frac_sigma2_1 + log_frac_sigma2_eps) -
+                                log_frac_sigma2_eps))
+            t = self.inv_var_N(var_N_t)
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = obj_weight_t_ll = 0.5 * (
+                log_frac_sigma2_1 +
+                log_frac_sigma2_eps) * self.var_N(t) / (1.0 - self.sigma2_min)
+
+        elif iw_sample_mode == 'drop_all_uniform':
+            # uniform t sampling - likelihood obj. for q, all-prefactors-dropped obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = torch.ones(1, device=dist_util.dev())
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'drop_all_iw':
+            # importance sampling for all-pref.-dropped obj. - likelihood obj. for q, all-pref.-dropped obj. for p
+            ones = torch.ones_like(rho, device=dist_util.dev())
+            nsigma2_1, nsigma2_eps, sigma2_eps = self.var_N(ones), self.var_N(
+                time_eps * ones), self.var(time_eps * ones)
+            log_frac_sigma2_1, log_frac_sigma2_eps = torch.log(
+                self.sigma2_max / nsigma2_1), torch.log(nsigma2_eps /
+                                                        sigma2_eps)
+            var_N_t = (1.0 - self.sigma2_min) / (
+                1.0 - torch.exp(rho *
+                                (log_frac_sigma2_1 + log_frac_sigma2_eps) -
+                                log_frac_sigma2_eps))
+            t = self.inv_var_N(var_N_t)
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t_ll = 0.5 * (log_frac_sigma2_1 +
+                                     log_frac_sigma2_eps) * self.var_N(t) / (
+                                         1.0 - self.sigma2_min)
+            obj_weight_t = 2.0 * obj_weight_t_ll / np.log(
+                self.sigma2_max / self.sigma2_min)
+
+        elif iw_sample_mode == 'drop_sigma2t_iw':
+            # importance sampling for inv_sigma2_t-dropped obj. - likelihood obj. for q, inv_sigma2_t-dropped obj. for p
+            ones = torch.ones_like(rho, device=dist_util.dev())
+            nsigma2_1, nsigma2_eps = self.var_N(ones), self.var_N(time_eps *
+                                                                  ones)
+            var_N_t = torch.exp(rho * torch.log(nsigma2_1) +
+                                (1 - rho) * torch.log(nsigma2_eps))
+            t = self.inv_var_N(var_N_t)
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = 0.5 * torch.log(
+                nsigma2_1 / nsigma2_eps) * self.var_N(t)
+            obj_weight_t_ll = obj_weight_t / var_t
+
+        elif iw_sample_mode == 'drop_sigma2t_uniform':
+            # uniform sampling for inv_sigma2_t-dropped obj. - likelihood obj. for q, inv_sigma2_t-dropped obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = g2_t / 2.0
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        elif iw_sample_mode == 'rescale_iw':
+            # uniform sampling for 1/(1-sigma2_t) resc. obj. - likelihood obj. for q, 1/(1-sigma2_t) resc. obj. for p
+            t = rho * (1. - time_eps) + time_eps
+            var_t, m_t, g2_t = self.var(t), self.e2int_f(t), self.g2(t)
+            obj_weight_t = 0.5 / (1.0 - var_t)
+            obj_weight_t_ll = g2_t / (2.0 * var_t)
+
+        else:
+            raise ValueError(
+                "Unrecognized importance sampling type: {}".format(
+                    iw_sample_mode))
+
+        return t, var_t.view(-1, 1, 1, 1), m_t.view(-1, 1, 1, 1), obj_weight_t.view(-1, 1, 1, 1), \
+            obj_weight_t_ll.view(-1, 1, 1, 1), g2_t.view(-1, 1, 1, 1)
+
+
+class DiffusionGeometric(DiffusionBase):
+    """
+    Diffusion implementation with dz = -0.5 * beta(t) * z * dt + sqrt(beta(t)) * dW SDE and geometric progression of
+    variance. This is our new diffusion.
+    """
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.sigma2_min = args.sde_sigma2_min
+        self.sigma2_max = args.sde_sigma2_max
+
+    def f(self, t):
+        return -0.5 * self.g2(t)
+
+    def g2(self, t):
+        sigma2_geom = self.sigma2_min * (
+            (self.sigma2_max / self.sigma2_min)**t)
+        log_term = np.log(self.sigma2_max / self.sigma2_min)
+        return sigma2_geom * log_term / (1.0 - self.sigma2_0 +
+                                         self.sigma2_min - sigma2_geom)
+
+    def var(self, t):
+        return self.sigma2_min * ((self.sigma2_max / self.sigma2_min)**
+                                  t) - self.sigma2_min + self.sigma2_0
+
+    def e2int_f(self, t):
+        return torch.sqrt(1.0 + self.sigma2_min *
+                          (1.0 - (self.sigma2_max / self.sigma2_min)**t) /
+                          (1.0 - self.sigma2_0))
+
+    def inv_var(self, var):
+        return torch.log(
+            (var + self.sigma2_min - self.sigma2_0) /
+            self.sigma2_min) / np.log(self.sigma2_max / self.sigma2_min)
+
+    def mixing_component(self, x_noisy, var_t, t, enabled):
+        if enabled:
+            return torch.sqrt(var_t) * x_noisy
+        else:
+            return None
+
+
+class DiffusionVPSDE(DiffusionBase):
+    """
+    Diffusion implementation of the VPSDE. This uses the same SDE like DiffusionGeometric but with linear beta(t).
+    Note that we need to scale beta_start and beta_end by 1000 relative to JH's DDPM values, since our t is in [0,1].
+    """
+
+    def __init__(self, args):
+        super().__init__(args)
+        # self.beta_start = args.sde_beta_start # 0.1
+        # self.beta_end = args.sde_beta_end # 20
+
+        # ! hard coded, in the scale of 1000.
+        # beta_start = scale * 0.0001
+        # beta_end = scale * 0.02
+
+        self.beta_start = 0.1
+        self.beta_end = 20
+
+        # auxiliary constants
+        self.time_eps = args.sde_time_eps  # 0.01 by default in LSGM. Any influence?
+        self.delta_beta_half = torch.tensor(0.5 *
+                                            (self.beta_end - self.beta_start),
+                                            device=dist_util.dev())
+        self.beta_frac = torch.tensor(self.beta_start /
+                                      (self.beta_end - self.beta_start),
+                                      device=dist_util.dev())
+        self.const_aq = (1.0 - self.sigma2_0) * torch.exp(
+            0.5 * self.beta_frac) * torch.sqrt(
+                0.25 * np.pi / self.delta_beta_half)
+        self.const_erf = torch.erf(
+            torch.sqrt(self.delta_beta_half) *
+            (self.time_eps + self.beta_frac))
+        self.const_norm = self.const_aq * (torch.erf(
+            torch.sqrt(self.delta_beta_half) *
+            (1.0 + self.beta_frac)) - self.const_erf)
+        self.const_norm_2 = torch.erf(
+            torch.sqrt(self.delta_beta_half) *
+            (1.0 + self.beta_frac)) - self.const_erf
+
+    def f(self, t):
+        return -0.5 * self.g2(t)
+
+    def g2(self, t):
+        return self.beta_start + (self.beta_end - self.beta_start) * t
+
+    def var(self, t):
+        return 1.0 - (1.0 - self.sigma2_0
+                      ) * torch.exp(-self.beta_start * t - 0.5 *
+                                    (self.beta_end - self.beta_start) * t * t)
+
+    def e2int_f(self, t):
+        return torch.exp(-0.5 * self.beta_start * t - 0.25 *
+                         (self.beta_end - self.beta_start) * t * t)
+
+    def inv_var(self, var):
+        c = torch.log((1 - var) / (1 - self.sigma2_0))
+        a = self.beta_end - self.beta_start
+        t = (-self.beta_start +
+             torch.sqrt(np.square(self.beta_start) - 2 * a * c)) / a
+        return t
+
+    def mixing_component(self, x_noisy, var_t, t, enabled):
+        if enabled:
+            return torch.sqrt(var_t) * x_noisy
+        else:
+            return None
+
+    def mixing_component_x0(self, x_noisy, var_t, t, enabled):
+        if enabled:
+            # return torch.sqrt(var_t) * x_noisy
+            return torch.sqrt(1-var_t) * x_noisy # zt * alpha_t
+        else:
+            return None
+
+
+class DiffusionSubVPSDE(DiffusionBase):
+    """
+    Diffusion implementation of the sub-VPSDE. Note that this uses a different SDE compared to the above two diffusions.
+    """
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.beta_start = args.sde_beta_start
+        self.beta_end = args.sde_beta_end
+
+        # auxiliary constants (assumes regular VPSDE)
+        self.time_eps = args.sde_time_eps
+        self.delta_beta_half = torch.tensor(0.5 *
+                                            (self.beta_end - self.beta_start),
+                                            device=dist_util.dev())
+        self.beta_frac = torch.tensor(self.beta_start /
+                                      (self.beta_end - self.beta_start),
+                                      device=dist_util.dev())
+        self.const_aq = (1.0 - self.sigma2_0) * torch.exp(
+            0.5 * self.beta_frac) * torch.sqrt(
+                0.25 * np.pi / self.delta_beta_half)
+        self.const_erf = torch.erf(
+            torch.sqrt(self.delta_beta_half) *
+            (self.time_eps + self.beta_frac))
+        self.const_norm = self.const_aq * (torch.erf(
+            torch.sqrt(self.delta_beta_half) *
+            (1.0 + self.beta_frac)) - self.const_erf)
+        self.const_norm_2 = torch.erf(
+            torch.sqrt(self.delta_beta_half) *
+            (1.0 + self.beta_frac)) - self.const_erf
+
+    def f(self, t):
+        return -0.5 * self.beta(t)
+
+    def g2(self, t):
+        return self.beta(t) * (
+            1.0 - torch.exp(-2.0 * self.beta_start * t -
+                            (self.beta_end - self.beta_start) * t * t))
+
+    def var(self, t):
+        int_term = torch.exp(-self.beta_start * t - 0.5 *
+                             (self.beta_end - self.beta_start) * t * t)
+        return torch.square(1.0 - int_term) + self.sigma2_0 * int_term
+
+    def e2int_f(self, t):
+        return torch.exp(-0.5 * self.beta_start * t - 0.25 *
+                         (self.beta_end - self.beta_start) * t * t)
+
+    def beta(self, t):
+        """ auxiliary beta function """
+        return self.beta_start + (self.beta_end - self.beta_start) * t
+
+    def inv_var(self, var):
+        raise NotImplementedError
+
+    def mixing_component(self, x_noisy, var_t, t, enabled):
+        if enabled:
+            int_term = torch.exp(-self.beta_start * t - 0.5 *
+                                 (self.beta_end - self.beta_start) * t *
+                                 t).view(-1, 1, 1, 1)
+            return torch.sqrt(var_t) * x_noisy / (
+                torch.square(1.0 - int_term) + int_term)
+        else:
+            return None
+
+    def var_vpsde(self, t):
+        return 1.0 - (1.0 - self.sigma2_0
+                      ) * torch.exp(-self.beta_start * t - 0.5 *
+                                    (self.beta_end - self.beta_start) * t * t)
+
+    def inv_var_vpsde(self, var):
+        c = torch.log((1 - var) / (1 - self.sigma2_0))
+        a = self.beta_end - self.beta_start
+        t = (-self.beta_start +
+             torch.sqrt(np.square(self.beta_start) - 2 * a * c)) / a
+        return t
+
+
+class DiffusionVESDE(DiffusionBase):
+    """
+    Diffusion implementation of the VESDE with dz = sqrt(beta(t)) * dW
+    """
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.sigma2_min = args.sde_sigma2_min
+        self.sigma2_max = args.sde_sigma2_max
+        assert self.sigma2_min == self.sigma2_0, "VESDE was proposed implicitly assuming sigma2_min = sigma2_0!"
+
+    def f(self, t):
+        return torch.zeros_like(t, device=dist_util.dev())
+
+    def g2(self, t):
+        return self.sigma2_min * np.log(self.sigma2_max / self.sigma2_min) * (
+            (self.sigma2_max / self.sigma2_min)**t)
+
+    def var(self, t):
+        return self.sigma2_min * ((self.sigma2_max / self.sigma2_min)**
+                                  t) - self.sigma2_min + self.sigma2_0
+
+    def e2int_f(self, t):
+        return torch.ones_like(t, device=dist_util.dev())
+
+    def inv_var(self, var):
+        return torch.log(
+            (var + self.sigma2_min - self.sigma2_0) /
+            self.sigma2_min) / np.log(self.sigma2_max / self.sigma2_min)
+
+    def mixing_component(self, x_noisy, var_t, t, enabled):
+        if enabled:
+            return torch.sqrt(var_t) * x_noisy / (self.sigma2_min * (
+                (self.sigma2_max / self.sigma2_min)**t.view(-1, 1, 1, 1)) -
+                                                  self.sigma2_min + 1.0)
+        else:
+            return None
+
+    def var_N(self, t):
+        return 1.0 - self.sigma2_min + self.sigma2_min * (
+            (self.sigma2_max / self.sigma2_min)**t)
+
+    def inv_var_N(self, var):
+        return torch.log(
+            (var + self.sigma2_min - 1.0) / self.sigma2_min) / np.log(
+                self.sigma2_max / self.sigma2_min)
diff --git a/guided_diffusion/continuous_diffusion_utils.py b/guided_diffusion/continuous_diffusion_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d70c43111c37cd219e545d14b6bfb5b2f95ca30f
--- /dev/null
+++ b/guided_diffusion/continuous_diffusion_utils.py
@@ -0,0 +1,815 @@
+# ---------------------------------------------------------------
+# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the NVIDIA Source Code License
+# for LSGM. To view a copy of this license, see the LICENSE file.
+# ---------------------------------------------------------------
+
+import logging
+import os
+import math
+import shutil
+import time
+import sys
+import types
+
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.distributed as dist
+# from util.distributions import PixelNormal
+from torch.cuda.amp import autocast
+
+# from tensorboardX import SummaryWriter
+
+
+class AvgrageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.avg = 0
+        self.sum = 0
+        self.cnt = 0
+
+    def update(self, val, n=1):
+        self.sum += val * n
+        self.cnt += n
+        self.avg = self.sum / self.cnt
+
+
+class ExpMovingAvgrageMeter(object):
+
+    def __init__(self, momentum=0.9):
+        self.momentum = momentum
+        self.reset()
+
+    def reset(self):
+        self.avg = 0
+
+    def update(self, val):
+        self.avg = (1. - self.momentum) * self.avg + self.momentum * val
+
+
+class DummyDDP(nn.Module):
+    def __init__(self, model):
+        super(DummyDDP, self).__init__()
+        self.module = model
+
+    def forward(self, *input, **kwargs):
+        return self.module(*input, **kwargs)
+
+
+def count_parameters_in_M(model):
+    return np.sum(np.prod(v.size()) for name, v in model.named_parameters() if "auxiliary" not in name) / 1e6
+
+
+def save_checkpoint(state, is_best, save):
+    filename = os.path.join(save, 'checkpoint.pth.tar')
+    torch.save(state, filename)
+    if is_best:
+        best_filename = os.path.join(save, 'model_best.pth.tar')
+        shutil.copyfile(filename, best_filename)
+
+
+def save(model, model_path):
+    torch.save(model.state_dict(), model_path)
+
+
+def load(model, model_path):
+    model.load_state_dict(torch.load(model_path))
+
+
+def create_exp_dir(path, scripts_to_save=None):
+    if not os.path.exists(path):
+        os.makedirs(path, exist_ok=True)
+    print('Experiment dir : {}'.format(path))
+
+    if scripts_to_save is not None:
+        if not os.path.exists(os.path.join(path, 'scripts')):
+            os.mkdir(os.path.join(path, 'scripts'))
+        for script in scripts_to_save:
+            dst_file = os.path.join(path, 'scripts', os.path.basename(script))
+            shutil.copyfile(script, dst_file)
+
+
+class Logger(object):
+    def __init__(self, rank, save):
+        # other libraries may set logging before arriving at this line.
+        # by reloading logging, we can get rid of previous configs set by other libraries.
+        from importlib import reload
+        reload(logging)
+        self.rank = rank
+        if self.rank == 0:
+            log_format = '%(asctime)s %(message)s'
+            logging.basicConfig(stream=sys.stdout, level=logging.INFO,
+                                format=log_format, datefmt='%m/%d %I:%M:%S %p')
+            fh = logging.FileHandler(os.path.join(save, 'log.txt'))
+            fh.setFormatter(logging.Formatter(log_format))
+            logging.getLogger().addHandler(fh)
+            self.start_time = time.time()
+
+    def info(self, string, *args):
+        if self.rank == 0:
+            elapsed_time = time.time() - self.start_time
+            elapsed_time = time.strftime(
+                '(Elapsed: %H:%M:%S) ', time.gmtime(elapsed_time))
+            if isinstance(string, str):
+                string = elapsed_time + string
+            else:
+                logging.info(elapsed_time)
+            logging.info(string, *args)
+
+
+class Writer(object):
+    def __init__(self, rank, save):
+        self.rank = rank
+        if self.rank == 0:
+            self.writer = SummaryWriter(log_dir=save, flush_secs=20)
+
+    def add_scalar(self, *args, **kwargs):
+        if self.rank == 0:
+            self.writer.add_scalar(*args, **kwargs)
+
+    def add_figure(self, *args, **kwargs):
+        if self.rank == 0:
+            self.writer.add_figure(*args, **kwargs)
+
+    def add_image(self, *args, **kwargs):
+        if self.rank == 0:
+            self.writer.add_image(*args, **kwargs)
+
+    def add_histogram(self, *args, **kwargs):
+        if self.rank == 0:
+            self.writer.add_histogram(*args, **kwargs)
+
+    def add_histogram_if(self, write, *args, **kwargs):
+        if write and False:  # Used for debugging.
+            self.add_histogram(*args, **kwargs)
+
+    def close(self, *args, **kwargs):
+        if self.rank == 0:
+            self.writer.close()
+
+
+def common_init(rank, seed, save_dir):
+    # we use different seeds per gpu. But we sync the weights after model initialization.
+    torch.manual_seed(rank + seed)
+    np.random.seed(rank + seed)
+    torch.cuda.manual_seed(rank + seed)
+    torch.cuda.manual_seed_all(rank + seed)
+    torch.backends.cudnn.benchmark = True
+
+    # prepare logging and tensorboard summary
+    logging = Logger(rank, save_dir)
+    writer = Writer(rank, save_dir)
+
+    return logging, writer
+
+
+def reduce_tensor(tensor, world_size):
+    rt = tensor.clone()
+    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
+    rt /= world_size
+    return rt
+
+
+def get_stride_for_cell_type(cell_type):
+    if cell_type.startswith('normal') or cell_type.startswith('combiner'):
+        stride = 1
+    elif cell_type.startswith('down'):
+        stride = 2
+    elif cell_type.startswith('up'):
+        stride = -1
+    else:
+        raise NotImplementedError(cell_type)
+
+    return stride
+
+
+def get_cout(cin, stride):
+    if stride == 1:
+        cout = cin
+    elif stride == -1:
+        cout = cin // 2
+    elif stride == 2:
+        cout = 2 * cin
+
+    return cout
+
+
+def kl_balancer_coeff(num_scales, groups_per_scale, fun):
+    if fun == 'equal':
+        coeff = torch.cat([torch.ones(groups_per_scale[num_scales - i - 1]) for i in range(num_scales)], dim=0).cuda()
+    elif fun == 'linear':
+        coeff = torch.cat([(2 ** i) * torch.ones(groups_per_scale[num_scales - i - 1]) for i in range(num_scales)],
+                          dim=0).cuda()
+    elif fun == 'sqrt':
+        coeff = torch.cat(
+            [np.sqrt(2 ** i) * torch.ones(groups_per_scale[num_scales - i - 1]) for i in range(num_scales)],
+            dim=0).cuda()
+    elif fun == 'square':
+        coeff = torch.cat(
+            [np.square(2 ** i) / groups_per_scale[num_scales - i - 1] * torch.ones(groups_per_scale[num_scales - i - 1])
+             for i in range(num_scales)], dim=0).cuda()
+    else:
+        raise NotImplementedError
+    # convert min to 1.
+    coeff /= torch.min(coeff)
+    return coeff
+
+
+def kl_per_group(kl_all):
+    kl_vals = torch.mean(kl_all, dim=0)
+    kl_coeff_i = torch.abs(kl_all)
+    kl_coeff_i = torch.mean(kl_coeff_i, dim=0, keepdim=True) + 0.01
+
+    return kl_coeff_i, kl_vals
+
+
+def kl_balancer(kl_all, kl_coeff=1.0, kl_balance=False, alpha_i=None):
+    if kl_balance and kl_coeff < 1.0:
+        alpha_i = alpha_i.unsqueeze(0)
+
+        kl_all = torch.stack(kl_all, dim=1)
+        kl_coeff_i, kl_vals = kl_per_group(kl_all)
+        total_kl = torch.sum(kl_coeff_i)
+
+        kl_coeff_i = kl_coeff_i / alpha_i * total_kl
+        kl_coeff_i = kl_coeff_i / torch.mean(kl_coeff_i, dim=1, keepdim=True)
+        kl = torch.sum(kl_all * kl_coeff_i.detach(), dim=1)
+
+        # for reporting
+        kl_coeffs = kl_coeff_i.squeeze(0)
+    else:
+        kl_all = torch.stack(kl_all, dim=1)
+        kl_vals = torch.mean(kl_all, dim=0)
+        # kl = torch.sum(kl_all, dim=1)
+        # kl = torch.mean(kl_all, dim=1)
+        kl = torch.mean(kl_all)
+        kl_coeffs = torch.ones(size=(len(kl_vals),))
+
+    return kl_coeff * kl, kl_coeffs, kl_vals
+
+
+def kl_per_group_vada(all_log_q, all_neg_log_p):
+    assert len(all_log_q) == len(all_neg_log_p)
+
+    kl_all_list = []
+    kl_diag = []
+    for log_q, neg_log_p in zip(all_log_q, all_neg_log_p):
+        # kl_diag.append(torch.mean(torch.sum(neg_log_p + log_q, dim=[2, 3]), dim=0))
+        kl_diag.append(torch.mean(torch.mean(neg_log_p + log_q, dim=[2, 3]), dim=0))
+        # kl_all_list.append(torch.sum(neg_log_p + log_q, dim=[1, 2, 3]))
+        kl_all_list.append(torch.mean(neg_log_p + log_q, dim=[1, 2, 3]))
+
+    # kl_all = torch.stack(kl_all, dim=1)   # batch x num_total_groups
+    kl_vals = torch.mean(torch.stack(kl_all_list, dim=1), dim=0)   # mean per group
+
+    return kl_all_list, kl_vals, kl_diag
+
+
+def kl_coeff(step, total_step, constant_step, min_kl_coeff, max_kl_coeff):
+    # return max(min(max_kl_coeff * (step - constant_step) / total_step, max_kl_coeff), min_kl_coeff)
+    return max(min(min_kl_coeff + (max_kl_coeff - min_kl_coeff) * (step - constant_step) / total_step, max_kl_coeff), min_kl_coeff)
+
+
+def log_iw(decoder, x, log_q, log_p, crop=False):
+    recon = reconstruction_loss(decoder, x, crop)
+    return - recon - log_q + log_p
+
+
+def reconstruction_loss(decoder, x, crop=False):
+    from util.distributions import DiscMixLogistic
+
+    recon = decoder.log_p(x)
+    if crop:
+        recon = recon[:, :, 2:30, 2:30]
+
+    if isinstance(decoder, DiscMixLogistic):
+        return - torch.sum(recon, dim=[1, 2])  # summation over RGB is done.
+    else:
+        return - torch.sum(recon, dim=[1, 2, 3])
+
+
+def vae_terms(all_log_q, all_eps):
+    from util.distributions import log_p_standard_normal
+
+    # compute kl
+    kl_all = []
+    kl_diag = []
+    log_p, log_q = 0., 0.
+    for log_q_conv, eps in zip(all_log_q, all_eps):
+        log_p_conv = log_p_standard_normal(eps)
+        kl_per_var = log_q_conv - log_p_conv
+        kl_diag.append(torch.mean(torch.sum(kl_per_var, dim=[2, 3]), dim=0))
+        kl_all.append(torch.sum(kl_per_var, dim=[1, 2, 3]))
+        log_q += torch.sum(log_q_conv, dim=[1, 2, 3])
+        log_p += torch.sum(log_p_conv, dim=[1, 2, 3])
+    return log_q, log_p, kl_all, kl_diag
+
+
+def sum_log_q(all_log_q):
+    log_q = 0.
+    for log_q_conv in all_log_q:
+        log_q += torch.sum(log_q_conv, dim=[1, 2, 3])
+
+    return log_q
+
+
+def cross_entropy_normal(all_eps):
+    from util.distributions import log_p_standard_normal
+
+    cross_entropy = 0.
+    neg_log_p_per_group = []
+    for eps in all_eps:
+        neg_log_p_conv = - log_p_standard_normal(eps)
+        neg_log_p = torch.sum(neg_log_p_conv, dim=[1, 2, 3])
+        cross_entropy += neg_log_p
+        neg_log_p_per_group.append(neg_log_p_conv)
+
+    return cross_entropy, neg_log_p_per_group
+
+
+def tile_image(batch_image, n, m=None):
+    if m is None:
+        m = n
+    assert n * m == batch_image.size(0)
+    channels, height, width = batch_image.size(1), batch_image.size(2), batch_image.size(3)
+    batch_image = batch_image.view(n, m, channels, height, width)
+    batch_image = batch_image.permute(2, 0, 3, 1, 4)  # n, height, n, width, c
+    batch_image = batch_image.contiguous().view(channels, n * height, m * width)
+    return batch_image
+
+
+def average_gradients_naive(params, is_distributed):
+    """ Gradient averaging. """
+    if is_distributed:
+        size = float(dist.get_world_size())
+        for param in params:
+            if param.requires_grad:
+                param.grad.data /= size
+                dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
+
+
+def average_gradients(params, is_distributed):
+    """ Gradient averaging. """
+    if is_distributed:
+        if isinstance(params, types.GeneratorType):
+            params = [p for p in params]
+
+        size = float(dist.get_world_size())
+        grad_data = []
+        grad_size = []
+        grad_shapes = []
+        # Gather all grad values
+        for param in params:
+            if param.requires_grad:
+                grad_size.append(param.grad.data.numel())
+                grad_shapes.append(list(param.grad.data.shape))
+                grad_data.append(param.grad.data.flatten())
+        grad_data = torch.cat(grad_data).contiguous()
+
+        # All-reduce grad values
+        grad_data /= size
+        dist.all_reduce(grad_data, op=dist.ReduceOp.SUM)
+
+        # Put back the reduce grad values to parameters
+        base = 0
+        for i, param in enumerate(params):
+            if param.requires_grad:
+                param.grad.data = grad_data[base:base + grad_size[i]].view(grad_shapes[i])
+                base += grad_size[i]
+
+
+def average_params(params, is_distributed):
+    """ parameter averaging. """
+    if is_distributed:
+        size = float(dist.get_world_size())
+        for param in params:
+            param.data /= size
+            dist.all_reduce(param.data, op=dist.ReduceOp.SUM)
+
+
+def average_tensor(t, is_distributed):
+    if is_distributed:
+        size = float(dist.get_world_size())
+        dist.all_reduce(t.data, op=dist.ReduceOp.SUM)
+        t.data /= size
+
+
+def broadcast_params(params, is_distributed):
+    if is_distributed:
+        for param in params:
+            dist.broadcast(param.data, src=0)
+
+
+def num_output(dataset):
+    if dataset in {'mnist',  'omniglot'}:
+        return 28 * 28
+    elif dataset == 'cifar10':
+        return 3 * 32 * 32
+    elif dataset.startswith('celeba') or dataset.startswith('imagenet') or dataset.startswith('lsun'):
+        size = int(dataset.split('_')[-1])
+        return 3 * size * size
+    elif dataset == 'ffhq':
+        return 3 * 256 * 256
+    else:
+        raise NotImplementedError
+
+
+def get_input_size(dataset):
+    if dataset in {'mnist', 'omniglot'}:
+        return 32
+    elif dataset == 'cifar10':
+        return 32
+    elif dataset.startswith('celeba') or dataset.startswith('imagenet') or dataset.startswith('lsun'):
+        size = int(dataset.split('_')[-1])
+        return size
+    elif dataset == 'ffhq':
+        return 256
+    else:
+        raise NotImplementedError
+
+
+def get_bpd_coeff(dataset):
+    n = num_output(dataset)
+    return 1. / np.log(2.) / n
+
+
+def get_channel_multiplier(dataset, num_scales):
+    if dataset in {'cifar10', 'omniglot'}:
+        mult = (1, 1, 1)
+    elif dataset in {'celeba_256', 'ffhq', 'lsun_church_256'}:
+        if num_scales == 3:
+            mult = (1, 1, 1)        # used for prior at 16
+        elif num_scales == 4:
+            mult = (1, 2, 2, 2)     # used for prior at 32
+        elif num_scales == 5:
+            mult = (1, 1, 2, 2, 2)  # used for prior at 64
+    elif dataset == 'mnist':
+        mult = (1, 1)
+    else:
+        raise NotImplementedError
+
+    return mult
+
+
+def get_attention_scales(dataset):
+    if dataset in {'cifar10', 'omniglot'}:
+        attn = (True, False, False)
+    elif dataset in {'celeba_256', 'ffhq', 'lsun_church_256'}:
+        # attn = (False, True, False, False) # used for 32
+        attn = (False, False, True, False, False)  # used for 64
+    elif dataset == 'mnist':
+        attn = (True, False)
+    else:
+        raise NotImplementedError
+
+    return attn
+
+
+def change_bit_length(x, num_bits):
+    if num_bits != 8:
+        x = torch.floor(x * 255 / 2 ** (8 - num_bits))
+        x /= (2 ** num_bits - 1)
+    return x
+
+
+def view4D(t, size, inplace=True):
+    """
+     Equal to view(-1, 1, 1, 1).expand(size)
+     Designed because of this bug:
+     https://github.com/pytorch/pytorch/pull/48696
+    """
+    if inplace:
+        return t.unsqueeze_(-1).unsqueeze_(-1).unsqueeze_(-1).expand(size)
+    else:
+        return t.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(size)
+
+
+def get_arch_cells(arch_type, use_se):
+    if arch_type == 'res_mbconv':
+        arch_cells = dict()
+        arch_cells['normal_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_dec'] = {'conv_branch': ['mconv_e6k5g0'], 'se': use_se}
+        arch_cells['up_dec'] = {'conv_branch': ['mconv_e6k5g0'], 'se': use_se}
+        arch_cells['normal_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_post'] = {'conv_branch': ['mconv_e3k5g0'], 'se': use_se}
+        arch_cells['up_post'] = {'conv_branch': ['mconv_e3k5g0'], 'se': use_se}
+        arch_cells['ar_nn'] = ['']
+    elif arch_type == 'res_bnswish':
+        arch_cells = dict()
+        arch_cells['normal_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_dec'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['up_dec'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_post'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['up_post'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['ar_nn'] = ['']
+    elif arch_type == 'res_bnswish2':
+        arch_cells = dict()
+        arch_cells['normal_enc'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['down_enc'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['normal_dec'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['up_dec'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['normal_pre'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['down_pre'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['normal_post'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['up_post'] = {'conv_branch': ['res_bnswish_x2'], 'se': use_se}
+        arch_cells['ar_nn'] = ['']
+    elif arch_type == 'res_mbconv_attn':
+        arch_cells = dict()
+        arch_cells['normal_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish', ], 'se': use_se, 'attn_type': 'attn'}
+        arch_cells['down_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se, 'attn_type': 'attn'}
+        arch_cells['normal_dec'] = {'conv_branch': ['mconv_e6k5g0'], 'se': use_se, 'attn_type': 'attn'}
+        arch_cells['up_dec'] = {'conv_branch': ['mconv_e6k5g0'], 'se': use_se, 'attn_type': 'attn'}
+        arch_cells['normal_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_post'] = {'conv_branch': ['mconv_e3k5g0'], 'se': use_se}
+        arch_cells['up_post'] = {'conv_branch': ['mconv_e3k5g0'], 'se': use_se}
+        arch_cells['ar_nn'] = ['']
+    elif arch_type == 'res_mbconv_attn_half':
+        arch_cells = dict()
+        arch_cells['normal_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_enc'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_dec'] = {'conv_branch': ['mconv_e6k5g0'], 'se': use_se, 'attn_type': 'attn'}
+        arch_cells['up_dec'] = {'conv_branch': ['mconv_e6k5g0'], 'se': use_se, 'attn_type': 'attn'}
+        arch_cells['normal_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['down_pre'] = {'conv_branch': ['res_bnswish', 'res_bnswish'], 'se': use_se}
+        arch_cells['normal_post'] = {'conv_branch': ['mconv_e3k5g0'], 'se': use_se}
+        arch_cells['up_post'] = {'conv_branch': ['mconv_e3k5g0'], 'se': use_se}
+        arch_cells['ar_nn'] = ['']
+    else:
+        raise NotImplementedError
+
+    return arch_cells
+
+
+def groups_per_scale(num_scales, num_groups_per_scale):
+    g = []
+    n = num_groups_per_scale
+    for s in range(num_scales):
+        assert n >= 1
+        g.append(n)
+    return g
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(self, embedding_dim, scale):
+        super(PositionalEmbedding, self).__init__()
+        self.embedding_dim = embedding_dim
+        self.scale = scale
+
+    def forward(self, timesteps):
+        assert len(timesteps.shape) == 1
+        timesteps = timesteps * self.scale
+        half_dim = self.embedding_dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim) * -emb)
+        emb = emb.to(device=timesteps.device)
+        emb = timesteps[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        return emb
+
+
+class RandomFourierEmbedding(nn.Module):
+    def __init__(self, embedding_dim, scale):
+        super(RandomFourierEmbedding, self).__init__()
+        self.w = nn.Parameter(torch.randn(size=(1, embedding_dim // 2)) * scale, requires_grad=False)
+
+    def forward(self, timesteps):
+        emb = torch.mm(timesteps[:, None], self.w * 2 * 3.14159265359)
+        return torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+
+
+def init_temb_fun(embedding_type, embedding_scale, embedding_dim):
+    if embedding_type == 'positional':
+        temb_fun = PositionalEmbedding(embedding_dim, embedding_scale)
+    elif embedding_type == 'fourier':
+        temb_fun = RandomFourierEmbedding(embedding_dim, embedding_scale)
+    else:
+        raise NotImplementedError
+
+    return temb_fun
+
+def get_dae_model(args, num_input_channels):
+    if args.dae_arch == 'ncsnpp':
+        # we need to import NCSNpp after processes are launched on the multi gpu training.
+        from score_sde.ncsnpp import NCSNpp
+        dae = NCSNpp(args, num_input_channels)
+    else:
+        raise NotImplementedError
+
+    return dae
+
+def symmetrize_image_data(images):
+    return 2.0 * images - 1.0
+
+
+def unsymmetrize_image_data(images):
+    return (images + 1.) / 2.
+
+
+def normalize_symmetric(images):
+    """
+    Normalize images by dividing the largest intensity. Used for visualizing the intermediate steps.
+    """
+    b = images.shape[0]
+    m, _ = torch.max(torch.abs(images).view(b, -1), dim=1)
+    images /= (m.view(b, 1, 1, 1) + 1e-3)
+
+    return images
+
+
+@torch.jit.script
+def soft_clamp5(x: torch.Tensor):
+    return x.div(5.).tanh_().mul(5.)  # 5. * torch.tanh(x / 5.) <--> soft differentiable clamp between [-5, 5]
+
+@torch.jit.script
+def soft_clamp(x: torch.Tensor, a: torch.Tensor):
+    return x.div(a).tanh_().mul(a)
+
+class SoftClamp5(nn.Module):
+    def __init__(self):
+        super(SoftClamp5, self).__init__()
+
+    def forward(self, x):
+        return soft_clamp5(x)
+
+
+def override_architecture_fields(args, stored_args, logging):
+    # list of architecture parameters used in NVAE:
+    architecture_fields = ['arch_instance', 'num_nf', 'num_latent_scales', 'num_groups_per_scale',
+                           'num_latent_per_group', 'num_channels_enc', 'num_preprocess_blocks',
+                           'num_preprocess_cells', 'num_cell_per_cond_enc', 'num_channels_dec',
+                           'num_postprocess_blocks', 'num_postprocess_cells', 'num_cell_per_cond_dec',
+                           'decoder_dist', 'num_x_bits', 'log_sig_q_scale',
+                           'progressive_input_vae', 'channel_mult']
+
+    # backward compatibility
+    """ We have broken backward compatibility. No need to se these manually
+    if not hasattr(stored_args, 'log_sig_q_scale'):
+        logging.info('*** Setting %s manually ****', 'log_sig_q_scale')
+        setattr(stored_args, 'log_sig_q_scale', 5.)
+
+    if not hasattr(stored_args, 'latent_grad_cutoff'):
+        logging.info('*** Setting %s manually ****', 'latent_grad_cutoff')
+        setattr(stored_args, 'latent_grad_cutoff', 0.)
+
+    if not hasattr(stored_args, 'progressive_input_vae'):
+        logging.info('*** Setting %s manually ****', 'progressive_input_vae')
+        setattr(stored_args, 'progressive_input_vae', 'none')
+
+    if not hasattr(stored_args, 'progressive_output_vae'):
+        logging.info('*** Setting %s manually ****', 'progressive_output_vae')
+        setattr(stored_args, 'progressive_output_vae', 'none')
+    """
+
+    if not hasattr(stored_args, 'num_x_bits'):
+        logging.info('*** Setting %s manually ****', 'num_x_bits')
+        setattr(stored_args, 'num_x_bits', 8)
+
+    if not hasattr(stored_args, 'channel_mult'):
+        logging.info('*** Setting %s manually ****', 'channel_mult')
+        setattr(stored_args, 'channel_mult', [1, 2])
+
+    for f in architecture_fields:
+        if not hasattr(args, f) or getattr(args, f) != getattr(stored_args, f):
+            logging.info('Setting %s from loaded checkpoint', f)
+            setattr(args, f, getattr(stored_args, f))
+
+
+def init_processes(rank, size, fn, args):
+    """ Initialize the distributed environment. """
+    os.environ['MASTER_ADDR'] = args.master_address
+    os.environ['MASTER_PORT'] = '6020'
+    torch.cuda.set_device(args.local_rank)
+    dist.init_process_group(backend='nccl', init_method='env://', rank=rank, world_size=size)
+    fn(args)
+    dist.barrier()
+    dist.destroy_process_group()
+
+
+def sample_rademacher_like(y):
+    return torch.randint(low=0, high=2, size=y.shape, device='cuda') * 2 - 1
+
+
+def sample_gaussian_like(y):
+    return torch.randn_like(y, device='cuda')
+
+
+def trace_df_dx_hutchinson(f, x, noise, no_autograd):
+    """
+    Hutchinson's trace estimator for Jacobian df/dx, O(1) call to autograd
+    """
+    if no_autograd:
+        # the following is compatible with checkpointing
+        torch.sum(f * noise).backward()
+        # torch.autograd.backward(tensors=[f], grad_tensors=[noise])
+        jvp = x.grad
+        trJ = torch.sum(jvp * noise, dim=[1, 2, 3])
+        x.grad = None
+    else:
+        jvp = torch.autograd.grad(f, x, noise, create_graph=False)[0]
+        trJ = torch.sum(jvp * noise, dim=[1, 2, 3])
+        # trJ = torch.einsum('bijk,bijk->b', jvp, noise)  # we could test if there's a speed difference in einsum vs sum
+
+    return trJ
+
+def different_p_q_objectives(iw_sample_p, iw_sample_q):
+    assert iw_sample_p in ['ll_uniform', 'drop_all_uniform', 'll_iw', 'drop_all_iw', 'drop_sigma2t_iw', 'rescale_iw',
+                           'drop_sigma2t_uniform']
+    assert iw_sample_q in ['reweight_p_samples', 'll_uniform', 'll_iw']
+    # In these cases, we reuse the likelihood-based p-objective (either the uniform sampling version or the importance
+    # sampling version) also for q.
+    if iw_sample_p in ['ll_uniform', 'll_iw'] and iw_sample_q == 'reweight_p_samples':
+        return False
+    # In these cases, we are using a non-likelihood-based objective for p, and hence definitly need to use another q
+    # objective.
+    else:
+        return True
+
+
+# def decoder_output(dataset, logits, fixed_log_scales=None):
+#     if dataset in {'cifar10', 'celeba_64', 'celeba_256', 'imagenet_32', 'imagenet_64', 'ffhq',
+#                    'lsun_bedroom_128', 'lsun_bedroom_256', 'mnist', 'omniglot',
+#                    'lsun_church_256'}:
+#         return PixelNormal(logits, fixed_log_scales)
+#     else:
+#         raise NotImplementedError
+
+
+def get_mixed_prediction(mixed_prediction, param, mixing_logit, mixing_component=None):
+    if mixed_prediction:
+        assert mixing_component is not None, 'Provide mixing component when mixed_prediction is enabled.'
+        coeff = torch.sigmoid(mixing_logit)
+        param = (1 - coeff) * mixing_component + coeff * param
+
+    return param
+
+
+def set_vesde_sigma_max(args, vae, train_queue, logging, is_distributed):
+    logging.info('')
+    logging.info('Calculating max. pairwise distance in latent space to set sigma2_max for VESDE...')
+
+    eps_list = []
+    vae.eval()
+    for step, x in enumerate(train_queue):
+        x = x[0] if len(x) > 1 else x
+        x = x.cuda()
+        x = symmetrize_image_data(x)
+
+        # run vae
+        with autocast(enabled=args.autocast_train):
+            with torch.set_grad_enabled(False):
+                logits, all_log_q, all_eps = vae(x)
+                eps = torch.cat(all_eps, dim=1)
+
+        eps_list.append(eps.detach())
+
+    # concat eps tensor on each GPU and then gather all on all GPUs
+    eps_this_rank = torch.cat(eps_list, dim=0)
+    if is_distributed:
+        eps_all_gathered = [torch.zeros_like(eps_this_rank)] * dist.get_world_size()
+        dist.all_gather(eps_all_gathered, eps_this_rank)
+        eps_full = torch.cat(eps_all_gathered, dim=0)
+    else:
+        eps_full = eps_this_rank
+
+    # max pairwise distance squared between all latent encodings, is computed on CPU
+    eps_full = eps_full.cpu().float()
+    eps_full = eps_full.flatten(start_dim=1).unsqueeze(0)
+    max_pairwise_dist_sqr = torch.cdist(eps_full, eps_full).square().max()
+    max_pairwise_dist_sqr = max_pairwise_dist_sqr.cuda()
+
+    # to be safe, we broadcast to all GPUs if we are in distributed environment. Shouldn't be necessary in principle.
+    if is_distributed:
+        dist.broadcast(max_pairwise_dist_sqr, src=0)
+
+    args.sigma2_max = max_pairwise_dist_sqr.item()
+
+    logging.info('Done! Set args.sigma2_max set to {}'.format(args.sigma2_max))
+    logging.info('')
+    return args
+
+
+def mask_inactive_variables(x, is_active):
+    x = x * is_active
+    return x
+
+
+def common_x_operations(x, num_x_bits):
+    x = x[0] if len(x) > 1 else x
+    x = x.cuda()
+
+    # change bit length
+    x = change_bit_length(x, num_x_bits)
+    x = symmetrize_image_data(x)
+
+    return x
diff --git a/guided_diffusion/continuous_distributions.py b/guided_diffusion/continuous_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..288c3d9a6ade4d2b2bef64704d666a00ddc499c0
--- /dev/null
+++ b/guided_diffusion/continuous_distributions.py
@@ -0,0 +1,284 @@
+# ---------------------------------------------------------------
+# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the NVIDIA Source Code License
+# for LSGM. To view a copy of this license, see the LICENSE file.
+# ---------------------------------------------------------------
+
+import torch
+import torch.nn.functional as F
+from torch.distributions.bernoulli import Bernoulli as Bern
+import numpy as np
+from pdb import set_trace as st
+# from util import utils
+from .continuous_diffusion_utils import view4D
+
+@torch.jit.script
+def sample_normal_jit(mu, sigma):
+    rho = mu.mul(0).normal_()
+    z = rho.mul_(sigma).add_(mu)
+    return z, rho
+
+
+@torch.jit.script
+def log_p_standard_normal(samples):
+    log_p = - 0.5 * torch.square(samples) - 0.9189385332  # 0.5 * np.log(2 * np.pi)
+    return log_p
+
+
+def log_p_var_normal(samples, var):
+    log_p = - 0.5 * torch.square(samples) / var - 0.5 * np.log(var) - 0.9189385332  # 0.5 * np.log(2 * np.pi)
+    return log_p
+
+
+def one_hot(indices, depth, dim):
+    indices = indices.unsqueeze(dim)
+    size = list(indices.size())
+    size[dim] = depth
+    y_onehot = torch.zeros(size).cuda()
+    y_onehot.zero_()
+    y_onehot.scatter_(dim, indices, 1)
+
+    return y_onehot
+
+# TODO: merge this with the next class
+class PixelNormal(object):
+    def __init__(self, param, fixed_log_scales=None):
+        size = param.size()
+        C = size[1]
+        if fixed_log_scales is None:
+            self.num_c = C // 2
+            self.means = param[:, :self.num_c, :, :]                                    # B, 1 or 3, H, W
+            self.log_scales = torch.clamp(param[:, self.num_c:, :, :], min=-7.0)        # B, 1 or 3, H, W
+            raise NotImplementedError
+        else:
+            self.num_c = C
+            self.means = param                                                          # B, 1 or 3, H, W
+            self.log_scales = view4D(fixed_log_scales, size)                      # B, 1 or 3, H, W
+
+    def get_params(self):
+        return self.means, self.log_scales, self.num_c
+
+    def log_prob(self, samples):
+        B, C, H, W = samples.size()
+        assert C == self.num_c
+
+        log_probs = -0.5 * torch.square(self.means - samples) * torch.exp(-2.0 * self.log_scales) - self.log_scales - 0.9189385332  # -0.5*log(2*pi)
+        return log_probs
+
+    def sample(self, t=1.):
+        z, rho = sample_normal_jit(self.means, torch.exp(self.log_scales)*t)  # B, 3, H, W
+        return z
+
+    def log_prob_discrete(self, samples):
+        """
+        Calculates discrete pixel probabilities.
+        """
+        # samples should be in [-1, 1] already
+        B, C, H, W = samples.size()
+        assert C == self.num_c
+
+        centered = samples - self.means
+        inv_stdv = torch.exp(- self.log_scales)
+        plus_in = inv_stdv * (centered + 1. / 255.)
+        cdf_plus = torch.distributions.Normal(0, 1).cdf(plus_in)
+        min_in = inv_stdv * (centered - 1. / 255.)
+        cdf_min = torch.distributions.Normal(0, 1).cdf(min_in)
+        log_cdf_plus = torch.log(torch.clamp(cdf_plus, min=1e-12))
+        log_one_minus_cdf_min = torch.log(torch.clamp(1. - cdf_min, min=1e-12))
+        cdf_delta = cdf_plus - cdf_min
+        log_probs = torch.where(samples < -0.999, log_cdf_plus, torch.where(samples > 0.999, log_one_minus_cdf_min,
+            torch.log(torch.clamp(cdf_delta, min=1e-12))))
+
+        assert log_probs.size() == samples.size()
+        return log_probs
+
+    def mean(self):
+        return self.means
+
+
+class Normal:
+    def __init__(self, mu, log_sigma):
+        self.mu = mu
+        self.log_sigma = log_sigma
+        self.sigma = torch.exp(log_sigma)
+
+    def sample(self, t=1.):
+        return sample_normal_jit(self.mu, self.sigma * t)
+
+    def sample_given_rho(self, rho):
+        return rho * self.sigma + self.mu
+
+    def log_p(self, samples):
+        normalized_samples = (samples - self.mu) / self.sigma
+        log_p = - 0.5 * normalized_samples * normalized_samples - 0.5 * np.log(2 * np.pi) - self.log_sigma
+        return log_p
+
+    def kl(self, normal_dist):
+        term1 = (self.mu - normal_dist.mu) / normal_dist.sigma
+        term2 = self.sigma / normal_dist.sigma
+
+        return 0.5 * (term1 * term1 + term2 * term2) - 0.5 - torch.log(self.log_sigma) + normal_dist.log_sigma
+
+    def mean(self):
+        return self.mu
+
+
+class Bernoulli:
+    def __init__(self, logits):
+        self.dist = Bern(logits=logits)
+
+    def log_p(self, samples):
+        # convert samples to {0, 1}
+        samples = (samples + 1.) / 2
+        return self.dist.log_prob(samples)
+
+    def mean(self):
+        # map the mean to [-1, 1]
+        return 2 * self.dist.mean - 1.
+
+class DiscLogistic:
+    def __init__(self, param):
+        B, C, H, W = param.size()
+        self.num_c = C // 2
+        self.means = param[:, :self.num_c, :, :]                              # B, 3, H, W
+        self.log_scales = torch.clamp(param[:, self.num_c:, :, :], min=-7.0)  # B, 3, H, W
+
+    def log_p(self, samples):
+        assert torch.max(samples) <= 1.0 and torch.min(samples) >= -1.0
+
+        B, C, H, W = samples.size()
+        assert C == self.num_c
+
+        centered = samples - self.means                                         # B, 3, H, W
+        inv_stdv = torch.exp(- self.log_scales)
+        plus_in = inv_stdv * (centered + 1. / 255.)
+        cdf_plus = torch.sigmoid(plus_in)
+        min_in = inv_stdv * (centered - 1. / 255.)
+        cdf_min = torch.sigmoid(min_in)
+        log_cdf_plus = plus_in - F.softplus(plus_in)
+        log_one_minus_cdf_min = - F.softplus(min_in)
+        cdf_delta = cdf_plus - cdf_min
+        mid_in = inv_stdv * centered
+        log_pdf_mid = mid_in - self.log_scales - 2. * F.softplus(mid_in)
+
+        log_prob_mid_safe = torch.where(cdf_delta > 1e-5,
+                                        torch.log(torch.clamp(cdf_delta, min=1e-10)),
+                                        log_pdf_mid - np.log(127.5))
+
+        log_probs = torch.where(samples < -0.999, log_cdf_plus, torch.where(samples > 0.999, log_one_minus_cdf_min,
+                                                                            log_prob_mid_safe))   # B, 3, H, W
+
+        return log_probs
+
+    def sample(self):
+        u = torch.Tensor(self.means.size()).uniform_(1e-5, 1. - 1e-5).cuda()                        # B, 3, H, W
+        x = self.means + torch.exp(self.log_scales) * (torch.log(u) - torch.log(1. - u))            # B, 3, H, W
+        x = torch.clamp(x, -1, 1.)
+        return x
+
+    def mean(self):
+        return self.means
+
+
+class DiscMixLogistic:
+    def __init__(self, param, num_mix=10, num_bits=8):
+        B, C, H, W = param.size()
+        self.num_mix = num_mix
+        self.logit_probs = param[:, :num_mix, :, :]                                   # B, M, H, W
+        l = param[:, num_mix:, :, :].view(B, 3, 3 * num_mix, H, W)                    # B, 3, 3 * M, H, W
+        self.means = l[:, :, :num_mix, :, :]                                          # B, 3, M, H, W
+        self.log_scales = torch.clamp(l[:, :, num_mix:2 * num_mix, :, :], min=-7.0)   # B, 3, M, H, W
+        self.coeffs = torch.tanh(l[:, :, 2 * num_mix:3 * num_mix, :, :])              # B, 3, M, H, W
+        self.max_val = 2. ** num_bits - 1
+
+    def log_p(self, samples):
+        assert torch.max(samples) <= 1.0 and torch.min(samples) >= -1.0
+
+        B, C, H, W = samples.size()
+        assert C == 3, 'only RGB images are considered.'
+
+        samples = samples.unsqueeze(4)                                                  # B, 3, H , W
+        samples = samples.expand(-1, -1, -1, -1, self.num_mix).permute(0, 1, 4, 2, 3)   # B, 3, M, H, W
+        mean1 = self.means[:, 0, :, :, :]                                               # B, M, H, W
+        mean2 = self.means[:, 1, :, :, :] + \
+                self.coeffs[:, 0, :, :, :] * samples[:, 0, :, :, :]                     # B, M, H, W
+        mean3 = self.means[:, 2, :, :, :] + \
+                self.coeffs[:, 1, :, :, :] * samples[:, 0, :, :, :] + \
+                self.coeffs[:, 2, :, :, :] * samples[:, 1, :, :, :]                     # B, M, H, W
+
+        mean1 = mean1.unsqueeze(1)                          # B, 1, M, H, W
+        mean2 = mean2.unsqueeze(1)                          # B, 1, M, H, W
+        mean3 = mean3.unsqueeze(1)                          # B, 1, M, H, W
+        means = torch.cat([mean1, mean2, mean3], dim=1)     # B, 3, M, H, W
+        centered = samples - means                          # B, 3, M, H, W
+
+        inv_stdv = torch.exp(- self.log_scales)
+        plus_in = inv_stdv * (centered + 1. / self.max_val)
+        cdf_plus = torch.sigmoid(plus_in)
+        min_in = inv_stdv * (centered - 1. / self.max_val)
+        cdf_min = torch.sigmoid(min_in)
+        log_cdf_plus = plus_in - F.softplus(plus_in)
+        log_one_minus_cdf_min = - F.softplus(min_in)
+        cdf_delta = cdf_plus - cdf_min
+        mid_in = inv_stdv * centered
+        log_pdf_mid = mid_in - self.log_scales - 2. * F.softplus(mid_in)
+
+        log_prob_mid_safe = torch.where(cdf_delta > 1e-5,
+                                        torch.log(torch.clamp(cdf_delta, min=1e-10)),
+                                        log_pdf_mid - np.log(self.max_val / 2))
+
+        log_probs = torch.where(samples < -0.999, log_cdf_plus, torch.where(samples > 0.999, log_one_minus_cdf_min,
+                                                                            log_prob_mid_safe))   # B, 3, M, H, W
+
+        log_probs = torch.sum(log_probs, 1) + F.log_softmax(self.logit_probs, dim=1)         # B, M, H, W
+        return torch.logsumexp(log_probs, dim=1)                                             # B, H, W
+
+    def sample(self, t=1.):
+        gumbel = -torch.log(- torch.log(torch.Tensor(self.logit_probs.size()).uniform_(1e-5, 1. - 1e-5).cuda()))  # B, M, H, W
+        sel = one_hot(torch.argmax(self.logit_probs / t + gumbel, 1), self.num_mix, dim=1)          # B, M, H, W
+        sel = sel.unsqueeze(1)                                                                 # B, 1, M, H, W
+
+        # select logistic parameters
+        means = torch.sum(self.means * sel, dim=2)                                             # B, 3, H, W
+        log_scales = torch.sum(self.log_scales * sel, dim=2)                                   # B, 3, H, W
+        coeffs = torch.sum(self.coeffs * sel, dim=2)                                           # B, 3, H, W
+
+        # cells from logistic & clip to interval
+        # we don't actually round to the nearest 8bit value when sampling
+        u = torch.Tensor(means.size()).uniform_(1e-5, 1. - 1e-5).cuda()                        # B, 3, H, W
+        x = means + torch.exp(log_scales) * t * (torch.log(u) - torch.log(1. - u))             # B, 3, H, W
+
+        x0 = torch.clamp(x[:, 0, :, :], -1, 1.)                                                # B, H, W
+        x1 = torch.clamp(x[:, 1, :, :] + coeffs[:, 0, :, :] * x0, -1, 1)                       # B, H, W
+        x2 = torch.clamp(x[:, 2, :, :] + coeffs[:, 1, :, :] * x0 + coeffs[:, 2, :, :] * x1, -1, 1)  # B, H, W
+
+        x0 = x0.unsqueeze(1)
+        x1 = x1.unsqueeze(1)
+        x2 = x2.unsqueeze(1)
+
+        x = torch.cat([x0, x1, x2], 1)
+        return x
+
+    def mean(self):
+        sel = torch.softmax(self.logit_probs, dim=1)                                           # B, M, H, W
+        sel = sel.unsqueeze(1)                                                                 # B, 1, M, H, W
+
+        # select logistic parameters
+        means = torch.sum(self.means * sel, dim=2)                                             # B, 3, H, W
+        coeffs = torch.sum(self.coeffs * sel, dim=2)                                           # B, 3, H, W
+
+        # we don't sample from logistic components, because of the linear dependencies, we use mean
+        x = means                                                                              # B, 3, H, W
+        x0 = torch.clamp(x[:, 0, :, :], -1, 1.)                                                # B, H, W
+        x1 = torch.clamp(x[:, 1, :, :] + coeffs[:, 0, :, :] * x0, -1, 1)                       # B, H, W
+        x2 = torch.clamp(x[:, 2, :, :] + coeffs[:, 1, :, :] * x0 + coeffs[:, 2, :, :] * x1, -1, 1)  # B, H, W
+
+        x0 = x0.unsqueeze(1)
+        x1 = x1.unsqueeze(1)
+        x2 = x2.unsqueeze(1)
+
+        x = torch.cat([x0, x1, x2], 1)
+        return x
+
+
diff --git a/guided_diffusion/dist_util.py b/guided_diffusion/dist_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9208d42dcac29dc20101a867906d386406078ba
--- /dev/null
+++ b/guided_diffusion/dist_util.py
@@ -0,0 +1,170 @@
+"""
+Helpers for distributed training.
+"""
+
+import datetime
+import io
+import os
+import socket
+
+import blobfile as bf
+from pdb import set_trace as st
+# from mpi4py import MPI
+import torch as th
+import torch.distributed as dist
+
+# Change this to reflect your cluster layout.
+# The GPU for a given rank is (rank % GPUS_PER_NODE).
+GPUS_PER_NODE = 8
+SETUP_RETRY_COUNT = 3
+
+
+def get_rank():
+    if not dist.is_available():
+        return 0
+
+    if not dist.is_initialized():
+        return 0
+
+    return dist.get_rank()
+
+
+def synchronize():
+    if not dist.is_available():
+        return
+
+    if not dist.is_initialized():
+        return
+
+    world_size = dist.get_world_size()
+
+    if world_size == 1:
+        return
+
+    dist.barrier()
+
+
+def get_world_size():
+    if not dist.is_available():
+        return 1
+
+    if not dist.is_initialized():
+        return 1
+
+    return dist.get_world_size()
+
+
+def setup_dist(args):
+    """
+    Setup a distributed process group.
+    """
+    if dist.is_initialized():
+        return
+
+    # print(f"{os.environ['MASTER_ADDR']=} {args.master_port=}")
+
+    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count(),  timeout=datetime.timedelta(seconds=5400))
+    # st() no mark
+    dist.init_process_group(backend='nccl', init_method='env://', timeout=datetime.timedelta(seconds=54000))
+    print(f"{args.local_rank=} init complete")
+
+    # synchronize() # extra memory on rank 0, why?
+
+    th.cuda.empty_cache()
+
+def cleanup():
+    dist.destroy_process_group()
+
+def dev():
+    """
+    Get the device to use for torch.distributed.
+    """
+    if th.cuda.is_available():
+
+        if get_world_size() > 1:
+            return th.device(f"cuda:{get_rank() % GPUS_PER_NODE}")
+        return th.device(f"cuda")
+
+    return th.device("cpu")
+
+
+# def load_state_dict(path, submodule_name='', **kwargs):
+def load_state_dict(path, **kwargs):
+    """
+    Load a PyTorch file without redundant fetches across MPI ranks.
+    """
+    # chunk_size = 2 ** 30  # MPI has a relatively small size limit
+    # if get_rank() == 0:
+    #     with bf.BlobFile(path, "rb") as f:
+    #         data = f.read()
+    #     num_chunks = len(data) // chunk_size
+    #     if len(data) % chunk_size:
+    #         num_chunks += 1
+    #     MPI.COMM_WORLD.bcast(num_chunks)
+    #     for i in range(0, len(data), chunk_size):
+    #         MPI.COMM_WORLD.bcast(data[i : i + chunk_size])
+    # else:
+    #     num_chunks = MPI.COMM_WORLD.bcast(None)
+    #     data = bytes()
+    #     for _ in range(num_chunks):
+    #         data += MPI.COMM_WORLD.bcast(None)
+
+    # return th.load(io.BytesIO(data), **kwargs)
+    # with open(path) as f:
+    ckpt = th.load(path, **kwargs)
+    # if submodule_name != '':
+    #     assert submodule_name in ckpt
+    #     return ckpt[submodule_name]
+    # else:
+    return ckpt
+
+
+def sync_params(params):
+    """
+    Synchronize a sequence of Tensors across ranks from rank 0.
+    """
+    # for k, p in params:
+    for p in params:
+        with th.no_grad():
+            try:
+                dist.broadcast(p, 0)
+            except Exception as e:
+                print(k, e)
+                # print(e)
+
+
+def _find_free_port():
+    try:
+        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        s.bind(("", 0))
+        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        return s.getsockname()[1]
+    finally:
+        s.close()
+
+
+_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
+_reduce_dtype   = th.float32 # Data type to use for initial per-tensor reduction.
+_counter_dtype  = th.float64 # Data type to use for the internal counters.
+_rank           = 0             # Rank of the current process.
+_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
+_sync_called    = False         # Has _sync() been called yet?
+_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
+_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
+
+def init_multiprocessing(rank, sync_device):
+    r"""Initializes `torch_utils.training_stats` for collecting statistics
+    across multiple processes.
+    This function must be called after
+    `torch.distributed.init_process_group()` and before `Collector.update()`.
+    The call is not necessary if multi-process collection is not needed.
+    Args:
+        rank:           Rank of the current process.
+        sync_device:    PyTorch device to use for inter-process
+                        communication, or None to disable multi-process
+                        collection. Typically `torch.device('cuda', rank)`.
+    """
+    global _rank, _sync_device
+    assert not _sync_called
+    _rank = rank
+    _sync_device = sync_device
\ No newline at end of file
diff --git a/guided_diffusion/fp16_util.py b/guided_diffusion/fp16_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..c92ebf408c962141cda925ea8c096fde8d596376
--- /dev/null
+++ b/guided_diffusion/fp16_util.py
@@ -0,0 +1,326 @@
+"""
+Helpers to train with 16-bit precision.
+"""
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
+
+from . import logger
+
+INITIAL_LOG_LOSS_SCALE = 20.0
+
+
+def convert_module_to_f16(l):
+    """
+    Convert primitive modules to float16.
+    """
+    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        l.weight.data = l.weight.data.half()
+        if l.bias is not None:
+            l.bias.data = l.bias.data.half()
+
+
+def convert_module_to_f32(l):
+    """
+    Convert primitive modules to float32, undoing convert_module_to_f16().
+    """
+    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        l.weight.data = l.weight.data.float()
+        if l.bias is not None:
+            l.bias.data = l.bias.data.float()
+
+
+def make_master_params(param_groups_and_shapes):
+    """
+    Copy model parameters into a (differently-shaped) list of full-precision
+    parameters.
+    """
+    master_params = []
+    for param_group, shape in param_groups_and_shapes:
+        master_param = nn.Parameter(
+            _flatten_dense_tensors([
+                param.detach().float() for (_, param) in param_group
+            ]).view(shape))
+        master_param.requires_grad = True
+        master_params.append(master_param)
+    return master_params
+
+
+def model_grads_to_master_grads(param_groups_and_shapes, master_params):
+    """
+    Copy the gradients from the model parameters into the master parameters
+    from make_master_params().
+    """
+    for master_param, (param_group, shape) in zip(master_params,
+                                                  param_groups_and_shapes):
+        master_param.grad = _flatten_dense_tensors([
+            param_grad_or_zeros(param) for (_, param) in param_group
+        ]).view(shape)
+
+
+def master_params_to_model_params(param_groups_and_shapes, master_params):
+    """
+    Copy the master parameter data back into the model parameters.
+    """
+    # Without copying to a list, if a generator is passed, this will
+    # silently not copy any parameters.
+    for master_param, (param_group, _) in zip(master_params,
+                                              param_groups_and_shapes):
+        for (_, param), unflat_master_param in zip(
+                param_group,
+                unflatten_master_params(param_group, master_param.view(-1))):
+            param.detach().copy_(unflat_master_param)
+
+
+def unflatten_master_params(param_group, master_param):
+    return _unflatten_dense_tensors(master_param,
+                                    [param for (_, param) in param_group])
+
+
+def get_param_groups_and_shapes(named_model_params):
+    named_model_params = list(named_model_params)
+    scalar_vector_named_params = (
+        [(n, p) for (n, p) in named_model_params if p.ndim <= 1],
+        (-1),
+    )
+    matrix_named_params = (
+        [(n, p) for (n, p) in named_model_params if p.ndim > 1],
+        (1, -1),
+    )
+    return [scalar_vector_named_params, matrix_named_params]
+
+
+def master_params_to_state_dict(model, param_groups_and_shapes, master_params,
+                                use_fp16):
+    if use_fp16:
+        state_dict = model.state_dict()
+        for master_param, (param_group, _) in zip(master_params,
+                                                  param_groups_and_shapes):
+            for (name, _), unflat_master_param in zip(
+                    param_group,
+                    unflatten_master_params(param_group,
+                                            master_param.view(-1))):
+                assert name in state_dict
+                state_dict[name] = unflat_master_param
+    else:
+        state_dict = model.state_dict()
+        for i, (name, _value) in enumerate(model.named_parameters()):
+            assert name in state_dict
+            state_dict[name] = master_params[i]
+    return state_dict
+
+
+def state_dict_to_master_params(model, state_dict, use_fp16):
+    if use_fp16:
+        named_model_params = [(name, state_dict[name])
+                              for name, _ in model.named_parameters()]
+        param_groups_and_shapes = get_param_groups_and_shapes(
+            named_model_params)
+        master_params = make_master_params(param_groups_and_shapes)
+    else:
+        master_params = [
+            state_dict[name] for name, _ in model.named_parameters()
+        ]
+    return master_params
+
+
+def zero_master_grads(master_params):
+    for param in master_params:
+        param.grad = None
+
+
+def zero_grad(model_params):
+    for param in model_params:
+        # Taken from https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer.add_param_group
+        if param.grad is not None:
+            param.grad.detach_()
+            param.grad.zero_()
+
+
+def param_grad_or_zeros(param):
+    if param.grad is not None:
+        return param.grad.data.detach()
+    else:
+        return th.zeros_like(param)
+
+
+class MixedPrecisionTrainer:
+
+    def __init__(self,
+                 *,
+                 model,
+                 use_fp16=False,
+                 use_amp=False,
+                 fp16_scale_growth=1e-3,
+                 initial_lg_loss_scale=INITIAL_LOG_LOSS_SCALE,
+                 model_name='ddpm',
+                 submodule_name='',
+                 model_params=None,
+                 clip_grad_throld=1.0):
+        self.clip_grad_throld = clip_grad_throld
+        self.model_name = model_name
+        self.model = model
+        self.use_fp16 = use_fp16
+        self.use_amp = use_amp
+        if self.use_amp:
+            # https://github.com/pytorch/pytorch/issues/40497#issuecomment-1262373602
+            # https://github.com/pytorch/pytorch/issues/111739
+            # self.scaler = th.cuda.amp.GradScaler(enabled=use_amp,
+            #                                      init_scale=2**15,
+            #                                      growth_interval=100)
+
+            # https://discuss.pytorch.org/t/gradscaler-amp-causes-nan-weights-on-backward-pass/177542
+            self.scaler = th.cuda.amp.GradScaler(enabled=use_amp,
+                                                #  growth_factor=2.0,
+                                                 growth_factor=1.5, # slower growth
+                                                 backoff_factor=0.25, # faster backoff
+                                                 init_scale=2**16, # 
+                                                 growth_interval=2000)
+
+            logger.log(model_name, 'enables AMP to accelerate training')
+        else:
+            logger.log(model_name, 'not enables AMP to accelerate training')
+
+        self.fp16_scale_growth = fp16_scale_growth
+
+        self.model_params = list(self.model.parameters(
+        )) if model_params is None else list(model_params) if not isinstance(
+            model_params, list) else model_params
+        self.master_params = self.model_params
+        self.param_groups_and_shapes = None
+        self.lg_loss_scale = initial_lg_loss_scale
+
+        if self.use_fp16:
+            self.param_groups_and_shapes = get_param_groups_and_shapes(
+                self.model.named_parameters())
+            self.master_params = make_master_params(
+                self.param_groups_and_shapes)
+            self.model.convert_to_fp16()
+
+    def zero_grad(self):
+        zero_grad(self.model_params)
+
+    def backward(self, loss: th.Tensor, disable_amp=False, **kwargs):
+        """**kwargs: retain_graph=True
+        """
+        if self.use_fp16:
+            loss_scale = 2**self.lg_loss_scale
+            (loss * loss_scale).backward(**kwargs)
+        elif self.use_amp and not disable_amp:
+            self.scaler.scale(loss).backward(**kwargs)
+        else:
+            loss.backward(**kwargs)
+
+    # def optimize(self, opt: th.optim.Optimizer, clip_grad=False):
+    def optimize(self, opt: th.optim.Optimizer, clip_grad=True):
+        if self.use_fp16:
+            return self._optimize_fp16(opt)
+        elif self.use_amp:
+            return self._optimize_amp(opt, clip_grad)
+        else:
+            return self._optimize_normal(opt, clip_grad)
+
+    def _optimize_fp16(self, opt: th.optim.Optimizer):
+        logger.logkv_mean("lg_loss_scale", self.lg_loss_scale)
+        model_grads_to_master_grads(self.param_groups_and_shapes,
+                                    self.master_params)
+        grad_norm, param_norm = self._compute_norms(
+            grad_scale=2**self.lg_loss_scale)
+        if check_overflow(grad_norm):
+            self.lg_loss_scale -= 1
+            logger.log(
+                f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}")
+            zero_master_grads(self.master_params)
+            return False
+
+        logger.logkv_mean("grad_norm", grad_norm)
+        logger.logkv_mean("param_norm", param_norm)
+
+        for p in self.master_params:
+            p.grad.mul_(1.0 / (2**self.lg_loss_scale))
+        opt.step()
+        zero_master_grads(self.master_params)
+        master_params_to_model_params(self.param_groups_and_shapes,
+                                      self.master_params)
+        self.lg_loss_scale += self.fp16_scale_growth
+        return True
+
+    def _optimize_amp(self, opt: th.optim.Optimizer, clip_grad=False):
+        # https://pytorch.org/docs/stable/notes/amp_examples.html#gradient-clipping
+        assert clip_grad
+        # clip_grad = False # ! debugging
+        self.scaler.unscale_(opt)  # to calculate accurate gradients
+
+        if clip_grad:
+            th.nn.utils.clip_grad_norm_(  # type: ignore
+                self.master_params,
+                # 5.0,
+                self.clip_grad_throld,
+                norm_type=2,
+                error_if_nonfinite=False,
+                foreach=True,
+            )  # clip before compute_norm
+
+        grad_norm, param_norm = self._compute_norms()
+        logger.logkv_mean("grad_norm", grad_norm)
+        logger.logkv_mean("param_norm", param_norm)
+
+        self.scaler.step(opt)
+        self.scaler.update()
+        return True
+
+    def _optimize_normal(self,
+                         opt: th.optim.Optimizer,
+                         clip_grad: bool = False):
+
+        assert clip_grad
+        # st()
+        if clip_grad:
+            th.nn.utils.clip_grad_norm_(  # type: ignore
+                self.master_params,
+                # 5.0,
+                self.clip_grad_throld,
+                norm_type=2,
+                error_if_nonfinite=False,
+                foreach=True,
+            )  # clip before compute_norm
+
+        grad_norm, param_norm = self._compute_norms()
+        logger.logkv_mean("grad_norm", grad_norm)
+        logger.logkv_mean("param_norm", param_norm)
+        opt.step()
+        return True
+
+    def _compute_norms(self, grad_scale=1.0):
+        grad_norm = 0.0
+        param_norm = 0.0
+        for p in self.master_params:
+            with th.no_grad():
+                param_norm += th.norm(p, p=2, dtype=th.float32).item()**2
+                if p.grad is not None:
+                    grad_norm += th.norm(p.grad, p=2,
+                                         dtype=th.float32).item()**2
+        return np.sqrt(grad_norm) / grad_scale, np.sqrt(param_norm)
+
+    def master_params_to_state_dict(self, master_params, model=None):
+        if model is None:
+            model = self.model
+        return master_params_to_state_dict(model, self.param_groups_and_shapes,
+                                           master_params, self.use_fp16)
+
+    def state_dict_to_master_params(self, state_dict, model=None):
+        if model is None:
+            model = self.model
+        return state_dict_to_master_params(model, state_dict, self.use_fp16)
+
+    def state_dict_to_master_params_given_submodule_name(
+            self, state_dict, submodule_name):
+        return state_dict_to_master_params(getattr(self.model, submodule_name),
+                                           state_dict, self.use_fp16)
+
+
+def check_overflow(value):
+    return (value == float("inf")) or (value == -float("inf")) or (value
+                                                                   != value)
diff --git a/guided_diffusion/gaussian_diffusion.py b/guided_diffusion/gaussian_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..08b6c2ca19c392901e6d6144ea2404e899ad8ba8
--- /dev/null
+++ b/guided_diffusion/gaussian_diffusion.py
@@ -0,0 +1,1203 @@
+"""
+This code started out as a PyTorch port of Ho et al's diffusion models:
+https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
+
+Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
+"""
+
+from pdb import set_trace as st
+import enum
+import math
+
+import numpy as np
+import torch as th
+
+from .nn import mean_flat
+from .losses import normal_kl, discretized_gaussian_log_likelihood
+from . import dist_util
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":  # * used here
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        beta_start = scale * 0.0001
+        beta_end = scale * 0.02
+        return np.linspace(beta_start,
+                           beta_end,
+                           num_diffusion_timesteps,
+                           dtype=np.float64)
+
+    elif schedule_name == "linear_simple":
+        return betas_for_alpha_bar_linear_simple(num_diffusion_timesteps,
+                                                 lambda t: 0.001 / (1.001 - t))
+
+    elif schedule_name == "cosine":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2)**2,
+        )
+
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar_linear_simple(num_diffusion_timesteps,
+                                      alpha_bar,
+                                      max_beta=0.999):
+    """proposed by Chen Ting, on the importance of noise schedule, arXiv 2023. 
+    gamma = 1-t
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t = i / num_diffusion_timesteps
+        betas.append(min(max_beta, alpha_bar(t)))
+
+    return betas
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+class ModelMeanType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
+    START_X = enum.auto()  # the model predicts x_0
+    EPSILON = enum.auto()  # the model predicts epsilon
+    V = enum.auto()  # the model predicts velosity
+
+
+class ModelVarType(enum.Enum):
+    """
+    What is used as the model's output variance.
+
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    LEARNED = enum.auto()
+    FIXED_SMALL = enum.auto()
+    FIXED_LARGE = enum.auto()
+    LEARNED_RANGE = enum.auto()
+
+
+class LossType(enum.Enum):
+    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
+    RESCALED_MSE = (
+        enum.auto()
+    )  # use raw MSE loss (with RESCALED_KL when learning variances)
+    KL = enum.auto()  # use the variational lower-bound
+    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
+
+    def is_vb(self):
+        return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+class GaussianDiffusion:
+    """
+    Utilities for training and sampling diffusion models.
+
+    Ported directly from here, and then adapted over time to further experimentation.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    :param model_mean_type: a ModelMeanType determining what the model outputs.
+    :param model_var_type: a ModelVarType determining how variance is output.
+    :param loss_type: a LossType determining the loss function to use.
+    :param rescale_timesteps: if True, pass floating point timesteps into the
+                              model so that they are always scaled like in the
+                              original paper (0 to 1000).
+    """
+    '''
+    defaults: 
+        learn_sigma=False,
+        diffusion_steps=1000,
+        noise_schedule="linear",
+        timestep_respacing="",
+        use_kl=False,
+        predict_xstart=False,
+        rescale_timesteps=False,
+        rescale_learned_sigmas=False, 
+    '''
+
+    def __init__(
+        self,
+        *,
+        betas,
+        model_mean_type,
+        model_var_type,
+        loss_type,
+        rescale_timesteps=False,
+        standarization_xt=False,
+    ):
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        self.loss_type = loss_type
+        self.rescale_timesteps = rescale_timesteps
+        self.standarization_xt = standarization_xt
+
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps, )
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(
+            1.0 / self.alphas_cumprod -
+            1)  # sqrt(1/cumprod(alphas) - 1), for calculating x_0 from x_t
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (betas * (1.0 - self.alphas_cumprod_prev) /
+                                   (1.0 - self.alphas_cumprod))
+        # log calculation clipped because the posterior variance is 0 at the
+        # beginning of the diffusion chain.
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:]))
+        self.posterior_mean_coef1 = (betas *
+                                     np.sqrt(self.alphas_cumprod_prev) /
+                                     (1.0 - self.alphas_cumprod))
+        self.posterior_mean_coef2 = ((1.0 - self.alphas_cumprod_prev) *
+                                     np.sqrt(alphas) /
+                                     (1.0 - self.alphas_cumprod))
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) *
+            x_start)
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t,
+                                        x_start.shape)
+        log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod,
+                                            t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None, return_detail=False):
+        """
+        Diffuse the data for a given number of diffusion steps.
+
+        In other words, sample from q(x_t | x_0).
+
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        alpha_bar = _extract_into_tensor(self.sqrt_alphas_cumprod, t,
+                                         x_start.shape)
+        one_minus_alpha_bar = _extract_into_tensor(
+            self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
+        xt = (alpha_bar * x_start + one_minus_alpha_bar * noise)
+
+        if self.standarization_xt:
+            xt = xt / (1e-5 + xt.std(dim=list(range(1, xt.ndim)), keepdim=True)
+                       )  # B 1 1 1 #
+
+        if return_detail:
+            return xt, alpha_bar, one_minus_alpha_bar
+
+        return xt
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+
+            q(x_{t-1} | x_t, x_0)
+
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) *
+            x_start +
+            _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) *
+            x_t)
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t,
+                                                  x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] ==
+                posterior_log_variance_clipped.shape[0] == x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self,
+                        model,
+                        x,
+                        t,
+                        c=None,
+                        clip_denoised=True,
+                        denoised_fn=None,
+                        model_kwargs=None,
+                        mixing_normal=False, 
+                        direct_return_model_output=False):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        # lazy import to avoid partially initialized import
+        from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction
+
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        # if mixing_normal is not None:
+            # t = t / self.num_timesteps  # [0,1] for SDE diffusion
+
+        B, C = x.shape[:2]
+        assert t.shape == (B, )
+        model_output = model(x, self._scale_timesteps(t), c=c, mixing_normal=mixing_normal, **model_kwargs)
+        if direct_return_model_output:
+            return model_output
+
+        if self.model_mean_type == ModelMeanType.V:
+            v_transformed_to_eps_flag = False
+
+        if mixing_normal:  # directly change the model predicted eps logits
+            if self.model_mean_type == ModelMeanType.START_X:
+                mixing_component = self.get_mixing_component_x0(x, t, enabled=True)
+            else:
+                assert self.model_mean_type in [ModelMeanType.EPSILON, ModelMeanType.V]
+                mixing_component = self.get_mixing_component(x, t, enabled=True)
+
+                if self.model_mean_type == ModelMeanType.V:
+                    model_output = self._predict_eps_from_z_and_v(x, t, model_output)
+                    v_transformed_to_eps_flag = True
+            # ! transform result to v first?
+            # model_output = 
+            model_output = get_mixed_prediction(True,
+                                                model_output,
+                                                model.mixing_logit,
+                                                mixing_component)
+        else:
+            # st()
+            if self.model_mean_type == ModelMeanType.V:
+                model_output = self._predict_eps_from_z_and_v(x, t, model_output)
+                v_transformed_to_eps_flag = True
+
+        if self.model_var_type in [
+                ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE
+        ]:
+            assert model_output.shape == (B, C * 2, *x.shape[2:])
+            model_output, model_var_values = th.split(model_output, C, dim=1)
+            if self.model_var_type == ModelVarType.LEARNED:
+                model_log_variance = model_var_values
+                model_variance = th.exp(model_log_variance)
+            else:
+                min_log = _extract_into_tensor(
+                    self.posterior_log_variance_clipped, t, x.shape)
+                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+                # The model_var_values is [-1, 1] for [min_var, max_var].
+                frac = (model_var_values + 1) / 2
+                model_log_variance = frac * max_log + (1 - frac) * min_log
+                model_variance = th.exp(model_log_variance)
+        else:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                # ?
+                ModelVarType.FIXED_LARGE: (  # * used here
+                    np.append(self.posterior_variance[1], self.betas[1:]),
+                    np.log(
+                        np.append(self.posterior_variance[1], self.betas[1:])),
+                ),
+                ModelVarType.FIXED_SMALL: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t,
+                                                      x.shape)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+            pred_xstart = process_xstart(
+                self._predict_xstart_from_xprev(x_t=x, t=t,
+                                                xprev=model_output))
+            model_mean = model_output
+        elif self.model_mean_type in [
+                ModelMeanType.START_X, ModelMeanType.EPSILON, ModelMeanType.V
+        ]:
+            if self.model_mean_type == ModelMeanType.START_X:
+                pred_xstart = process_xstart(model_output)
+            else:  # * used here
+                if self.model_mean_type == ModelMeanType.V:
+                    assert v_transformed_to_eps_flag # type: ignore
+                pred_xstart = process_xstart(  # * return the x_0 using self._predict_xstart_from_eps as the denoised_fn
+                    self._predict_xstart_from_eps(x_t=x, t=t,
+                                                  eps=model_output))
+            model_mean, _, _ = self.q_posterior_mean_variance(
+                x_start=pred_xstart, x_t=x, t=t)
+        else:
+            raise NotImplementedError(self.model_mean_type)
+
+        assert (model_mean.shape == model_log_variance.shape ==
+                pred_xstart.shape == x.shape)
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t,
+                                     x_t.shape) * x_t -
+                _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t,
+                                     x_t.shape) * eps)
+
+    def _predict_xstart_from_xprev(self, x_t, t, xprev):
+        assert x_t.shape == xprev.shape
+        return (  # (xprev - coef2*x_t) / coef1
+            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape)
+            * xprev - _extract_into_tensor(
+                self.posterior_mean_coef2 / self.posterior_mean_coef1, t,
+                x_t.shape) * x_t)
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t,
+                                     x_t.shape) * x_t -
+                pred_xstart) / _extract_into_tensor(
+                    self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    # https://github.com/Stability-AI/stablediffusion/blob/cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf/ldm/models/diffusion/ddpm.py#L288
+    def _predict_start_from_z_and_v(self, x_t, t, v):
+        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        return (
+                _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+                _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+        )
+
+    def _predict_eps_from_z_and_v(self, x_t, t, v):
+        return (
+                _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+                _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+        )
+
+    def _scale_timesteps(self, t):
+        if self.rescale_timesteps:
+            return t.float() * (1000.0 / self.num_timesteps)
+        return t
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
+        new_mean = (p_mean_var["mean"].float() +
+                    p_mean_var["variance"] * gradient.float())
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+
+        See condition_mean() for details on cond_fn.
+
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
+            x, self._scale_timesteps(t), **model_kwargs)
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(
+            x_start=out["pred_xstart"], x_t=x, t=t)
+        return out
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        cond=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        mixing_normal=False,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(model,
+                                   x,
+                                   t,
+                                   c=cond,
+                                   clip_denoised=clip_denoised,
+                                   denoised_fn=denoised_fn,
+                                   model_kwargs=model_kwargs,
+                                   mixing_normal=mixing_normal)
+        noise = th.randn_like(x)
+        nonzero_mask = ((t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+                        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(cond_fn,
+                                              out,
+                                              x,
+                                              t,
+                                              model_kwargs=model_kwargs)
+        sample = out["mean"] + nonzero_mask * th.exp(
+            0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def get_mixing_component(self, x_noisy, t, enabled):
+        # alpha_bars = th.gather(self._alpha_bars, 0, timestep-1)
+        if enabled:
+            # one_minus_alpha_bars_sqrt = utils.view4D(th.sqrt(1.0 - alpha_bars), size)
+            one_minus_alpha_bars_sqrt = _extract_into_tensor(
+                self.sqrt_one_minus_alphas_cumprod, t, x_noisy.shape)
+            mixing_component = one_minus_alpha_bars_sqrt * x_noisy
+        else:
+            mixing_component = None
+
+        return mixing_component
+
+    def get_mixing_component_x0(self, x_noisy, t, enabled):
+        # alpha_bars = th.gather(self._alpha_bars, 0, timestep-1)
+        if enabled:
+            # one_minus_alpha_bars_sqrt = utils.view4D(th.sqrt(1.0 - alpha_bars), size)
+            one_minus_alpha_bars_sqrt = _extract_into_tensor(
+                self.sqrt_alphas_cumprod, t, x_noisy.shape)
+            mixing_component = one_minus_alpha_bars_sqrt * x_noisy
+        else:
+            mixing_component = None
+
+        return mixing_component
+
+    def p_sample_mixing_component(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+
+        assert self.model_mean_type == ModelMeanType.EPSILON, 'currently LSGM only implemented for EPSILON prediction'
+
+        out = self.p_mean_variance(
+            model,
+            x,
+            t / self.
+            num_timesteps,  # trained on SDE diffusion, normalize steps to (0,1]
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        # mixing_component = self.get_mixing_component(x, t, enabled=True)
+        # out['mean'] = get_mixed_prediction(model.mixed_prediction, out['mean'], model.mixing_logit, mixing_component)
+
+        noise = th.randn_like(x)
+        nonzero_mask = ((t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+                        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(cond_fn,
+                                              out,
+                                              x,
+                                              t,
+                                              model_kwargs=model_kwargs)
+        sample = out["mean"] + nonzero_mask * th.exp(
+            0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        cond=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        mixing_normal=False,
+    ):
+        """
+        Generate samples from the model.
+
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+                model,
+                shape,
+                cond=cond,
+                noise=noise,
+                clip_denoised=clip_denoised,
+                denoised_fn=denoised_fn,
+                cond_fn=cond_fn,
+                model_kwargs=model_kwargs,
+                device=device,
+                progress=progress,
+                mixing_normal=mixing_normal):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        cond=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        mixing_normal=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        if device is None:
+            device = dist_util.dev()
+        #     device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(model,
+                                    img,
+                                    t,
+                                    cond=cond,
+                                    clip_denoised=clip_denoised,
+                                    denoised_fn=denoised_fn,
+                                    cond_fn=cond_fn,
+                                    model_kwargs=model_kwargs,
+                                    mixing_normal=mixing_normal)
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        cond=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+        unconditional_guidance_scale=1., 
+        unconditional_conditioning=None,
+        mixing_normal=False,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+
+        Same usage as p_sample().
+        """
+
+        if unconditional_guidance_scale != 1.0:
+            assert cond is not None
+            if unconditional_conditioning is None:
+                unconditional_conditioning = {
+                    k: th.zeros_like(cond[k]) for k in cond.keys()
+                } 
+                # ImageEmbedding adopts zero as the null embedding
+        # st()
+
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            # e_t = self.model.apply_model(x, t, c)
+            
+            out = self.p_mean_variance(
+                model,
+                x,
+                t,
+                c=cond,
+                clip_denoised=clip_denoised,
+                denoised_fn=denoised_fn,
+                model_kwargs=model_kwargs,
+                mixing_normal=mixing_normal,
+            )
+            eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        else:
+            assert cond is not None
+            x_in = th.cat([x] * 2)
+            t_in = th.cat([t] * 2)
+            c_in = {}
+            for k in cond:
+                c_in[k] = th.cat([unconditional_conditioning[k], cond[k]]) 
+
+            model_uncond, model_t = self.p_mean_variance(
+                model,
+                x_in,
+                t_in,
+                c=c_in,
+                clip_denoised=clip_denoised,
+                denoised_fn=denoised_fn,
+                model_kwargs=model_kwargs,
+                mixing_normal=mixing_normal,
+                direct_return_model_output=True, # ! compat with _wrapper
+            ).chunk(2)
+            # Usually our model outputs epsilon, but we re-derive it
+            # model_uncond, model_t = model(x_in, self._scale_timesteps(t_in), c=c_in, mixing_normal=mixing_normal, **model_kwargs).chunk(2)
+
+            # in case we used x_start or x_prev prediction.
+            # st()
+
+            # ! guidance
+            # e_t_uncond, e_t = eps.chunk(2)
+            model_out = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+            if self.model_mean_type == ModelMeanType.V:
+                eps = self._predict_eps_from_z_and_v(x, t, model_out)
+
+            # eps = self._predict_eps_from_xstart(x_in, t_in, out["pred_xstart"])
+
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn,
+                                       out,
+                                       x,
+                                       t,
+                                       model_kwargs=model_kwargs)
+
+        # eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"]) 
+        # ! re-derive xstart
+        pred_x0 = self._predict_xstart_from_eps(x, t, eps)
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t,
+                                              x.shape)
+        sigma = (eta * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) *
+                 th.sqrt(1 - alpha_bar / alpha_bar_prev))
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (pred_x0 * th.sqrt(alpha_bar_prev) +
+                     th.sqrt(1 - alpha_bar_prev - sigma**2) * eps)
+        nonzero_mask = ((t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+                        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": pred_x0}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape)
+               * x - out["pred_xstart"]) / _extract_into_tensor(
+                   self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t,
+                                              x.shape)
+
+        # Equation 12. reversed
+        mean_pred = (out["pred_xstart"] * th.sqrt(alpha_bar_next) +
+                     th.sqrt(1 - alpha_bar_next) * eps)
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        cond=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+        mixing_normal=False,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+    ):
+        """
+        Generate samples from the model using DDIM.
+
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+                model,
+                shape,
+                cond=cond,
+                noise=noise,
+                clip_denoised=clip_denoised,
+                denoised_fn=denoised_fn,
+                cond_fn=cond_fn,
+                model_kwargs=model_kwargs,
+                device=device,
+                progress=progress,
+                eta=eta,mixing_normal=mixing_normal,
+                unconditional_guidance_scale=unconditional_guidance_scale,
+                unconditional_conditioning=unconditional_conditioning,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        cond=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+        mixing_normal=False,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    cond=cond,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                    mixing_normal=mixing_normal,
+                    unconditional_guidance_scale=unconditional_guidance_scale,
+                    unconditional_conditioning=unconditional_conditioning,
+                )
+                yield out
+                img = out["sample"]
+
+    def _vb_terms_bpd(self,
+                      model,
+                      x_start,
+                      x_t,
+                      t,
+                      clip_denoised=True,
+                      model_kwargs=None):
+        """
+        Get a term for the variational lower-bound.
+
+        The resulting units are bits (rather than nats, as one might expect).
+        This allows for comparison to other papers.
+
+        :return: a dict with the following keys:
+                 - 'output': a shape [N] tensor of NLLs or KLs.
+                 - 'pred_xstart': the x_0 predictions.
+        """
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+            x_start=x_start, x_t=x_t, t=t)
+        out = self.p_mean_variance(model,
+                                   x_t,
+                                   t,
+                                   clip_denoised=clip_denoised,
+                                   model_kwargs=model_kwargs)
+        kl = normal_kl(true_mean, true_log_variance_clipped, out["mean"],
+                       out["log_variance"])
+        kl = mean_flat(kl) / np.log(2.0)
+
+        decoder_nll = -discretized_gaussian_log_likelihood(
+            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"])
+        assert decoder_nll.shape == x_start.shape
+        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+        # At the first timestep return the decoder NLL,
+        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+        output = th.where((t == 0), decoder_nll, kl)
+        return {"output": output, "pred_xstart": out["pred_xstart"]}
+
+    def training_losses(self,
+                        model,
+                        x_start,
+                        t,
+                        model_kwargs=None,
+                        noise=None,
+                        return_detail=False):
+        """
+        Compute training losses for a single timestep.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+        if model_kwargs is None:  # * micro_cond
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start)  # x_start is the x0 image
+        x_t = self.q_sample(x_start,
+                            t,
+                            noise=noise,
+                            return_detail=return_detail
+                            )  # * add noise according to predefined schedule
+        if return_detail:
+            x_t, alpha_bar, _ = x_t
+
+        # terms = {}
+        terms = {"x_t": x_t}
+
+        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+            terms["loss"] = self._vb_terms_bpd(
+                model=model,
+                x_start=x_start,
+                x_t=x_t,
+                t=t,
+                clip_denoised=False,
+                model_kwargs=model_kwargs,
+            )["output"]
+            if self.loss_type == LossType.RESCALED_KL:
+                terms["loss"] *= self.num_timesteps
+        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_output = model(
+                x_t, self._scale_timesteps(t), **model_kwargs
+            )  # directly predict epsilon or x_0; no learned sigma
+
+            if self.model_var_type in [
+                    ModelVarType.LEARNED,
+                    ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
+                model_output, model_var_values = th.split(model_output,
+                                                          C,
+                                                          dim=1)
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values],
+                                    dim=1)
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            target = {
+                ModelMeanType.PREVIOUS_X:
+                self.q_posterior_mean_variance(x_start=x_start, x_t=x_t,
+                                               t=t)[0],
+                ModelMeanType.START_X:
+                x_start,
+                ModelMeanType.EPSILON:
+                noise,
+            }[self.model_mean_type]  # ModelMeanType.EPSILON
+            # st()
+            assert model_output.shape == target.shape == x_start.shape
+            terms["mse"] = mean_flat((target - model_output)**2)
+
+            terms['model_output'] = model_output
+            # terms['target'] = target # TODO, flag.
+            if return_detail:
+                terms.update({
+                    'diffusion_target': target,
+                    'alpha_bar': alpha_bar,
+                    # 'one_minus_alpha':one_minus_alpha
+                    # 'noise': noise
+                })
+
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+
+        This term can't be optimized, as it only depends on the encoder.
+
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = th.tensor([self.num_timesteps - 1] * batch_size,
+                      device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean,
+                             logvar1=qt_log_variance,
+                             mean2=0.0,
+                             logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def calc_bpd_loop(self,
+                      model,
+                      x_start,
+                      clip_denoised=True,
+                      model_kwargs=None):
+        """
+        Compute the entire variational lower-bound, measured in bits-per-dim,
+        as well as other related quantities.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param clip_denoised: if True, clip denoised samples.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+
+        :return: a dict containing the following keys:
+                 - total_bpd: the total variational lower-bound, per batch element.
+                 - prior_bpd: the prior term in the lower-bound.
+                 - vb: an [N x T] tensor of terms in the lower-bound.
+                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+        """
+        device = x_start.device
+        batch_size = x_start.shape[0]
+
+        vb = []
+        xstart_mse = []
+        mse = []
+        for t in list(range(self.num_timesteps))[::-1]:
+            t_batch = th.tensor([t] * batch_size, device=device)
+            noise = th.randn_like(x_start)
+            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+            # Calculate VLB term at the current timestep
+            with th.no_grad():
+                out = self._vb_terms_bpd(
+                    model,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t_batch,
+                    clip_denoised=clip_denoised,
+                    model_kwargs=model_kwargs,
+                )
+            vb.append(out["output"])
+            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start)**2))
+            eps = self._predict_eps_from_xstart(x_t, t_batch,
+                                                out["pred_xstart"])
+            mse.append(mean_flat((eps - noise)**2))
+
+        vb = th.stack(vb, dim=1)
+        xstart_mse = th.stack(xstart_mse, dim=1)
+        mse = th.stack(mse, dim=1)
+
+        prior_bpd = self._prior_bpd(x_start)
+        total_bpd = vb.sum(dim=1) + prior_bpd
+        return {
+            "total_bpd": total_bpd,
+            "prior_bpd": prior_bpd,
+            "vb": vb,
+            "xstart_mse": xstart_mse,
+            "mse": mse,
+        }
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res.expand(broadcast_shape)
diff --git a/guided_diffusion/image_datasets.py b/guided_diffusion/image_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..93022ae208a01e72eb162d7b63c07bf94a6afe3b
--- /dev/null
+++ b/guided_diffusion/image_datasets.py
@@ -0,0 +1,167 @@
+import math
+import random
+
+from PIL import Image
+import blobfile as bf
+from mpi4py import MPI
+import numpy as np
+from torch.utils.data import DataLoader, Dataset
+
+
+def load_data(
+    *,
+    data_dir,
+    batch_size,
+    image_size,
+    class_cond=False,
+    deterministic=False,
+    random_crop=False,
+    random_flip=True,
+):
+    """
+    For a dataset, create a generator over (images, kwargs) pairs.
+
+    Each images is an NCHW float tensor, and the kwargs dict contains zero or
+    more keys, each of which map to a batched Tensor of their own.
+    The kwargs dict can be used for class labels, in which case the key is "y"
+    and the values are integer tensors of class labels.
+
+    :param data_dir: a dataset directory.
+    :param batch_size: the batch size of each returned pair.
+    :param image_size: the size to which images are resized.
+    :param class_cond: if True, include a "y" key in returned dicts for class
+                       label. If classes are not available and this is true, an
+                       exception will be raised.
+    :param deterministic: if True, yield results in a deterministic order.
+    :param random_crop: if True, randomly crop the images for augmentation.
+    :param random_flip: if True, randomly flip the images for augmentation.
+    """
+    if not data_dir:
+        raise ValueError("unspecified data directory")
+    all_files = _list_image_files_recursively(data_dir)
+    classes = None
+    if class_cond:
+        # Assume classes are the first part of the filename,
+        # before an underscore.
+        class_names = [bf.basename(path).split("_")[0] for path in all_files]
+        sorted_classes = {x: i for i, x in enumerate(sorted(set(class_names)))}
+        classes = [sorted_classes[x] for x in class_names]
+    dataset = ImageDataset(
+        image_size,
+        all_files,
+        classes=classes,
+        shard=MPI.COMM_WORLD.Get_rank(),
+        num_shards=MPI.COMM_WORLD.Get_size(),
+        random_crop=random_crop,
+        random_flip=random_flip,
+    )
+    if deterministic:
+        loader = DataLoader(
+            dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True
+        )
+    else:
+        loader = DataLoader(
+            dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True
+        )
+    while True:
+        yield from loader
+
+
+def _list_image_files_recursively(data_dir):
+    results = []
+    for entry in sorted(bf.listdir(data_dir)):
+        full_path = bf.join(data_dir, entry)
+        ext = entry.split(".")[-1]
+        if "." in entry and ext.lower() in ["jpg", "jpeg", "png", "gif"]:
+            results.append(full_path)
+        elif bf.isdir(full_path):
+            results.extend(_list_image_files_recursively(full_path))
+    return results
+
+
+class ImageDataset(Dataset):
+    def __init__(
+        self,
+        resolution,
+        image_paths,
+        classes=None,
+        shard=0,
+        num_shards=1,
+        random_crop=False,
+        random_flip=True,
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.local_images = image_paths[shard:][::num_shards]
+        self.local_classes = None if classes is None else classes[shard:][::num_shards]
+        self.random_crop = random_crop
+        self.random_flip = random_flip
+
+    def __len__(self):
+        return len(self.local_images)
+
+    def __getitem__(self, idx):
+        path = self.local_images[idx]
+        with bf.BlobFile(path, "rb") as f:
+            pil_image = Image.open(f)
+            pil_image.load()
+        pil_image = pil_image.convert("RGB")
+
+        if self.random_crop:
+            arr = random_crop_arr(pil_image, self.resolution)
+        else:
+            arr = center_crop_arr(pil_image, self.resolution)
+
+        if self.random_flip and random.random() < 0.5:
+            arr = arr[:, ::-1]
+
+        arr = arr.astype(np.float32) / 127.5 - 1
+
+        out_dict = {}
+        if self.local_classes is not None:
+            out_dict["y"] = np.array(self.local_classes[idx], dtype=np.int64)
+        return np.transpose(arr, [2, 0, 1]), out_dict
+
+
+def center_crop_arr(pil_image, image_size):
+    # We are not on a new enough PIL to support the `reducing_gap`
+    # argument, which uses BOX downsampling at powers of two first.
+    # Thus, we do it by hand to improve downsample quality.
+    while min(*pil_image.size) >= 2 * image_size:
+        pil_image = pil_image.resize(
+            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
+        )
+
+    scale = image_size / min(*pil_image.size)
+    pil_image = pil_image.resize(
+        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
+    )
+
+    arr = np.array(pil_image)
+    crop_y = (arr.shape[0] - image_size) // 2
+    crop_x = (arr.shape[1] - image_size) // 2
+    return arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
+
+
+def random_crop_arr(pil_image, image_size, min_crop_frac=0.8, max_crop_frac=1.0):
+    min_smaller_dim_size = math.ceil(image_size / max_crop_frac)
+    max_smaller_dim_size = math.ceil(image_size / min_crop_frac)
+    smaller_dim_size = random.randrange(min_smaller_dim_size, max_smaller_dim_size + 1)
+
+    # We are not on a new enough PIL to support the `reducing_gap`
+    # argument, which uses BOX downsampling at powers of two first.
+    # Thus, we do it by hand to improve downsample quality.
+    while min(*pil_image.size) >= 2 * smaller_dim_size:
+        pil_image = pil_image.resize(
+            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
+        )
+
+    scale = smaller_dim_size / min(*pil_image.size)
+    pil_image = pil_image.resize(
+        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
+    )
+
+    arr = np.array(pil_image)
+    crop_y = random.randrange(arr.shape[0] - image_size + 1)
+    crop_x = random.randrange(arr.shape[1] - image_size + 1)
+    return arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
diff --git a/guided_diffusion/logger.py b/guided_diffusion/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bcc37050913426603e230fabda3c18a0f9a7a47
--- /dev/null
+++ b/guided_diffusion/logger.py
@@ -0,0 +1,508 @@
+"""
+Logger copied from OpenAI baselines to avoid extra RL-based dependencies:
+https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/logger.py
+"""
+
+import os
+import sys
+import shutil
+import os.path as osp
+import json
+import time
+import datetime
+import tempfile
+import warnings
+from collections import defaultdict
+from contextlib import contextmanager
+from pdb import set_trace as st
+
+DEBUG = 10
+INFO = 20
+WARN = 30
+ERROR = 40
+
+DISABLED = 50
+
+
+class KVWriter(object):
+    def writekvs(self, kvs):
+        raise NotImplementedError
+
+
+class SeqWriter(object):
+    def writeseq(self, seq):
+        raise NotImplementedError
+
+
+class HumanOutputFormat(KVWriter, SeqWriter):
+    def __init__(self, filename_or_file):
+        if isinstance(filename_or_file, str):
+            self.file = open(filename_or_file, "wt")
+            self.own_file = True
+        else:
+            assert hasattr(filename_or_file, "read"), (
+                "expected file or str, got %s" % filename_or_file
+            )
+            self.file = filename_or_file
+            self.own_file = False
+
+    def writekvs(self, kvs):
+        # Create strings for printing
+        key2str = {}
+        for (key, val) in sorted(kvs.items()):
+            if hasattr(val, "__float__"):
+                valstr = "%-8.3g" % val
+            else:
+                valstr = str(val)
+            key2str[self._truncate(key)] = self._truncate(valstr)
+
+        # Find max widths
+        if len(key2str) == 0:
+            print("WARNING: tried to write empty key-value dict")
+            return
+        else:
+            keywidth = max(map(len, key2str.keys()))
+            valwidth = max(map(len, key2str.values()))
+
+        # Write out the data
+        dashes = "-" * (keywidth + valwidth + 7)
+        lines = [dashes]
+        for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()):
+            lines.append(
+                "| %s%s | %s%s |"
+                % (key, " " * (keywidth - len(key)), val, " " * (valwidth - len(val)))
+            )
+        lines.append(dashes)
+        self.file.write("\n".join(lines) + "\n")
+
+        # Flush the output to the file
+        self.file.flush()
+
+    def _truncate(self, s):
+        maxlen = 30
+        return s[: maxlen - 3] + "..." if len(s) > maxlen else s
+
+    def writeseq(self, seq):
+        seq = list(seq)
+        for (i, elem) in enumerate(seq):
+            self.file.write(elem)
+            if i < len(seq) - 1:  # add space unless this is the last one
+                self.file.write(" ")
+        self.file.write("\n")
+        self.file.flush()
+
+    def close(self):
+        if self.own_file:
+            self.file.close()
+
+
+class JSONOutputFormat(KVWriter):
+    def __init__(self, filename):
+        self.file = open(filename, "wt")
+
+    def writekvs(self, kvs):
+        for k, v in sorted(kvs.items()):
+            if hasattr(v, "dtype"):
+                kvs[k] = float(v)
+        self.file.write(json.dumps(kvs) + "\n")
+        self.file.flush()
+
+    def close(self):
+        self.file.close()
+
+
+class CSVOutputFormat(KVWriter):
+    def __init__(self, filename):
+        self.file = open(filename, "w+t")
+        self.keys = []
+        self.sep = ","
+
+    def writekvs(self, kvs):
+        # Add our current row to the history
+        extra_keys = list(kvs.keys() - self.keys)
+        extra_keys.sort()
+        if extra_keys:
+            self.keys.extend(extra_keys)
+            self.file.seek(0)
+            lines = self.file.readlines()
+            self.file.seek(0)
+            for (i, k) in enumerate(self.keys):
+                if i > 0:
+                    self.file.write(",")
+                self.file.write(k)
+            self.file.write("\n")
+            for line in lines[1:]:
+                self.file.write(line[:-1])
+                self.file.write(self.sep * len(extra_keys))
+                self.file.write("\n")
+        for (i, k) in enumerate(self.keys):
+            if i > 0:
+                self.file.write(",")
+            v = kvs.get(k)
+            if v is not None:
+                self.file.write(str(v))
+        self.file.write("\n")
+        self.file.flush()
+
+    def close(self):
+        self.file.close()
+
+
+class TensorBoardOutputFormat(KVWriter):
+    """
+    Dumps key/value pairs into TensorBoard's numeric format.
+    """
+
+    def __init__(self, dir):
+        os.makedirs(dir, exist_ok=True)
+        self.dir = dir
+        self.step = 1
+        prefix = "events"
+        path = osp.join(osp.abspath(dir), prefix)
+        import tensorflow as tf
+        from tensorflow.python import pywrap_tensorflow
+        from tensorflow.core.util import event_pb2
+        from tensorflow.python.util import compat
+
+        self.tf = tf
+        self.event_pb2 = event_pb2
+        self.pywrap_tensorflow = pywrap_tensorflow
+        self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path))
+
+    def writekvs(self, kvs):
+        def summary_val(k, v):
+            kwargs = {"tag": k, "simple_value": float(v)}
+            return self.tf.Summary.Value(**kwargs)
+
+        summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
+        event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
+        event.step = (
+            self.step
+        )  # is there any reason why you'd want to specify the step?
+        self.writer.WriteEvent(event)
+        self.writer.Flush()
+        self.step += 1
+
+    def close(self):
+        if self.writer:
+            self.writer.Close()
+            self.writer = None
+
+
+def make_output_format(format, ev_dir, log_suffix=""):
+    os.makedirs(ev_dir, exist_ok=True)
+    if format == "stdout":
+        return HumanOutputFormat(sys.stdout)
+    elif format == "log":
+        return HumanOutputFormat(osp.join(ev_dir, "log%s.txt" % log_suffix))
+    elif format == "json":
+        return JSONOutputFormat(osp.join(ev_dir, "progress%s.json" % log_suffix))
+    elif format == "csv":
+        return CSVOutputFormat(osp.join(ev_dir, "progress%s.csv" % log_suffix))
+    elif format == "tensorboard":
+        return TensorBoardOutputFormat(osp.join(ev_dir, "tb%s" % log_suffix))
+    else:
+        raise ValueError("Unknown format specified: %s" % (format,))
+
+
+# ================================================================
+# API
+# ================================================================
+
+
+def logkv(key, val):
+    """
+    Log a value of some diagnostic
+    Call this once for each diagnostic quantity, each iteration
+    If called many times, last value will be used.
+    """
+    get_current().logkv(key, val)
+
+
+def logkv_mean(key, val):
+    """
+    The same as logkv(), but if called many times, values averaged.
+    """
+    get_current().logkv_mean(key, val)
+
+def log_hist(key, val):
+    """
+    The same as logkv(), but if called many times, values averaged.
+    """
+    get_current().logkv_mean(key, val)
+
+
+def logkvs(d):
+    """
+    Log a dictionary of key-value pairs
+    """
+    for (k, v) in d.items():
+        logkv(k, v)
+
+
+def dumpkvs():
+    """
+    Write all of the diagnostics from the current iteration
+    """
+    return get_current().dumpkvs()
+
+
+def getkvs():
+    return get_current().name2val
+
+
+def log(*args, level=INFO):
+    """
+    Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
+    """
+    get_current().log(*args, level=level)
+
+
+def debug(*args):
+    log(*args, level=DEBUG)
+
+
+def info(*args):
+    log(*args, level=INFO)
+
+
+def warn(*args):
+    log(*args, level=WARN)
+
+
+def error(*args):
+    log(*args, level=ERROR)
+
+
+def set_level(level):
+    """
+    Set logging threshold on current logger.
+    """
+    get_current().set_level(level)
+
+
+def set_comm(comm):
+    get_current().set_comm(comm)
+
+
+def get_dir():
+    """
+    Get directory that log files are being written to.
+    will be None if there is no output directory (i.e., if you didn't call start)
+    """
+    return get_current().get_dir()
+
+def get_tensorboard_writer():
+    """get the tensorboard writer
+    """
+    pass
+
+
+record_tabular = logkv
+dump_tabular = dumpkvs
+
+
+@contextmanager
+def profile_kv(scopename):
+    logkey = "wait_" + scopename
+    tstart = time.time()
+    try:
+        yield
+    finally:
+        get_current().name2val[logkey] += time.time() - tstart
+
+
+def profile(n):
+    """
+    Usage:
+    @profile("my_func")
+    def my_func(): code
+    """
+
+    def decorator_with_name(func):
+        def func_wrapper(*args, **kwargs):
+            with profile_kv(n):
+                return func(*args, **kwargs)
+
+        return func_wrapper
+
+    return decorator_with_name
+
+
+# ================================================================
+# Backend
+# ================================================================
+
+
+def get_current():
+    if Logger.CURRENT is None:
+        _configure_default_logger()
+
+    return Logger.CURRENT
+
+
+class Logger(object):
+    DEFAULT = None  # A logger with no output files. (See right below class definition)
+    # So that you can still log to the terminal without setting up any output files
+    CURRENT = None  # Current logger being used by the free functions above
+
+    def __init__(self, dir, output_formats, comm=None):
+        self.name2val = defaultdict(float)  # values this iteration
+        self.name2cnt = defaultdict(int)
+        self.level = INFO
+        self.dir = dir
+        self.output_formats = output_formats
+        self.comm = comm
+
+    # Logging API, forwarded
+    # ----------------------------------------
+    def logkv(self, key, val):
+        self.name2val[key] = val
+
+    def logkv_mean(self, key, val):
+        oldval, cnt = self.name2val[key], self.name2cnt[key]
+        self.name2val[key] = oldval * cnt / (cnt + 1) + val / (cnt + 1)
+        self.name2cnt[key] = cnt + 1
+
+    def dumpkvs(self):
+        if self.comm is None:
+            d = self.name2val
+        else:
+            d = mpi_weighted_mean(
+                self.comm,
+                {
+                    name: (val, self.name2cnt.get(name, 1))
+                    for (name, val) in self.name2val.items()
+                },
+            )
+            if self.comm.rank != 0:
+                d["dummy"] = 1  # so we don't get a warning about empty dict
+        out = d.copy()  # Return the dict for unit testing purposes
+        for fmt in self.output_formats:
+            if isinstance(fmt, KVWriter):
+                fmt.writekvs(d)
+        self.name2val.clear()
+        self.name2cnt.clear()
+        return out
+
+    def log(self, *args, level=INFO):
+        if self.level <= level:
+            self._do_log(args)
+
+    # Configuration
+    # ----------------------------------------
+    def set_level(self, level):
+        self.level = level
+
+    def set_comm(self, comm):
+        self.comm = comm
+
+    def get_dir(self):
+        return self.dir
+
+    def close(self):
+        for fmt in self.output_formats:
+            fmt.close()
+
+    # Misc
+    # ----------------------------------------
+    def _do_log(self, args):
+        for fmt in self.output_formats:
+            if isinstance(fmt, SeqWriter):
+                fmt.writeseq(map(str, args))
+
+
+def get_rank_without_mpi_import():
+    # check environment variables here instead of importing mpi4py
+    # to avoid calling MPI_Init() when this module is imported
+    for varname in ["PMI_RANK", "OMPI_COMM_WORLD_RANK"]:
+        if varname in os.environ:
+            return int(os.environ[varname])
+    return 0
+
+
+def mpi_weighted_mean(comm, local_name2valcount):
+    """
+    Copied from: https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/mpi_util.py#L110
+    Perform a weighted average over dicts that are each on a different node
+    Input: local_name2valcount: dict mapping key -> (value, count)
+    Returns: key -> mean
+    """
+    all_name2valcount = comm.gather(local_name2valcount)
+    if comm.rank == 0:
+        name2sum = defaultdict(float)
+        name2count = defaultdict(float)
+        for n2vc in all_name2valcount:
+            for (name, (val, count)) in n2vc.items():
+                try:
+                    val = float(val)
+                except ValueError:
+                    if comm.rank == 0:
+                        warnings.warn(
+                            "WARNING: tried to compute mean on non-float {}={}".format(
+                                name, val
+                            )
+                        )
+                else:
+                    name2sum[name] += val * count
+                    name2count[name] += count
+        return {name: name2sum[name] / name2count[name] for name in name2sum}
+    else:
+        return {}
+
+
+def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
+    """
+    If comm is provided, average all numerical stats across that comm
+    """
+    if dir is None:
+        dir = os.getenv("OPENAI_LOGDIR")
+    if dir is None:
+        dir = osp.join(
+            tempfile.gettempdir(),
+            datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"),
+        )
+    assert isinstance(dir, str)
+    dir = os.path.expanduser(dir)
+    os.makedirs(os.path.expanduser(dir), exist_ok=True)
+
+    rank = get_rank_without_mpi_import()
+    if rank > 0:
+        log_suffix = log_suffix + "-rank%03i" % rank
+
+    if format_strs is None:
+        if rank == 0:
+            format_strs = os.getenv("OPENAI_LOG_FORMAT", "stdout,log,csv").split(",")
+        else:
+            format_strs = os.getenv("OPENAI_LOG_FORMAT_MPI", "log").split(",")
+    format_strs = filter(None, format_strs)
+    # st()
+    output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
+
+    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
+    if output_formats:
+        log("Logging to %s" % dir)
+
+
+def _configure_default_logger():
+    configure()
+    Logger.DEFAULT = Logger.CURRENT
+
+
+def reset():
+    if Logger.CURRENT is not Logger.DEFAULT:
+        Logger.CURRENT.close()
+        Logger.CURRENT = Logger.DEFAULT
+        log("Reset logger")
+
+
+@contextmanager
+def scoped_configure(dir=None, format_strs=None, comm=None):
+    prevlogger = Logger.CURRENT
+    configure(dir=dir, format_strs=format_strs, comm=comm)
+    try:
+        yield
+    finally:
+        Logger.CURRENT.close()
+        Logger.CURRENT = prevlogger
+
diff --git a/guided_diffusion/losses.py b/guided_diffusion/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..251e42e4f36a31bb5e1aeda874b3a45d722000a2
--- /dev/null
+++ b/guided_diffusion/losses.py
@@ -0,0 +1,77 @@
+"""
+Helpers for various likelihood-based losses. These are ported from the original
+Ho et al. diffusion models codebase:
+https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
+"""
+
+import numpy as np
+
+import torch as th
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    Compute the KL divergence between two gaussians.
+
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, th.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for th.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + th.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
+    )
+
+
+def approx_standard_normal_cdf(x):
+    """
+    A fast approximation of the cumulative distribution function of the
+    standard normal.
+    """
+    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
+
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    """
+    Compute the log-likelihood of a Gaussian distribution discretizing to a
+    given image.
+
+    :param x: the target images. It is assumed that this was uint8 values,
+              rescaled to the range [-1, 1].
+    :param means: the Gaussian mean Tensor.
+    :param log_scales: the Gaussian log stddev Tensor.
+    :return: a tensor like x of log probabilities (in nats).
+    """
+    assert x.shape == means.shape == log_scales.shape
+    centered_x = x - means
+    inv_stdv = th.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
+    cdf_plus = approx_standard_normal_cdf(plus_in)
+    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
+    cdf_min = approx_standard_normal_cdf(min_in)
+    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
+    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
+    cdf_delta = cdf_plus - cdf_min
+    log_probs = th.where(
+        x < -0.999,
+        log_cdf_plus,
+        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
+    )
+    assert log_probs.shape == x.shape
+    return log_probs
diff --git a/guided_diffusion/nn.py b/guided_diffusion/nn.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fc05ff451c095b98e54a2098223d10816abc32d
--- /dev/null
+++ b/guided_diffusion/nn.py
@@ -0,0 +1,171 @@
+"""
+Various utilities for neural networks.
+"""
+
+import math
+
+import torch as th
+import torch.nn as nn
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * th.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def update_ema(target_params, source_params, rate=0.99):
+    """
+    Update target parameters to be closer to those of source parameters using
+    an exponential moving average.
+
+    :param target_params: the target parameter sequence.
+    :param source_params: the source parameter sequence.
+    :param rate: the EMA rate (closer to 1 means slower).
+    """
+    for targ, src in zip(target_params, source_params):
+        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(32, channels)
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    half = dim // 2
+    freqs = th.exp(
+        -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
+    ).to(device=timesteps.device)
+    args = timesteps[:, None].float() * freqs[None]
+    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding
+
+
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        args = tuple(inputs) + tuple(params)
+        return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(th.autograd.Function):
+    @staticmethod
+    @th.autocast(device_type='cuda')
+    def forward(ctx, run_function, length, *args):
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+        with th.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with th.enable_grad():
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = th.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads
diff --git a/guided_diffusion/resample.py b/guided_diffusion/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..c82eccdcd47c468d41e7cbe02de6a731f2c9bf81
--- /dev/null
+++ b/guided_diffusion/resample.py
@@ -0,0 +1,154 @@
+from abc import ABC, abstractmethod
+
+import numpy as np
+import torch as th
+import torch.distributed as dist
+
+
+def create_named_schedule_sampler(name, diffusion):
+    """
+    Create a ScheduleSampler from a library of pre-defined samplers.
+
+    :param name: the name of the sampler.
+    :param diffusion: the diffusion object to sample for.
+    """
+    if name == "uniform":
+        return UniformSampler(diffusion)
+    elif name == "loss-second-moment":
+        return LossSecondMomentResampler(diffusion)
+    else:
+        raise NotImplementedError(f"unknown schedule sampler: {name}")
+
+
+class ScheduleSampler(ABC):
+    """
+    A distribution over timesteps in the diffusion process, intended to reduce
+    variance of the objective.
+
+    By default, samplers perform unbiased importance sampling, in which the
+    objective's mean is unchanged.
+    However, subclasses may override sample() to change how the resampled
+    terms are reweighted, allowing for actual changes in the objective.
+    """
+
+    @abstractmethod
+    def weights(self):
+        """
+        Get a numpy array of weights, one per diffusion step.
+
+        The weights needn't be normalized, but must be positive.
+        """
+
+    def sample(self, batch_size, device):
+        """
+        Importance-sample timesteps for a batch.
+
+        :param batch_size: the number of timesteps.
+        :param device: the torch device to save to.
+        :return: a tuple (timesteps, weights):
+                 - timesteps: a tensor of timestep indices.
+                 - weights: a tensor of weights to scale the resulting losses.
+        """
+        w = self.weights()
+        p = w / np.sum(w)
+        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
+        indices = th.from_numpy(indices_np).long().to(device)
+        weights_np = 1 / (len(p) * p[indices_np])
+        weights = th.from_numpy(weights_np).float().to(device)
+        return indices, weights
+
+
+class UniformSampler(ScheduleSampler):
+    def __init__(self, diffusion):
+        self.diffusion = diffusion
+        self._weights = np.ones([diffusion.num_timesteps])
+
+    def weights(self):
+        return self._weights
+
+
+class LossAwareSampler(ScheduleSampler):
+    def update_with_local_losses(self, local_ts, local_losses):
+        """
+        Update the reweighting using losses from a model.
+
+        Call this method from each rank with a batch of timesteps and the
+        corresponding losses for each of those timesteps.
+        This method will perform synchronization to make sure all of the ranks
+        maintain the exact same reweighting.
+
+        :param local_ts: an integer Tensor of timesteps.
+        :param local_losses: a 1D Tensor of losses.
+        """
+        batch_sizes = [
+            th.tensor([0], dtype=th.int32, device=local_ts.device)
+            for _ in range(dist.get_world_size())
+        ]
+        dist.all_gather(
+            batch_sizes,
+            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
+        )
+
+        # Pad all_gather batches to be the maximum batch size.
+        batch_sizes = [x.item() for x in batch_sizes]
+        max_bs = max(batch_sizes)
+
+        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
+        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
+        dist.all_gather(timestep_batches, local_ts)
+        dist.all_gather(loss_batches, local_losses)
+        timesteps = [
+            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
+        ]
+        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
+        self.update_with_all_losses(timesteps, losses)
+
+    @abstractmethod
+    def update_with_all_losses(self, ts, losses):
+        """
+        Update the reweighting using losses from a model.
+
+        Sub-classes should override this method to update the reweighting
+        using losses from the model.
+
+        This method directly updates the reweighting without synchronizing
+        between workers. It is called by update_with_local_losses from all
+        ranks with identical arguments. Thus, it should have deterministic
+        behavior to maintain state across workers.
+
+        :param ts: a list of int timesteps.
+        :param losses: a list of float losses, one per timestep.
+        """
+
+
+class LossSecondMomentResampler(LossAwareSampler):
+    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
+        self.diffusion = diffusion
+        self.history_per_term = history_per_term
+        self.uniform_prob = uniform_prob
+        self._loss_history = np.zeros(
+            [diffusion.num_timesteps, history_per_term], dtype=np.float64
+        )
+        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
+
+    def weights(self):
+        if not self._warmed_up():
+            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
+        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
+        weights /= np.sum(weights)
+        weights *= 1 - self.uniform_prob
+        weights += self.uniform_prob / len(weights)
+        return weights
+
+    def update_with_all_losses(self, ts, losses):
+        for t, loss in zip(ts, losses):
+            if self._loss_counts[t] == self.history_per_term:
+                # Shift out the oldest loss term.
+                self._loss_history[t, :-1] = self._loss_history[t, 1:]
+                self._loss_history[t, -1] = loss
+            else:
+                self._loss_history[t, self._loss_counts[t]] = loss
+                self._loss_counts[t] += 1
+
+    def _warmed_up(self):
+        return (self._loss_counts == self.history_per_term).all()
diff --git a/guided_diffusion/respace.py b/guided_diffusion/respace.py
new file mode 100644
index 0000000000000000000000000000000000000000..19b29db2bb029b74abaf15874dfb6668be8d365d
--- /dev/null
+++ b/guided_diffusion/respace.py
@@ -0,0 +1,135 @@
+import numpy as np
+import torch as th
+from pdb import set_trace as st
+
+from .gaussian_diffusion import GaussianDiffusion
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+
+    If the stride is a string starting with "ddim", then the fixed striding
+    from the DDIM paper is used, and only one section is allowed.
+
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim") :])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(
+                f"cannot create exactly {num_timesteps} steps with an integer stride"
+            )
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(
+                f"cannot divide section of {size} steps into {section_count}"
+            )
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+class SpacedDiffusion(GaussianDiffusion):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+
+    def __init__(self, use_timesteps, **kwargs):
+        self.use_timesteps = set(use_timesteps)
+        self.timestep_map = []
+        self.original_num_steps = len(kwargs["betas"])
+
+        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                self.timestep_map.append(i)
+        kwargs["betas"] = np.array(new_betas)
+        super().__init__(**kwargs)
+
+    def p_mean_variance(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+    def training_losses(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().training_losses(self._wrap_model(model), *args, **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def _wrap_model(self, model):
+        if isinstance(model, _WrappedModel):
+            return model
+        return _WrappedModel(
+            model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
+        )
+
+    def _scale_timesteps(self, t):
+        # Scaling is done by the wrapped model.
+        return t
+
+
+class _WrappedModel:
+    def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, ts, c=None, mixing_normal=False, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        if self.rescale_timesteps:
+            new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+        # assert mixing_normal
+        new_ts = new_ts / self.original_num_steps # already respaced to 1000 steps
+        if mixing_normal:
+            self.mixing_logit = self.model.ddp_model(x=None, # will be queried in gaussian_diffusion.py
+                                        timesteps=None,
+                                        get_attr='mixing_logit')
+        return self.model.apply_model_inference(x,new_ts, c, **kwargs) # send in "self" not "Unet", to use cldm
diff --git a/guided_diffusion/script_util.py b/guided_diffusion/script_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..680b6b0adb57181b6ee0c3bf390a3105d72e1e5b
--- /dev/null
+++ b/guided_diffusion/script_util.py
@@ -0,0 +1,729 @@
+import argparse
+import inspect
+
+from pdb import set_trace as st
+
+from cldm.cldm import ControlledUnetModel, ControlNet
+
+from . import gaussian_diffusion as gd
+from .respace import SpacedDiffusion, space_timesteps
+from .unet import SuperResModel, UNetModel, EncoderUNetModel, UNetModelWithHint
+import torch as th
+# from dit.dit_models_xformers import DiT_models, TextCondDiTBlock
+from dit.dit_models_xformers import TextCondDiTBlock, ImageCondDiTBlock, FinalLayer
+from dit.dit_trilatent import DiT_models as DiT_models_t23d
+from dit.dit_i23d import DiT_models as DiT_models_i23d
+
+# if th.cuda.is_available():
+#     from xformers.triton import FusedLayerNorm as LayerNorm
+
+NUM_CLASSES = 1000
+
+
+def diffusion_defaults():
+    """
+    Defaults for image and classifier training.
+    """
+    return dict(
+        learn_sigma=False,
+        diffusion_steps=1000,
+        noise_schedule="linear",
+        standarization_xt=False,
+        timestep_respacing="",
+        use_kl=False,
+        predict_xstart=False,
+        predict_v=False,
+        rescale_timesteps=False,
+        rescale_learned_sigmas=False,
+        mixed_prediction=False,  # ! to assign later
+    )
+
+
+def classifier_defaults():
+    """
+    Defaults for classifier models.
+    """
+    return dict(
+        image_size=64,
+        classifier_use_fp16=False,
+        classifier_width=128,
+        classifier_depth=2,
+        classifier_attention_resolutions="32,16,8",  # 16
+        classifier_use_scale_shift_norm=True,  # False
+        classifier_resblock_updown=True,  # False
+        classifier_pool="attention",
+    )
+
+
+def control_net_defaults():
+    res = dict(
+        only_mid_control=False,  # TODO
+        control_key='img',
+        normalize_clip_encoding=False,  # zero-shot text inference
+        scale_clip_encoding=1.0,
+        cfg_dropout_prob=0.0,  # dropout condition for CFG training
+        cond_key='caption',
+    )
+    return res
+
+
+def continuous_diffusion_defaults():
+    # NVlabs/LSGM/train_vada.py
+    res = dict(
+        sde_time_eps=1e-2,
+        sde_beta_start=0.1,
+        sde_beta_end=20.0,
+        sde_sde_type='vpsde',
+        sde_sigma2_0=0.0,  # ?
+        iw_sample_p='drop_sigma2t_iw',
+        iw_sample_q='ll_iw',
+        iw_subvp_like_vp_sde=False,
+        train_vae=True,
+        pred_type='eps',  # [x0, eps]
+        # joint_train=False,
+        p_rendering_loss=False,
+        unfix_logit=False,
+        loss_type='eps',
+        loss_weight='simple',  # snr snr_sqrt sigmoid_snr
+        # train_vae_denoise_rendering=False,
+        diffusion_ce_anneal=True,
+        enable_mixing_normal=True,
+    )
+
+    return res
+
+
+def model_and_diffusion_defaults():
+    """
+    Defaults for image training.
+    """
+    res = dict(
+        # image_size=64,
+        diffusion_input_size=224,
+        num_channels=128,
+        num_res_blocks=2,
+        num_heads=4,
+        num_heads_upsample=-1,
+        num_head_channels=-1,
+        attention_resolutions="16,8",
+        channel_mult="",
+        dropout=0.0,
+        class_cond=False,
+        use_checkpoint=False,
+        use_scale_shift_norm=True,
+        resblock_updown=False,
+        use_fp16=False,
+        use_new_attention_order=False,
+        denoise_in_channels=3,
+        denoise_out_channels=3,
+        # ! controlnet args
+        create_controlnet=False,
+        create_dit=False,
+        i23d=False,
+        create_unet_with_hint=False,
+        dit_model_arch='DiT-L/2',
+        # ! ldm unet support
+        use_spatial_transformer=False,  # custom transformer support
+        transformer_depth=1,  # custom transformer support
+        context_dim=-1,  # custom transformer support
+        pooling_ctx_dim=-1,
+        roll_out=False,  # whether concat in batch, not channel
+        n_embed=
+        None,  # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+        mixing_logit_init=-6,
+        hint_channels=3,
+        # unconditional_guidance_scale=1.0,
+        # normalize_clip_encoding=False, # for zero-shot conditioning
+    )
+    res.update(diffusion_defaults())
+    # res.update(continuous_diffusion_defaults())
+    return res
+
+
+def classifier_and_diffusion_defaults():
+    res = classifier_defaults()
+    res.update(diffusion_defaults())
+    return res
+
+
+def create_model_and_diffusion(
+    # image_size,
+    diffusion_input_size,
+    class_cond,
+    learn_sigma,
+    num_channels,
+    num_res_blocks,
+    channel_mult,
+    num_heads,
+    num_head_channels,
+    num_heads_upsample,
+    attention_resolutions,
+    dropout,
+    diffusion_steps,
+    noise_schedule,
+    timestep_respacing,
+    use_kl,
+    predict_xstart,
+    predict_v,
+    rescale_timesteps,
+    rescale_learned_sigmas,
+    use_checkpoint,
+    use_scale_shift_norm,
+    resblock_updown,
+    use_fp16,
+    use_new_attention_order,
+    denoise_in_channels,
+    denoise_out_channels,
+    standarization_xt,
+    mixed_prediction,
+    # controlnet
+    create_controlnet,
+    # only_mid_control,
+    # control_key,
+    use_spatial_transformer,
+    transformer_depth,
+    context_dim,
+    pooling_ctx_dim,
+    n_embed,
+    legacy,
+    mixing_logit_init,
+    create_dit,
+    i23d,
+    create_unet_with_hint,
+    dit_model_arch,
+    roll_out,
+    hint_channels,
+    # unconditional_guidance_scale,
+    # normalize_clip_encoding,
+):
+    model = create_model(
+        diffusion_input_size,
+        num_channels,
+        num_res_blocks,
+        channel_mult=channel_mult,
+        learn_sigma=learn_sigma,
+        class_cond=class_cond,
+        use_checkpoint=use_checkpoint,
+        attention_resolutions=attention_resolutions,
+        num_heads=num_heads,
+        num_head_channels=num_head_channels,
+        num_heads_upsample=num_heads_upsample,
+        use_scale_shift_norm=use_scale_shift_norm,
+        dropout=dropout,
+        resblock_updown=resblock_updown,
+        use_fp16=use_fp16,
+        use_new_attention_order=use_new_attention_order,
+        denoise_in_channels=denoise_in_channels,
+        denoise_out_channels=denoise_out_channels,
+        mixed_prediction=mixed_prediction,
+        create_controlnet=create_controlnet,
+        # only_mid_control=only_mid_control,
+        # control_key=control_key,
+        use_spatial_transformer=use_spatial_transformer,
+        transformer_depth=transformer_depth,
+        context_dim=context_dim,
+        pooling_ctx_dim=pooling_ctx_dim,
+        n_embed=n_embed,
+        legacy=legacy,
+        mixing_logit_init=mixing_logit_init,
+        create_dit=create_dit,
+        i23d=i23d,
+        create_unet_with_hint=create_unet_with_hint,
+        dit_model_arch=dit_model_arch,
+        roll_out=roll_out,
+        hint_channels=hint_channels,
+        # normalize_clip_encoding=normalize_clip_encoding,
+    )
+    diffusion = create_gaussian_diffusion(
+        diffusion_steps=diffusion_steps,
+        learn_sigma=learn_sigma,
+        noise_schedule=noise_schedule,
+        use_kl=use_kl,
+        predict_xstart=predict_xstart,
+        predict_v=predict_v,
+        rescale_timesteps=rescale_timesteps,
+        rescale_learned_sigmas=rescale_learned_sigmas,
+        timestep_respacing=timestep_respacing,
+        standarization_xt=standarization_xt,
+    )
+    return model, diffusion
+
+
+def create_model(
+    image_size,
+    num_channels,
+    num_res_blocks,
+    channel_mult="",
+    learn_sigma=False,
+    class_cond=False,
+    use_checkpoint=False,
+    attention_resolutions="16",
+    num_heads=1,
+    num_head_channels=-1,
+    num_heads_upsample=-1,
+    use_scale_shift_norm=False,
+    dropout=0,
+    resblock_updown=False,
+    use_fp16=False,
+    use_new_attention_order=False,
+    # denoise_in_channels=3,
+    denoise_in_channels=-1,
+    denoise_out_channels=3,
+    mixed_prediction=False,
+    create_controlnet=False,
+    create_dit=False,
+    # t23d=True,
+    i23d=False,
+    create_unet_with_hint=False,
+    dit_model_arch='DiT-L/2',
+    hint_channels=3,
+    use_spatial_transformer=False,  # custom transformer support
+    transformer_depth=1,  # custom transformer support
+    context_dim=None,  # custom transformer support
+    pooling_ctx_dim=768,
+    n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
+    legacy=True,
+    mixing_logit_init=-6,
+    roll_out=False,
+    # normalize_clip_encoding=False,
+):
+    if channel_mult == "":
+        if image_size == 512:
+            channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
+        elif image_size == 448:
+            channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
+        elif image_size == 320:  # ffhq
+            channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
+        elif image_size == 224 and denoise_in_channels == 144:  # ffhq
+            channel_mult = (1, 1, 2, 3, 4, 4)
+        elif image_size == 224:
+            channel_mult = (1, 1, 2, 2, 4, 4)
+        elif image_size == 256:
+            channel_mult = (1, 1, 2, 2, 4, 4)
+        elif image_size == 128:
+            channel_mult = (1, 1, 2, 3, 4)
+        elif image_size == 64:
+            channel_mult = (1, 2, 3, 4)
+
+        elif image_size == 32:  # https://github.com/CompVis/latent-diffusion/blob/a506df5756472e2ebaf9078affdde2c4f1502cd4/configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml#L37
+            channel_mult = (1, 2, 4, 4)
+
+        elif image_size == 16:  # B,12,16,16. just for baseline check. not good performance.
+            channel_mult = (1, 2, 3, 4)
+        else:
+            raise ValueError(f"unsupported image size: {image_size}")
+    else:
+        channel_mult = tuple(
+            int(ch_mult) for ch_mult in channel_mult.split(","))
+
+    attention_ds = []
+    for res in attention_resolutions.split(","):
+        attention_ds.append(image_size // int(res))
+
+    if create_controlnet:
+
+        controlledUnetModel = ControlledUnetModel(
+            image_size=image_size,
+            in_channels=denoise_in_channels,
+            model_channels=num_channels,
+            # out_channels=(3 if not learn_sigma else 6),
+            out_channels=(denoise_out_channels
+                          if not learn_sigma else denoise_out_channels * 2),
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=tuple(attention_ds),
+            dropout=dropout,
+            channel_mult=channel_mult,
+            num_classes=(NUM_CLASSES if class_cond else None),
+            use_checkpoint=use_checkpoint,
+            use_fp16=use_fp16,
+            num_heads=num_heads,
+            num_head_channels=num_head_channels,
+            num_heads_upsample=num_heads_upsample,
+            use_scale_shift_norm=use_scale_shift_norm,
+            resblock_updown=resblock_updown,
+            use_new_attention_order=use_new_attention_order,
+            mixed_prediction=mixed_prediction,
+            # ldm support
+            use_spatial_transformer=use_spatial_transformer,
+            transformer_depth=transformer_depth,
+            context_dim=context_dim,
+            pooling_ctx_dim=pooling_ctx_dim,
+            n_embed=n_embed,
+            legacy=legacy,
+            mixing_logit_init=mixing_logit_init,
+            roll_out=roll_out
+            )
+
+        controlNet = ControlNet(
+            image_size=image_size,
+            in_channels=denoise_in_channels,
+            model_channels=num_channels,
+            # ! condition channels
+            hint_channels=hint_channels,
+            # out_channels=(3 if not learn_sigma else 6),
+            # out_channels=(denoise_out_channels
+            #             if not learn_sigma else denoise_out_channels * 2),
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=tuple(attention_ds),
+            dropout=dropout,
+            channel_mult=channel_mult,
+            # num_classes=(NUM_CLASSES if class_cond else None),
+            use_checkpoint=use_checkpoint,
+            use_fp16=use_fp16,
+            num_heads=num_heads,
+            num_head_channels=num_head_channels,
+            num_heads_upsample=num_heads_upsample,
+            use_scale_shift_norm=use_scale_shift_norm,
+            resblock_updown=resblock_updown,
+            use_new_attention_order=use_new_attention_order,
+            roll_out=roll_out
+        )
+        # mixed_prediction=mixed_prediction)
+
+        return controlledUnetModel, controlNet
+
+    elif create_dit:
+        # if i23d
+        if i23d:
+            return DiT_models_i23d[dit_model_arch](
+                input_size=image_size,
+                num_classes=0,
+                learn_sigma=learn_sigma,
+                in_channels=denoise_in_channels,
+                context_dim=context_dim,  # add CLIP text embedding
+                roll_out=roll_out, 
+                # vit_blk=ImageCondDiTBlock,
+                pooling_ctx_dim=pooling_ctx_dim,)
+        else: # t23d
+            return DiT_models_t23d[dit_model_arch](
+                input_size=image_size,
+                num_classes=0,
+                learn_sigma=learn_sigma,
+                in_channels=denoise_in_channels,
+                context_dim=context_dim,  # add CLIP text embedding
+                roll_out=roll_out, 
+                # vit_blk=TextCondDiTBlock
+                )
+    else:
+
+        if create_unet_with_hint:
+            unet_cls = UNetModelWithHint
+        else:
+            unet_cls = UNetModel
+
+        # st()
+        return unet_cls(
+            image_size=image_size,
+            in_channels=denoise_in_channels,
+            model_channels=num_channels,
+            # out_channels=(3 if not learn_sigma else 6),
+            out_channels=(denoise_out_channels
+                          if not learn_sigma else denoise_out_channels * 2),
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=tuple(attention_ds),
+            dropout=dropout,
+            channel_mult=channel_mult,
+            num_classes=(NUM_CLASSES if class_cond else None),
+            use_checkpoint=use_checkpoint,
+            use_fp16=use_fp16,
+            num_heads=num_heads,
+            num_head_channels=num_head_channels,
+            num_heads_upsample=num_heads_upsample,
+            use_scale_shift_norm=use_scale_shift_norm,
+            resblock_updown=resblock_updown,
+            use_new_attention_order=use_new_attention_order,
+            mixed_prediction=mixed_prediction,
+            # ldm support
+            use_spatial_transformer=use_spatial_transformer,
+            transformer_depth=transformer_depth,
+            context_dim=context_dim,
+            pooling_ctx_dim=pooling_ctx_dim,
+            n_embed=n_embed,
+            legacy=legacy,
+            mixing_logit_init=mixing_logit_init,
+            roll_out=roll_out,
+            hint_channels=hint_channels,
+            # normalize_clip_encoding=normalize_clip_encoding,
+        )
+
+
+def create_classifier_and_diffusion(
+    image_size,
+    classifier_use_fp16,
+    classifier_width,
+    classifier_depth,
+    classifier_attention_resolutions,
+    classifier_use_scale_shift_norm,
+    classifier_resblock_updown,
+    classifier_pool,
+    learn_sigma,
+    diffusion_steps,
+    noise_schedule,
+    timestep_respacing,
+    use_kl,
+    predict_xstart,
+    rescale_timesteps,
+    rescale_learned_sigmas,
+):
+    classifier = create_classifier(
+        image_size,
+        classifier_use_fp16,
+        classifier_width,
+        classifier_depth,
+        classifier_attention_resolutions,
+        classifier_use_scale_shift_norm,
+        classifier_resblock_updown,
+        classifier_pool,
+    )
+    diffusion = create_gaussian_diffusion(
+        steps=diffusion_steps,
+        learn_sigma=learn_sigma,
+        noise_schedule=noise_schedule,
+        use_kl=use_kl,
+        predict_xstart=predict_xstart,
+        rescale_timesteps=rescale_timesteps,
+        rescale_learned_sigmas=rescale_learned_sigmas,
+        timestep_respacing=timestep_respacing,
+    )
+    return classifier, diffusion
+
+
+def create_classifier(
+    image_size,
+    classifier_use_fp16,
+    classifier_width,
+    classifier_depth,
+    classifier_attention_resolutions,
+    classifier_use_scale_shift_norm,
+    classifier_resblock_updown,
+    classifier_pool,
+):
+    if image_size == 512:
+        channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
+    elif image_size == 256:
+        channel_mult = (1, 1, 2, 2, 4, 4)
+    elif image_size == 128:
+        channel_mult = (1, 1, 2, 3, 4)
+    elif image_size == 64:
+        channel_mult = (1, 2, 3, 4)
+    else:
+        raise ValueError(f"unsupported image size: {image_size}")
+
+    attention_ds = []
+    for res in classifier_attention_resolutions.split(","):
+        attention_ds.append(image_size // int(res))
+
+    return EncoderUNetModel(
+        image_size=image_size,
+        in_channels=3,
+        model_channels=classifier_width,
+        out_channels=1000,
+        num_res_blocks=classifier_depth,
+        attention_resolutions=tuple(attention_ds),
+        channel_mult=channel_mult,
+        use_fp16=classifier_use_fp16,
+        num_head_channels=64,
+        use_scale_shift_norm=classifier_use_scale_shift_norm,
+        resblock_updown=classifier_resblock_updown,
+        pool=classifier_pool,
+    )
+
+
+def sr_model_and_diffusion_defaults():
+    res = model_and_diffusion_defaults()
+    res["large_size"] = 256
+    res["small_size"] = 64
+    arg_names = inspect.getfullargspec(sr_create_model_and_diffusion)[0]
+    for k in res.copy().keys():
+        if k not in arg_names:
+            del res[k]
+    return res
+
+
+def sr_create_model_and_diffusion(
+    large_size,
+    small_size,
+    class_cond,
+    learn_sigma,
+    num_channels,
+    num_res_blocks,
+    num_heads,
+    num_head_channels,
+    num_heads_upsample,
+    attention_resolutions,
+    dropout,
+    diffusion_steps,
+    noise_schedule,
+    timestep_respacing,
+    use_kl,
+    predict_xstart,
+    rescale_timesteps,
+    rescale_learned_sigmas,
+    use_checkpoint,
+    use_scale_shift_norm,
+    resblock_updown,
+    use_fp16,
+):
+    model = sr_create_model(
+        large_size,
+        small_size,
+        num_channels,
+        num_res_blocks,
+        learn_sigma=learn_sigma,
+        class_cond=class_cond,
+        use_checkpoint=use_checkpoint,
+        attention_resolutions=attention_resolutions,
+        num_heads=num_heads,
+        num_head_channels=num_head_channels,
+        num_heads_upsample=num_heads_upsample,
+        use_scale_shift_norm=use_scale_shift_norm,
+        dropout=dropout,
+        resblock_updown=resblock_updown,
+        use_fp16=use_fp16,
+    )
+    diffusion = create_gaussian_diffusion(
+        steps=diffusion_steps,
+        learn_sigma=learn_sigma,
+        noise_schedule=noise_schedule,
+        use_kl=use_kl,
+        predict_xstart=predict_xstart,
+        rescale_timesteps=rescale_timesteps,
+        rescale_learned_sigmas=rescale_learned_sigmas,
+        timestep_respacing=timestep_respacing,
+    )
+    return model, diffusion
+
+
+def sr_create_model(
+    large_size,
+    small_size,
+    num_channels,
+    num_res_blocks,
+    learn_sigma,
+    class_cond,
+    use_checkpoint,
+    attention_resolutions,
+    num_heads,
+    num_head_channels,
+    num_heads_upsample,
+    use_scale_shift_norm,
+    dropout,
+    resblock_updown,
+    use_fp16,
+):
+    _ = small_size  # hack to prevent unused variable
+
+    if large_size == 512:
+        channel_mult = (1, 1, 2, 2, 4, 4)
+    elif large_size == 256:
+        channel_mult = (1, 1, 2, 2, 4, 4)
+    elif large_size == 64:
+        channel_mult = (1, 2, 3, 4)
+    else:
+        raise ValueError(f"unsupported large size: {large_size}")
+
+    attention_ds = []
+    for res in attention_resolutions.split(","):
+        attention_ds.append(large_size // int(res))
+
+    return SuperResModel(
+        image_size=large_size,
+        in_channels=3,
+        model_channels=num_channels,
+        out_channels=(3 if not learn_sigma else 6),
+        num_res_blocks=num_res_blocks,
+        attention_resolutions=tuple(attention_ds),
+        dropout=dropout,
+        channel_mult=channel_mult,
+        num_classes=(NUM_CLASSES if class_cond else None),
+        use_checkpoint=use_checkpoint,
+        num_heads=num_heads,
+        num_head_channels=num_head_channels,
+        num_heads_upsample=num_heads_upsample,
+        use_scale_shift_norm=use_scale_shift_norm,
+        resblock_updown=resblock_updown,
+        use_fp16=use_fp16,
+    )
+
+
+def create_gaussian_diffusion(
+    *,
+    diffusion_steps=1000,
+    learn_sigma=False,
+    sigma_small=False,
+    noise_schedule="linear",
+    use_kl=False,
+    predict_xstart=False,
+    predict_v=False,
+    rescale_timesteps=False,
+    rescale_learned_sigmas=False,
+    timestep_respacing="",
+    standarization_xt=False,
+):
+    betas = gd.get_named_beta_schedule(noise_schedule, diffusion_steps)
+    if use_kl:
+        loss_type = gd.LossType.RESCALED_KL
+    elif rescale_learned_sigmas:
+        loss_type = gd.LossType.RESCALED_MSE
+    else:
+        loss_type = gd.LossType.MSE  # * used here.
+    if not timestep_respacing:
+        timestep_respacing = [diffusion_steps]
+
+    if predict_xstart:
+        model_mean_type = gd.ModelMeanType.START_X
+    elif predict_v:
+        model_mean_type = gd.ModelMeanType.V
+    else:
+        model_mean_type = gd.ModelMeanType.EPSILON
+
+        # model_mean_type=(
+        #     gd.ModelMeanType.EPSILON if not predict_xstart else
+        #     gd.ModelMeanType.START_X  # * used gd.ModelMeanType.EPSILON
+        # ),
+
+    return SpacedDiffusion(
+        use_timesteps=space_timesteps(diffusion_steps, timestep_respacing),
+        betas=betas,
+        model_mean_type=model_mean_type,
+        # (
+        #     gd.ModelMeanType.EPSILON if not predict_xstart else
+        #     gd.ModelMeanType.START_X  # * used gd.ModelMeanType.EPSILON
+        # ),
+        model_var_type=((
+            gd.ModelVarType.FIXED_LARGE  # * used here
+            if not sigma_small else gd.ModelVarType.FIXED_SMALL)
+                        if not learn_sigma else gd.ModelVarType.LEARNED_RANGE),
+        loss_type=loss_type,
+        rescale_timesteps=rescale_timesteps,
+        standarization_xt=standarization_xt,
+    )
+
+
+def add_dict_to_argparser(parser, default_dict):
+    for k, v in default_dict.items():
+        v_type = type(v)
+        if v is None:
+            v_type = str
+        elif isinstance(v, bool):
+            v_type = str2bool
+        parser.add_argument(f"--{k}", default=v, type=v_type)
+
+
+def args_to_dict(args, keys):
+    return {k: getattr(args, k) for k in keys}
+
+
+def str2bool(v):
+    """
+    https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
+    """
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("yes", "true", "t", "y", "1"):
+        return True
+    elif v.lower() in ("no", "false", "f", "n", "0"):
+        return False
+    else:
+        raise argparse.ArgumentTypeError("boolean value expected")
diff --git a/guided_diffusion/train_util.py b/guided_diffusion/train_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..2593ada9076b000c35f220cac6971653e8d82748
--- /dev/null
+++ b/guided_diffusion/train_util.py
@@ -0,0 +1,827 @@
+import copy
+from tqdm import tqdm, trange
+import imageio
+from pdb import set_trace as st
+import functools
+import os
+import numpy as np
+
+import blobfile as bf
+import torch as th
+import torch.distributed as dist
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+import matplotlib.pyplot as plt
+from torch.optim import AdamW
+
+from . import dist_util, logger
+from .fp16_util import MixedPrecisionTrainer
+from .nn import update_ema
+from .resample import LossAwareSampler, UniformSampler
+
+from pathlib import Path
+
+# For ImageNet experiments, this was a good default value.
+# We found that the lg_loss_scale quickly climbed to
+# 20-21 within the first ~1K steps of training.
+INITIAL_LOG_LOSS_SCALE = 20.0
+
+# use_amp = True
+# use_amp = False
+# if use_amp:
+# logger.log('ddpm use AMP to accelerate training')
+
+
+class TrainLoop:
+
+    def __init__(
+        self,
+        *,
+        model,
+        diffusion,
+        data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        save_interval,
+        resume_checkpoint,
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        schedule_sampler=None,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        use_amp=False,
+        model_name='ddpm',
+        train_vae=True,
+        compile=False,
+        clip_grad_throld=1.0,
+        **kwargs
+    ):
+
+        self.kwargs = kwargs
+        self.clip_grad_throld = clip_grad_throld
+        self.pool_512 = th.nn.AdaptiveAvgPool2d((512, 512))
+        self.pool_256 = th.nn.AdaptiveAvgPool2d((256, 256))
+        self.pool_128 = th.nn.AdaptiveAvgPool2d((128, 128))
+        self.pool_64 = th.nn.AdaptiveAvgPool2d((64, 64))
+
+        self.use_amp = use_amp
+
+        self.dtype = th.float32
+        # if use_amp:
+        #     if th.backends.cuda.matmul.allow_tf32: # a100
+        #         self.dtype = th.bfloat16
+        #     else:
+        #         self.dtype = th.float16
+        # else:
+
+        if use_amp: 
+            if th.cuda.get_device_capability(0)[0] < 8:
+                self.dtype = th.float16 # e.g., v100
+            else:
+                self.dtype = th.bfloat16 # e.g., a100 / a6000
+
+        self.model_name = model_name
+        self.model = model
+
+        self.diffusion = diffusion
+        self.data = data
+        self.batch_size = batch_size
+        self.microbatch = microbatch if microbatch > 0 else batch_size
+        self.lr = lr
+        self.ema_rate = ([ema_rate] if isinstance(ema_rate, float) else
+                         [float(x) for x in ema_rate.split(",")])
+        self.log_interval = log_interval
+        self.save_interval = save_interval
+        self.resume_checkpoint = resume_checkpoint
+        self.use_fp16 = use_fp16
+        self.fp16_scale_growth = fp16_scale_growth
+        self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
+        self.weight_decay = weight_decay
+        self.lr_anneal_steps = lr_anneal_steps
+
+        self.step = 0
+        self.resume_step = 0
+        self.global_batch = self.batch_size * dist.get_world_size()
+        self.train_vae = train_vae
+
+        self.sync_cuda = th.cuda.is_available()
+        self.triplane_scaling_divider = 1.0
+        self.latent_name = 'latent_normalized_2Ddiffusion'  # normalized triplane latent
+        self.render_latent_behaviour = 'decode_after_vae'  # directly render using triplane operations
+        self._setup_model()
+        self._load_model()
+        self._setup_opt()
+    
+    def _load_model(self):
+        self._load_and_sync_parameters()
+    
+    def _setup_opt(self):
+        self.opt = AdamW(self.mp_trainer.master_params,
+                         lr=self.lr,
+                         weight_decay=self.weight_decay)
+    
+    def _setup_model(self):
+
+        # st()
+        self.mp_trainer = MixedPrecisionTrainer(
+            model=self.model,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=self.fp16_scale_growth,
+            use_amp=self.use_amp,
+            model_name=self.model_name,
+            clip_grad_throld=self.clip_grad_throld,
+        )
+
+        if self.resume_step:
+            self._load_optimizer_state()
+            # Model was resumed, either due to a restart or a checkpoint
+            # being specified at the command line.
+            self.ema_params = [
+                self._load_ema_parameters(rate) for rate in self.ema_rate
+            ]
+        else:
+            self.ema_params = [
+                copy.deepcopy(self.mp_trainer.master_params)
+                for _ in range(len(self.ema_rate))
+            ]
+            
+        # for compatability
+
+        # print('creating DDP')
+        if th.cuda.is_available():
+            self.use_ddp = True
+            self.ddpm_model = self.model
+            self.ddp_model = DDP(
+                self.model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            if dist.get_world_size() > 1:
+                logger.warn("Distributed training requires CUDA. "
+                            "Gradients will not be synchronized properly!")
+            self.use_ddp = False
+            self.ddp_model = self.model
+        # print('creating DDP done')
+
+        # if compile:
+        #     self.model = th.compile(self.model) # some op will break graph now
+        #     logger.warn("compiling...")
+
+
+    def _load_and_sync_parameters(self):
+        resume_checkpoint, resume_step = find_resume_checkpoint(
+        ) or self.resume_checkpoint
+
+        if resume_checkpoint:
+            if not Path(resume_checkpoint).exists():
+                logger.log(
+                    f"failed to load model from checkpoint: {resume_checkpoint}, not exist"
+                )
+                return
+
+            # self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
+            self.resume_step = resume_step  # TODO, EMA part
+            if dist.get_rank() == 0:
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                # if model is None:
+                #     model = self.model
+                self.model.load_state_dict(
+                    dist_util.load_state_dict(
+                        resume_checkpoint,
+                        map_location=dist_util.dev(),
+                    ))
+
+        # ! debugging, remove to check which key fails.
+        dist_util.sync_params(self.model.parameters())
+        # dist_util.sync_params(self.model.named_parameters())
+
+    def _load_ema_parameters(self,
+                             rate,
+                             model=None,
+                             mp_trainer=None,
+                             model_name='ddpm'):
+
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer
+        if model is None:
+            model = self.model
+
+        ema_params = copy.deepcopy(mp_trainer.master_params)
+
+        main_checkpoint, _ = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step,
+                                             rate, model_name)
+        if ema_checkpoint:
+
+            if dist_util.get_rank() == 0:
+
+                if not Path(ema_checkpoint).exists():
+                    logger.log(
+                        f"failed to load EMA from checkpoint: {ema_checkpoint}, not exist"
+                    )
+                    return
+
+                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                state_dict = dist_util.load_state_dict(
+                    ema_checkpoint, map_location=map_location)
+
+                model_ema_state_dict = model.state_dict()
+
+                for k, v in state_dict.items():
+                    if k in model_ema_state_dict.keys() and v.size(
+                    ) == model_ema_state_dict[k].size():
+                        model_ema_state_dict[k] = v
+
+                    # elif 'IN' in k and model_name == 'rec' and getattr(model.decoder, 'decomposed_IN', False):
+                    #     model_ema_state_dict[k.replace('IN', 'superresolution.norm.norm_layer')] = v # decomposed IN
+
+                    else:
+                        print('ignore key: ', k, ": ", v.size())
+
+                ema_params = mp_trainer.state_dict_to_master_params(
+                    model_ema_state_dict)
+
+                del state_dict
+
+        # print('ema mark 3, ', model_name, flush=True)
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(ema_params)
+        # print('ema mark 4, ', model_name, flush=True)
+        # del ema_params
+        return ema_params
+
+    def _load_ema_parameters_freezeAE(
+            self,
+            rate,
+            model,
+            #  mp_trainer=None,
+            model_name='rec'):
+
+        # if mp_trainer is None:
+        # mp_trainer = self.mp_trainer
+        # if model is None:
+        # model = self.model_rec
+
+        # ema_params = copy.deepcopy(mp_trainer.master_params)
+
+        main_checkpoint, _ = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step,
+                                             rate, model_name)
+        if ema_checkpoint:
+
+            if dist_util.get_rank() == 0:
+
+                if not Path(ema_checkpoint).exists():
+                    logger.log(
+                        f"failed to load EMA from checkpoint: {ema_checkpoint}, not exist"
+                    )
+                    return
+
+                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                state_dict = dist_util.load_state_dict(
+                    ema_checkpoint, map_location=map_location)
+
+                model_ema_state_dict = model.state_dict()
+
+                for k, v in state_dict.items():
+                    if k in model_ema_state_dict.keys() and v.size(
+                    ) == model_ema_state_dict[k].size():
+                        model_ema_state_dict[k] = v
+                    else:
+                        print('ignore key: ', k, ": ", v.size())
+
+                ema_params = mp_trainer.state_dict_to_master_params(
+                    model_ema_state_dict)
+
+                del state_dict
+
+        # print('ema mark 3, ', model_name, flush=True)
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(ema_params)
+        # print('ema mark 4, ', model_name, flush=True)
+        # del ema_params
+        return ema_params
+
+    # def _load_ema_parameters(self, rate):
+    #     ema_params = copy.deepcopy(self.mp_trainer.master_params)
+
+    #     main_checkpoint, _ = find_resume_checkpoint() or self.resume_checkpoint
+    #     ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
+    #     if ema_checkpoint:
+    #         if dist.get_rank() == 0:
+    #             logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+    #             state_dict = dist_util.load_state_dict(
+    #                 ema_checkpoint, map_location=dist_util.dev()
+    #             )
+    #             ema_params = self.mp_trainer.state_dict_to_master_params(state_dict)
+
+    #     dist_util.sync_params(ema_params)
+    #     return ema_params
+
+    def _load_optimizer_state(self):
+        main_checkpoint, _ = find_resume_checkpoint() or self.resume_checkpoint
+        opt_checkpoint = bf.join(bf.dirname(main_checkpoint),
+                                 f"opt{self.resume_step:06}.pt")
+        if bf.exists(opt_checkpoint):
+            logger.log(
+                f"loading optimizer state from checkpoint: {opt_checkpoint}")
+            state_dict = dist_util.load_state_dict(
+                opt_checkpoint, map_location=dist_util.dev())
+            self.opt.load_state_dict(state_dict)
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+            batch, cond = next(self.data)
+            self.run_step(batch, cond)
+            if self.step % self.log_interval == 0:
+                logger.dumpkvs()
+            if self.step % self.save_interval == 0:
+                self.save()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+            self.step += 1
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+
+    def run_step(self, batch, cond):
+        self.forward_backward(batch, cond)
+        took_step = self.mp_trainer.optimize(self.opt)
+        if took_step:
+            self._update_ema()
+        self._anneal_lr()
+        self.log_step()
+
+    def forward_backward(self, batch, cond):
+        self.mp_trainer.zero_grad()
+        for i in range(0, batch.shape[0], self.microbatch):
+
+            # st()
+            with th.autocast(device_type=dist_util.dev(),
+                             dtype=th.float16,
+                             enabled=self.mp_trainer.use_amp):
+
+                micro = batch[i:i + self.microbatch].to(dist_util.dev())
+                micro_cond = {
+                    k: v[i:i + self.microbatch].to(dist_util.dev())
+                    for k, v in cond.items()
+                }
+                last_batch = (i + self.microbatch) >= batch.shape[0]
+                t, weights = self.schedule_sampler.sample(
+                    micro.shape[0], dist_util.dev())
+
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    micro,
+                    t,
+                    model_kwargs=micro_cond,
+                )
+
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                else:
+                    with self.ddp_model.no_sync():
+                        losses = compute_losses()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                loss = (losses["loss"] * weights).mean()
+                log_loss_dict(self.diffusion, t,
+                              {k: v * weights
+                               for k, v in losses.items()})
+
+            self.mp_trainer.backward(loss)
+
+    def _update_ema(self):
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            update_ema(params, self.mp_trainer.master_params, rate=rate)
+
+    def _anneal_lr(self):
+        if not self.lr_anneal_steps:
+            return
+        frac_done = (self.step + self.resume_step) / self.lr_anneal_steps
+        lr = self.lr * (1 - frac_done)
+        for param_group in self.opt.param_groups:
+            param_group["lr"] = lr
+
+    def log_step(self):
+        logger.logkv("step", self.step + self.resume_step)
+        logger.logkv("samples",
+                     (self.step + self.resume_step + 1) * self.global_batch)
+
+    @th.no_grad()
+    def _make_vis_img(self, pred):
+
+        # if True:
+        pred_depth = pred['image_depth']
+        pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                        pred_depth.min())
+
+
+        pred_depth = pred_depth.cpu()[0].permute(1, 2, 0).numpy()
+        pred_depth = (plt.cm.viridis(pred_depth[..., 0])[..., :3]) * 2 - 1
+        pred_depth = th.from_numpy(pred_depth).to(
+            pred['image_raw'].device).permute(2, 0, 1).unsqueeze(0)
+        # rend_normal = pred['rend_normal']
+
+        # if 'image_sr' in pred:
+
+        #     gen_img = pred['image_sr']
+
+        #     if pred['image_sr'].shape[-1] == 512:
+
+        #         pred_vis = th.cat([
+        #             micro['img_sr'],
+        #             self.pool_512(pred['image_raw']), gen_img,
+        #             self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+        #         ],
+        #                             dim=-1)
+
+        #     elif pred['image_sr'].shape[-1] == 128:
+
+        #         pred_vis = th.cat([
+        #             micro['img_sr'],
+        #             self.pool_128(pred['image_raw']), pred['image_sr'],
+        #             self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+        #         ],
+        #                             dim=-1)
+
+        # else:
+        gen_img = pred['image_raw']
+
+        pred_vis = th.cat(
+            [
+                gen_img,
+                # rend_normal,
+                pred_depth,
+            ],
+            dim=-1)  # B, 3, H, W
+        
+        return pred_vis
+
+    @th.inference_mode()
+    def render_video_given_triplane(self,
+                                    planes,
+                                    rec_model,
+                                    name_prefix='0',
+                                    save_img=False, 
+                                    render_reference=None, 
+                                    export_mesh=False):
+
+        planes *= self.triplane_scaling_divider  # if setting clip_denoised=True, the sampled planes will lie in [-1,1]. Thus, values beyond [+- std] will be abandoned in this version. Move to IN for later experiments.
+
+        # sr_w_code = getattr(self.ddp_rec_model.module.decoder, 'w_avg', None)
+        # sr_w_code = None
+        batch_size = planes.shape[0]
+
+        # if sr_w_code is not None:
+        #     sr_w_code = sr_w_code.reshape(1, 1,
+        #                                   -1).repeat_interleave(batch_size, 0)
+
+        # used during diffusion sampling inference
+        # if not save_img:
+
+        # ! mesh
+
+        if planes.shape[1] == 16:  # ffhq/car
+            ddpm_latent = {
+                self.latent_name: planes[:, :12],
+                'bg_plane': planes[:, 12:16],
+            }
+        else:
+            ddpm_latent = {
+                self.latent_name: planes,
+            }
+        
+        ddpm_latent.update(rec_model(latent=ddpm_latent, behaviour='decode_after_vae_no_render')) 
+
+        if export_mesh:
+        # if True:
+            # mesh_size = 512
+            # mesh_size = 256
+            mesh_size = 384
+            # mesh_size = 320
+            # mesh_thres = 3 # TODO, requires tuning
+            # mesh_thres = 5 # TODO, requires tuning
+            mesh_thres = 10 # TODO, requires tuning
+            import mcubes
+            import trimesh
+            dump_path = f'{logger.get_dir()}/mesh/'
+
+            os.makedirs(dump_path, exist_ok=True)
+
+            grid_out = rec_model(
+                latent=ddpm_latent,
+                grid_size=mesh_size,
+                behaviour='triplane_decode_grid',
+            )
+            
+            vtx, faces = mcubes.marching_cubes(grid_out['sigma'].squeeze(0).squeeze(-1).cpu().numpy(), mesh_thres)
+            vtx = vtx / (mesh_size - 1) * 2 - 1
+
+            # vtx_tensor = th.tensor(vtx, dtype=th.float32, device=dist_util.dev()).unsqueeze(0)
+            # vtx_colors = self.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].squeeze(0).cpu().numpy()  # (0, 1)
+            # vtx_colors = (vtx_colors * 255).astype(np.uint8)
+            
+            # mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+            mesh = trimesh.Trimesh(vertices=vtx, faces=faces,)
+
+            mesh_dump_path = os.path.join(dump_path, f'{name_prefix}.ply')
+            mesh.export(mesh_dump_path, 'ply')
+
+            print(f"Mesh dumped to {dump_path}")
+            del grid_out, mesh
+            th.cuda.empty_cache()
+            # return
+
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_{name_prefix}.mp4',
+            mode='I',
+            fps=15,
+            codec='libx264')
+
+        if planes.shape[1] == 16:  # ffhq/car
+            ddpm_latent = {
+                self.latent_name: planes[:, :12],
+                'bg_plane': planes[:, 12:16],
+            }
+        else:
+            ddpm_latent = {
+                self.latent_name: planes,
+            }
+        
+        # TODO, duplicated?
+        ddpm_latent.update(rec_model(latent=ddpm_latent, behaviour='decode_after_vae_no_render')) 
+
+        # planes = planes.repeat_interleave(micro['c'].shape[0], 0)
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        # micro_batchsize = 2
+        # micro_batchsize = batch_size
+
+        if render_reference is None:
+            render_reference = self.eval_data # compat
+        else: # use train_traj
+            for key in ['ins', 'bbox', 'caption']:
+                if key in render_reference:
+                    render_reference.pop(key)
+            # render_reference.pop('bbox')
+            # render_reference.pop('caption')
+
+            # compat lst for enumerate
+            render_reference = [ { k:v[idx:idx+1] for k, v in render_reference.items() } for idx in range(40) ]
+
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+        for i, batch in enumerate(tqdm(render_reference)):
+            micro = {
+                k: v.to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+                for k, v in batch.items() 
+            }
+            # micro = {'c': batch['c'].to(dist_util.dev()).repeat_interleave(batch_size, 0)}
+
+            # all_pred = []
+            pred = rec_model(
+                img=None,
+                c=micro['c'],
+                latent=ddpm_latent,
+                # latent={
+                #     # k: v.repeat_interleave(micro['c'].shape[0], 0) if v is not None else None
+                #     k: v.repeat_interleave(micro['c'].shape[0], 0) if v is not None else None
+                #     for k, v in ddpm_latent.items()
+                # },
+                behaviour='triplane_dec')
+            
+            # if True:
+            # pred_depth = pred['image_depth']
+            # pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+            #                                                 pred_depth.min())
+
+            # if 'image_sr' in pred:
+
+            #     gen_img = pred['image_sr']
+
+            #     if pred['image_sr'].shape[-1] == 512:
+
+            #         pred_vis = th.cat([
+            #             micro['img_sr'],
+            #             self.pool_512(pred['image_raw']), gen_img,
+            #             self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+            #         ],
+            #                           dim=-1)
+
+            #     elif pred['image_sr'].shape[-1] == 128:
+
+            #         pred_vis = th.cat([
+            #             micro['img_sr'],
+            #             self.pool_128(pred['image_raw']), pred['image_sr'],
+            #             self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+            #         ],
+            #                           dim=-1)
+
+            # else:
+            #     gen_img = pred['image_raw']
+
+            #     pred_vis = th.cat(
+            #         [
+            #             # self.pool_128(micro['img']),
+            #             self.pool_128(gen_img),
+            #             self.pool_128(pred_depth.repeat_interleave(3, dim=1))
+            #         ],
+            #         dim=-1)  # B, 3, H, W
+            pred_vis = self._make_vis_img(pred)
+
+            if save_img:
+                for batch_idx in range(gen_img.shape[0]):
+                    sampled_img = Image.fromarray(
+                        (gen_img[batch_idx].permute(1, 2, 0).cpu().numpy() *
+                         127.5 + 127.5).clip(0, 255).astype(np.uint8))
+                    if sampled_img.size != (512, 512):
+                        sampled_img = sampled_img.resize(
+                            (128, 128), Image.HAMMING)  # for shapenet
+                    sampled_img.save(logger.get_dir() +
+                                     '/FID_Cals/{}_{}.png'.format(
+                                         int(name_prefix) * batch_size +
+                                         batch_idx, i))
+                    # print('FID_Cals/{}_{}.png'.format(int(name_prefix)*batch_size+batch_idx, i))
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            # if vis.shape[0] > 1:
+            #     vis = np.concatenate(np.split(vis, vis.shape[0], axis=0),
+            #                          axis=-3)
+
+            # if not save_img:
+            for j in range(vis.shape[0]
+                        ):  # ! currently only export one plane at a time
+                video_out.append_data(vis[j])
+
+        # if not save_img:
+        video_out.close()
+        del video_out
+        print('logged video to: ',
+            f'{logger.get_dir()}/triplane_{name_prefix}.mp4')
+
+        del vis, pred_vis, micro, pred, 
+
+    def save(self):
+
+        def save_checkpoint(rate, params):
+            state_dict = self.mp_trainer.master_params_to_state_dict(params)
+            if dist.get_rank() == 0:
+                logger.log(f"saving model {rate}...")
+                if not rate:
+                    filename = f"model{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, self.mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        if dist.get_rank() == 0:
+            with bf.BlobFile(
+                    bf.join(get_blob_logdir(),
+                            f"opt{(self.step+self.resume_step):07d}.pt"),
+                    "wb",
+            ) as f:
+                th.save(self.opt.state_dict(), f)
+
+        dist.barrier()
+
+
+def parse_resume_step_from_filename(filename):
+    """
+    Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
+    checkpoint's number of steps.
+    """
+    # split1 = Path(filename).stem[-6:]
+    split1 = Path(filename).stem[-7:]
+    # split = filename.split("model")
+    # if len(split) < 2:
+    #     return 0
+    # split1 = split[-1].split(".")[0]
+    try:
+        return int(split1)
+    except ValueError:
+        print('fail to load model step', split1)
+        return 0
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+def find_resume_checkpoint(resume_checkpoint='', model_name='ddpm'):
+    # On your infrastructure, you may want to override this to automatically
+    # discover the latest checkpoint on your blob storage, etc.
+
+    if resume_checkpoint != '':
+        step = parse_resume_step_from_filename(resume_checkpoint)
+        split = resume_checkpoint.split("model")
+        resume_ckpt_path = str(
+            Path(split[0]) / f'model_{model_name}{step:07d}.pt')
+    else:
+        resume_ckpt_path = ''
+        step = 0
+
+    return resume_ckpt_path, step
+
+
+def find_ema_checkpoint(main_checkpoint, step, rate, model_name=''):
+    if main_checkpoint is None:
+        return None
+    if model_name == '':
+        filename = f"ema_{rate}_{(step):07d}.pt"
+    else:
+        filename = f"ema_{model_name}_{rate}_{(step):07d}.pt"
+    path = bf.join(bf.dirname(main_checkpoint), filename)
+    # print(path)
+    # st()
+    if bf.exists(path):
+        print('fine ema model', path)
+        return path
+    else:
+        print('fail to find ema model', path)
+    return None
+
+
+def log_loss_dict(diffusion, ts, losses):
+    for key, values in losses.items():
+        logger.logkv_mean(key, values.mean().item())
+        # Log the quantiles (four quartiles, in particular).
+        for sub_t, sub_loss in zip(ts.cpu().numpy(),
+                                   values.detach().cpu().numpy()):
+            quartile = int(4 * sub_t / diffusion.num_timesteps)
+            logger.logkv_mean(f"{key}_q{quartile}", sub_loss)
+
+
+def log_rec3d_loss_dict(loss_dict):
+    for key, values in loss_dict.items():
+        try:
+            logger.logkv_mean(key, values.mean().item())
+        except:
+            print('type error:', key)
+    
+
+
+def calc_average_loss(all_loss_dicts, verbose=True):
+    all_scores = {}  # todo, defaultdict
+    mean_all_scores = {}
+
+    for loss_dict in all_loss_dicts:
+        for k, v in loss_dict.items():
+            v = v.item()
+            if k not in all_scores:
+                # all_scores[f'{k}_val'] = [v]
+                all_scores[k] = [v]
+            else:
+                all_scores[k].append(v)
+
+    for k, v in all_scores.items():
+        mean = np.mean(v)
+        std = np.std(v)
+        if k in ['loss_lpis', 'loss_ssim']:
+            mean = 1 - mean
+        result_str = '{} average loss is {:.4f} +- {:.4f}'.format(k, mean, std)
+        mean_all_scores[k] = mean
+        if verbose:
+            print(result_str)
+
+    val_scores_for_logging = {
+        f'{k}_val': v
+        for k, v in mean_all_scores.items()
+    }
+    return val_scores_for_logging
\ No newline at end of file
diff --git a/guided_diffusion/train_util_accelerate.py b/guided_diffusion/train_util_accelerate.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9850ba04fbc27a25876157fb4ff0c5919f1657c
--- /dev/null
+++ b/guided_diffusion/train_util_accelerate.py
@@ -0,0 +1,500 @@
+import copy
+from pdb import set_trace as st
+import functools
+import os
+import numpy as np
+
+import blobfile as bf
+import torch as th
+import torch.distributed as dist
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+
+from . import dist_util, logger
+from .fp16_util import MixedPrecisionTrainer
+from .nn import update_ema
+from .resample import LossAwareSampler, UniformSampler
+
+from pathlib import Path
+
+# For ImageNet experiments, this was a good default value.
+# We found that the lg_loss_scale quickly climbed to
+# 20-21 within the first ~1K steps of training.
+INITIAL_LOG_LOSS_SCALE = 20.0
+
+# use_amp = True
+# use_amp = False
+# if use_amp:
+# logger.log('ddpm use AMP to accelerate training')
+
+
+class TrainLoop:
+
+    def __init__(
+        self,
+        *,
+        model,
+        diffusion,
+        data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        save_interval,
+        resume_checkpoint,
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        schedule_sampler=None,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        use_amp=False,
+    ):
+        self.model = model
+        self.diffusion = diffusion
+        self.data = data
+        self.batch_size = batch_size
+        self.microbatch = microbatch if microbatch > 0 else batch_size
+        self.lr = lr
+        self.ema_rate = ([ema_rate] if isinstance(ema_rate, float) else
+                         [float(x) for x in ema_rate.split(",")])
+        self.log_interval = log_interval
+        self.save_interval = save_interval
+        self.resume_checkpoint = resume_checkpoint
+        self.use_fp16 = use_fp16
+        self.fp16_scale_growth = fp16_scale_growth
+        self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
+        self.weight_decay = weight_decay
+        self.lr_anneal_steps = lr_anneal_steps
+
+        self.step = 0
+        self.resume_step = 0
+        self.global_batch = self.batch_size * dist.get_world_size()
+
+        self.sync_cuda = th.cuda.is_available()
+
+        self._load_and_sync_parameters()
+        self.mp_trainer = MixedPrecisionTrainer(
+            model=self.model,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            use_amp=use_amp,
+        )
+
+        self.opt = AdamW(self.mp_trainer.master_params,
+                         lr=self.lr,
+                         weight_decay=self.weight_decay)
+        if self.resume_step:
+            self._load_optimizer_state()
+            # Model was resumed, either due to a restart or a checkpoint
+            # being specified at the command line.
+            self.ema_params = [
+                self._load_ema_parameters(rate) for rate in self.ema_rate
+            ]
+        else:
+            self.ema_params = [
+                copy.deepcopy(self.mp_trainer.master_params)
+                for _ in range(len(self.ema_rate))
+            ]
+
+        # print('creating DDP')
+        if th.cuda.is_available():
+            self.use_ddp = True
+            self.ddp_model = DDP(
+                self.model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            if dist.get_world_size() > 1:
+                logger.warn("Distributed training requires CUDA. "
+                            "Gradients will not be synchronized properly!")
+            self.use_ddp = False
+            self.ddp_model = self.model
+        # print('creating DDP done')
+
+    def _load_and_sync_parameters(self):
+        resume_checkpoint, resume_step = find_resume_checkpoint(
+        ) or self.resume_checkpoint
+
+        if resume_checkpoint:
+            if not Path(resume_checkpoint).exists():
+                logger.log(
+                    f"failed to load model from checkpoint: {resume_checkpoint}, not exist"
+                )
+                return
+
+            # self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
+            self.resume_step = resume_step  # TODO, EMA part
+            if dist.get_rank() == 0:
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                # if model is None:
+                #     model = self.model
+                self.model.load_state_dict(
+                    dist_util.load_state_dict(
+                        resume_checkpoint,
+                        map_location=dist_util.dev(),
+                    ))
+
+        dist_util.sync_params(self.model.parameters())
+
+    def _load_ema_parameters(self,
+                             rate,
+                             model=None,
+                             mp_trainer=None,
+                             model_name='ddpm'):
+
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer
+        if model is None:
+            model = self.model
+
+        ema_params = copy.deepcopy(mp_trainer.master_params)
+
+        main_checkpoint, _ = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step,
+                                             rate, model_name)
+        if ema_checkpoint:
+
+            if dist_util.get_rank() == 0:
+
+                if not Path(ema_checkpoint).exists():
+                    logger.log(
+                        f"failed to load EMA from checkpoint: {ema_checkpoint}, not exist"
+                    )
+                    return
+
+                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                state_dict = dist_util.load_state_dict(
+                    ema_checkpoint, map_location=map_location)
+
+                model_ema_state_dict = model.state_dict()
+
+                for k, v in state_dict.items():
+                    if k in model_ema_state_dict.keys() and v.size(
+                    ) == model_ema_state_dict[k].size():
+                        model_ema_state_dict[k] = v
+                    else:
+                        logger.log('ignore key: ', k, ": ", v.size())
+
+                ema_params = mp_trainer.state_dict_to_master_params(
+                    model_ema_state_dict)
+
+                del state_dict
+
+        # print('ema mark 3, ', model_name, flush=True)
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(ema_params)
+        # print('ema mark 4, ', model_name, flush=True)
+        # del ema_params
+        return ema_params
+
+    def _load_ema_parameters_freezeAE(
+            self,
+            rate,
+            model,
+            #  mp_trainer=None,
+            model_name='rec'):
+
+        # if mp_trainer is None:
+        # mp_trainer = self.mp_trainer
+        # if model is None:
+        # model = self.model_rec
+
+        # ema_params = copy.deepcopy(mp_trainer.master_params)
+
+        main_checkpoint, _ = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step,
+                                             rate, model_name)
+        if ema_checkpoint:
+
+            if dist_util.get_rank() == 0:
+
+                if not Path(ema_checkpoint).exists():
+                    logger.log(
+                        f"failed to load EMA from checkpoint: {ema_checkpoint}, not exist"
+                    )
+                    return
+
+                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                state_dict = dist_util.load_state_dict(
+                    ema_checkpoint, map_location=map_location)
+
+                model_ema_state_dict = model.state_dict()
+
+                for k, v in state_dict.items():
+                    if k in model_ema_state_dict.keys() and v.size(
+                    ) == model_ema_state_dict[k].size():
+                        model_ema_state_dict[k] = v
+                    else:
+                        logger.log('ignore key: ', k, ": ", v.size())
+
+                ema_params = mp_trainer.state_dict_to_master_params(
+                    model_ema_state_dict)
+
+                del state_dict
+
+        # print('ema mark 3, ', model_name, flush=True)
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(ema_params)
+        # print('ema mark 4, ', model_name, flush=True)
+        # del ema_params
+        return ema_params
+
+    # def _load_ema_parameters(self, rate):
+    #     ema_params = copy.deepcopy(self.mp_trainer.master_params)
+
+    #     main_checkpoint, _ = find_resume_checkpoint() or self.resume_checkpoint
+    #     ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
+    #     if ema_checkpoint:
+    #         if dist.get_rank() == 0:
+    #             logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+    #             state_dict = dist_util.load_state_dict(
+    #                 ema_checkpoint, map_location=dist_util.dev()
+    #             )
+    #             ema_params = self.mp_trainer.state_dict_to_master_params(state_dict)
+
+    #     dist_util.sync_params(ema_params)
+    #     return ema_params
+
+    def _load_optimizer_state(self):
+        main_checkpoint, _ = find_resume_checkpoint() or self.resume_checkpoint
+        opt_checkpoint = bf.join(bf.dirname(main_checkpoint),
+                                 f"opt{self.resume_step:06}.pt")
+        if bf.exists(opt_checkpoint):
+            logger.log(
+                f"loading optimizer state from checkpoint: {opt_checkpoint}")
+            state_dict = dist_util.load_state_dict(
+                opt_checkpoint, map_location=dist_util.dev())
+            self.opt.load_state_dict(state_dict)
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+            batch, cond = next(self.data)
+            self.run_step(batch, cond)
+            if self.step % self.log_interval == 0:
+                logger.dumpkvs()
+            if self.step % self.save_interval == 0:
+                self.save()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+            self.step += 1
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+
+    def run_step(self, batch, cond):
+        self.forward_backward(batch, cond)
+        took_step = self.mp_trainer.optimize(self.opt)
+        if took_step:
+            self._update_ema()
+        self._anneal_lr()
+        self.log_step()
+
+    def forward_backward(self, batch, cond):
+        self.mp_trainer.zero_grad()
+        for i in range(0, batch.shape[0], self.microbatch):
+
+            # st()
+            with th.autocast(device_type=dist_util.dev(),
+                             dtype=th.float16,
+                             enabled=self.mp_trainer.use_amp):
+
+                micro = batch[i:i + self.microbatch].to(dist_util.dev())
+                micro_cond = {
+                    k: v[i:i + self.microbatch].to(dist_util.dev())
+                    for k, v in cond.items()
+                }
+                last_batch = (i + self.microbatch) >= batch.shape[0]
+                t, weights = self.schedule_sampler.sample(
+                    micro.shape[0], dist_util.dev())
+
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    micro,
+                    t,
+                    model_kwargs=micro_cond,
+                )
+
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                else:
+                    with self.ddp_model.no_sync():
+                        losses = compute_losses()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                loss = (losses["loss"] * weights).mean()
+                log_loss_dict(self.diffusion, t,
+                              {k: v * weights
+                               for k, v in losses.items()})
+
+            self.mp_trainer.backward(loss)
+
+    def _update_ema(self):
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            update_ema(params, self.mp_trainer.master_params, rate=rate)
+
+    def _anneal_lr(self):
+        if not self.lr_anneal_steps:
+            return
+        frac_done = (self.step + self.resume_step) / self.lr_anneal_steps
+        lr = self.lr * (1 - frac_done)
+        for param_group in self.opt.param_groups:
+            param_group["lr"] = lr
+
+    def log_step(self):
+        logger.logkv("step", self.step + self.resume_step)
+        logger.logkv("samples",
+                     (self.step + self.resume_step + 1) * self.global_batch)
+
+    def save(self):
+
+        def save_checkpoint(rate, params):
+            state_dict = self.mp_trainer.master_params_to_state_dict(params)
+            if dist.get_rank() == 0:
+                logger.log(f"saving model {rate}...")
+                if not rate:
+                    filename = f"model{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, self.mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        if dist.get_rank() == 0:
+            with bf.BlobFile(
+                    bf.join(get_blob_logdir(),
+                            f"opt{(self.step+self.resume_step):07d}.pt"),
+                    "wb",
+            ) as f:
+                th.save(self.opt.state_dict(), f)
+
+        dist.barrier()
+
+
+def parse_resume_step_from_filename(filename):
+    """
+    Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
+    checkpoint's number of steps.
+    """
+    split1 = Path(filename).stem[-6:]
+    # split = filename.split("model")
+    # if len(split) < 2:
+    #     return 0
+    # split1 = split[-1].split(".")[0]
+    try:
+        return int(split1)
+    except ValueError:
+        print('fail to load model step', split1)
+        return 0
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+def find_resume_checkpoint(resume_checkpoint='', model_name='ddpm'):
+    # On your infrastructure, you may want to override this to automatically
+    # discover the latest checkpoint on your blob storage, etc.
+
+    if resume_checkpoint != '':
+        step = parse_resume_step_from_filename(resume_checkpoint)
+        split = resume_checkpoint.split("model")
+        resume_ckpt_path = str(
+            Path(split[0]) / f'model_{model_name}{step:07d}.pt')
+    else:
+        resume_ckpt_path = ''
+        step = 0
+
+    return resume_ckpt_path, step
+
+
+def find_ema_checkpoint(main_checkpoint, step, rate, model_name=''):
+    if main_checkpoint is None:
+        return None
+    if model_name == '':
+        filename = f"ema_{rate}_{(step):07d}.pt"
+    else:
+        filename = f"ema_{model_name}_{rate}_{(step):07d}.pt"
+    path = bf.join(bf.dirname(main_checkpoint), filename)
+    # print(path)
+    # st()
+    if bf.exists(path):
+        print('load ema model', path)
+        return path
+    else:
+        print('fail to load ema model', path)
+    return None
+
+
+def log_loss_dict(diffusion, ts, losses):
+    for key, values in losses.items():
+        logger.logkv_mean(key, values.mean().item())
+        # Log the quantiles (four quartiles, in particular).
+        for sub_t, sub_loss in zip(ts.cpu().numpy(),
+                                   values.detach().cpu().numpy()):
+            quartile = int(4 * sub_t / diffusion.num_timesteps)
+            logger.logkv_mean(f"{key}_q{quartile}", sub_loss)
+
+
+def log_rec3d_loss_dict(loss_dict):
+    for key, values in loss_dict.items():
+        logger.logkv_mean(key, values.mean().item())
+
+
+def calc_average_loss(all_loss_dicts):
+    all_scores = {}  # todo, defaultdict
+    mean_all_scores = {}
+
+    for loss_dict in all_loss_dicts:
+        for k, v in loss_dict.items():
+            v = v.item()
+            if k not in all_scores:
+                # all_scores[f'{k}_val'] = [v]
+                all_scores[k] = [v]
+            else:
+                all_scores[k].append(v)
+
+    for k, v in all_scores.items():
+        mean = np.mean(v)
+        std = np.std(v)
+        if k in ['loss_lpis', 'loss_ssim']:
+            mean = 1 - mean
+        result_str = '{} average loss is {:.4f} +- {:.4f}'.format(k, mean, std)
+        mean_all_scores[k] = mean
+        print(result_str)
+
+    val_scores_for_logging = {
+        f'{k}_val': v
+        for k, v in mean_all_scores.items()
+    }
+    return val_scores_for_logging
\ No newline at end of file
diff --git a/guided_diffusion/unet.py b/guided_diffusion/unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f617306558e70d9a1a255e4883e1864c778bb78
--- /dev/null
+++ b/guided_diffusion/unet.py
@@ -0,0 +1,1116 @@
+from abc import abstractmethod
+
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+from pdb import set_trace as st
+from einops import rearrange, repeat
+
+from .fp16_util import convert_module_to_f16, convert_module_to_f32
+from .nn import (
+    checkpoint,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    zero_module,
+    normalization,
+    timestep_embedding,
+)
+
+from ldm.modules.attention import SpatialTransformer
+
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(
+            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
+        )
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+# class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+#     """
+#     A sequential module that passes timestep embeddings to the children that
+#     support it as an extra input.
+#     """
+
+#     def forward(self, x, emb):
+#         for layer in self:
+#             if isinstance(layer, TimestepBlock):
+#                 x = layer(x, emb)
+#             else:
+#                 x = layer(x)
+#         return x
+
+# from LDM openaimodel.py
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, context=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, SpatialTransformer):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
+
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+
+        with th.autocast(enabled=False, device_type='cuda'): # only handles the execusion, not data typeS
+            if self.dims == 3:
+                x = F.interpolate(
+                    x.float(), (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+                )
+            else:
+                x = F.interpolate(x.float(), scale_factor=2, mode="nearest")
+
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        # return checkpoint(
+        #     self._forward, (x, emb), self.parameters(), self.use_checkpoint
+        # )
+        return self._forward(x, emb)
+
+    def _forward(self, x, emb):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    # def forward(self, x):
+    #     return checkpoint(self._forward, (x,), self.parameters(), True)
+
+    # def _forward(self, x):
+    #     b, c, *spatial = x.shape
+    #     x = x.reshape(b, c, -1)
+    #     qkv = self.qkv(self.norm(x))
+    #     h = self.attention(qkv)
+    #     h = self.proj_out(h)
+    #     return (x + h).reshape(b, c, *spatial)
+
+    # ! disable checkpoint here since it is incompatible with torch.amp
+    def forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        mixed_prediction=False,
+        use_spatial_transformer=False,    # custom transformer support
+        transformer_depth=1,              # custom transformer support
+        context_dim=-1,                 # custom transformer support
+        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+        mixing_logit_init=-6,
+        roll_out=False,**kwargs
+    ):
+        super().__init__()
+        self.roll_out = roll_out
+        if context_dim == -1:
+            context_dim = None
+
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            # from omegaconf.listconfig import ListConfig
+            # if type(context_dim) == ListConfig:
+            #     context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        # follow LSGM
+        self.mixed_prediction = mixed_prediction  # This enables mixed prediction
+        if self.mixed_prediction:
+            if self.roll_out:
+                init = mixing_logit_init * th.ones(size=[1, in_channels*3, 1, 1]) # hard coded for now
+            else:
+                init = mixing_logit_init * th.ones(size=[1, in_channels, 1, 1]) # hard coded for now
+            self.mixing_logit = th.nn.Parameter(init, requires_grad=True)
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=dim_head,
+                            use_new_attention_order=use_new_attention_order,
+                        ) if not use_spatial_transformer else SpatialTransformer(
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer(
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=dim_head,
+                            use_new_attention_order=use_new_attention_order,
+                        ) if not use_spatial_transformer else SpatialTransformer(
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+        if self.predict_codebook_ids:
+            self.id_predictor = nn.Sequential(
+            normalization(ch),
+            conv_nd(dims, model_channels, n_embed, 1),
+            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps=None, context=None, y=None, get_attr='', **kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+
+        if isinstance(context, dict):
+            context = context['crossattn'] # sgm conditioner compat
+
+        if get_attr != '': # not breaking the forward hooks
+            return getattr(self, get_attr)
+        
+
+        # if forward
+        # assert context is not None
+
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        # st()
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.roll_out:
+            # x = rearrange(x, 'b (n c) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+            # ! fix order bug
+            x = rearrange(x, 'b (c n) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+
+        if self.num_classes is not None:
+            assert y.shape == (x.shape[0],)
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+        h = h.type(x.dtype)
+
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            h = self.out(h)
+            if self.roll_out:
+                # return rearrange(h, 'b c h (n w) -> b (n c) h w', n=3)
+                # ! fix order bug
+                return rearrange(h, 'b c h (n w) -> b (c n) h w', n=3)
+            return h
+
+
+class SuperResModel(UNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(self, image_size, in_channels, *args, **kwargs):
+        super().__init__(image_size, in_channels * 2, *args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
+        x = th.cat([x, upsampled], dim=1)
+        return super().forward(x, timesteps, **kwargs)
+
+
+class EncoderUNetModel(nn.Module):
+    """
+    The half UNet model with attention and timestep embedding.
+
+    For usage, see UNet.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        pool="adaptive",
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        ch = int(channel_mult[0] * model_channels)
+        self.input_blocks = nn.ModuleList(
+            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
+        )
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+        self.pool = pool
+        if pool == "adaptive":
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                nn.AdaptiveAvgPool2d((1, 1)),
+                zero_module(conv_nd(dims, ch, out_channels, 1)),
+                nn.Flatten(),
+            )
+        elif pool == "attention":
+            assert num_head_channels != -1
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                AttentionPool2d(
+                    (image_size // ds), ch, num_head_channels, out_channels
+                ),
+            )
+        elif pool == "spatial":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                nn.ReLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        elif pool == "spatial_v2":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                normalization(2048),
+                nn.SiLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        else:
+            raise NotImplementedError(f"Unexpected {pool} pooling")
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :return: an [N x K] Tensor of outputs.
+        """
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        results = []
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            if self.pool.startswith("spatial"):
+                results.append(h.type(x.dtype).mean(dim=(2, 3)))
+        h = self.middle_block(h, emb)
+        if self.pool.startswith("spatial"):
+            results.append(h.type(x.dtype).mean(dim=(2, 3)))
+            h = th.cat(results, axis=-1)
+            return self.out(h)
+        else:
+            h = h.type(x.dtype)
+            return self.out(h)
+
+
+class UNetModelWithHint(UNetModel):
+    def __init__(self, image_size, in_channels, model_channels, hint_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, mixed_prediction=False, use_spatial_transformer=False, transformer_depth=1, context_dim=-1, n_embed=None, legacy=True, mixing_logit_init=-6, roll_out=False):
+        super().__init__(image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout, channel_mult, conv_resample, dims, num_classes, use_checkpoint, use_fp16, num_heads, num_head_channels, num_heads_upsample, use_scale_shift_norm, resblock_updown, use_new_attention_order, mixed_prediction, use_spatial_transformer, transformer_depth, context_dim, n_embed, legacy, mixing_logit_init, roll_out)
+    
+        # lite encoder, borrowed from ControlNet
+
+        self.input_hint_block = TimestepEmbedSequential( # f=8
+            conv_nd(dims, hint_channels, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 32, 32, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 96, 96, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+        )
+
+    def forward(self, x, hint, timesteps=None, context=None, y=None, get_attr='', **kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+
+        # st()
+
+        # if forward
+        # assert context is not None
+
+        assert context is not None
+        # assert (y is not None) == (
+        #     self.num_classes is not None
+        # ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.roll_out:
+            x = rearrange(x, 'b (n c) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+
+        # if self.num_classes is not None:
+        #     assert y.shape == (x.shape[0],)
+        #     emb = emb + self.label_emb(y)
+
+        guided_hint = self.input_hint_block(hint, emb, context)
+
+        if self.roll_out:
+            guided_hint = repeat(guided_hint, 'b c h w -> b c h (n w)', n=3) # torch.Size([84, 4, 32, 96])
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            if guided_hint is not None:
+                h = module(h, emb, context) # B, 320, 32, 96
+
+                h += guided_hint
+                guided_hint = None
+            else:
+                h = module(h, emb, context)
+
+            hs.append(h)
+
+        h = self.middle_block(h, emb, context)
+
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+        h = h.type(x.dtype)
+
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            h = self.out(h)
+            if self.roll_out:
+                return rearrange(h, 'b c h (n w) -> b (n c) h w', n=3)
+            return h
\ No newline at end of file
diff --git a/guided_diffusion/unet_adm.py b/guided_diffusion/unet_adm.py
new file mode 100644
index 0000000000000000000000000000000000000000..99df55076b10f2b30f0a7e8712995194d731b8ae
--- /dev/null
+++ b/guided_diffusion/unet_adm.py
@@ -0,0 +1,934 @@
+from abc import abstractmethod
+
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .fp16_util import convert_module_to_f16, convert_module_to_f32
+from .nn import (
+    checkpoint,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    zero_module,
+    normalization,
+    timestep_embedding,
+)
+
+from ldm.modules.attention import SpatialTransformer
+
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(
+            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
+        )
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+# class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+#     """
+#     A sequential module that passes timestep embeddings to the children that
+#     support it as an extra input.
+#     """
+
+#     def forward(self, x, emb):
+#         for layer in self:
+#             if isinstance(layer, TimestepBlock):
+#                 x = layer(x, emb)
+#             else:
+#                 x = layer(x)
+#         return x
+
+# from LDM openaimodel.py
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, context=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, SpatialTransformer):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
+
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, (x, emb), self.parameters(), self.use_checkpoint
+        )
+
+    def _forward(self, x, emb):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    # def forward(self, x):
+    #     return checkpoint(self._forward, (x,), self.parameters(), True)
+
+    # def _forward(self, x):
+    #     b, c, *spatial = x.shape
+    #     x = x.reshape(b, c, -1)
+    #     qkv = self.qkv(self.norm(x))
+    #     h = self.attention(qkv)
+    #     h = self.proj_out(h)
+    #     return (x + h).reshape(b, c, *spatial)
+
+    # ! disable checkpoint here since it is incompatible with torch.amp
+    def forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        mixed_prediction=False,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        # follow LSGM
+        self.mixed_prediction = mixed_prediction  # This enables mixed prediction
+        if self.mixed_prediction:
+            init = -6 * th.ones(size=[1, in_channels, 1, 1]) # hard coded for now
+            self.mixing_logit = th.nn.Parameter(init, requires_grad=True)
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+
+        ch = input_ch = int(channel_mult[0] * model_channels)
+        self.input_blocks = nn.ModuleList(
+            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
+        )
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(model_channels * mult),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(model_channels * mult)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    # @th.autocast(device_type='cuda')
+    def forward(self, x, timesteps, y=None, get_attr=''):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+
+        if get_attr != '': # not breaking the forward hooks
+            return getattr(self, get_attr)
+
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        if self.num_classes is not None:
+            assert y.shape == (x.shape[0],)
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            hs.append(h)
+        h = self.middle_block(h, emb)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        return self.out(h)
+
+
+class SuperResModel(UNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(self, image_size, in_channels, *args, **kwargs):
+        super().__init__(image_size, in_channels * 2, *args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
+        x = th.cat([x, upsampled], dim=1)
+        return super().forward(x, timesteps, **kwargs)
+
+
+class EncoderUNetModel(nn.Module):
+    """
+    The half UNet model with attention and timestep embedding.
+
+    For usage, see UNet.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        pool="adaptive",
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        ch = int(channel_mult[0] * model_channels)
+        self.input_blocks = nn.ModuleList(
+            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
+        )
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+        self.pool = pool
+        if pool == "adaptive":
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                nn.AdaptiveAvgPool2d((1, 1)),
+                zero_module(conv_nd(dims, ch, out_channels, 1)),
+                nn.Flatten(),
+            )
+        elif pool == "attention":
+            assert num_head_channels != -1
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                AttentionPool2d(
+                    (image_size // ds), ch, num_head_channels, out_channels
+                ),
+            )
+        elif pool == "spatial":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                nn.ReLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        elif pool == "spatial_v2":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                normalization(2048),
+                nn.SiLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        else:
+            raise NotImplementedError(f"Unexpected {pool} pooling")
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :return: an [N x K] Tensor of outputs.
+        """
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        results = []
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            if self.pool.startswith("spatial"):
+                results.append(h.type(x.dtype).mean(dim=(2, 3)))
+        h = self.middle_block(h, emb)
+        if self.pool.startswith("spatial"):
+            results.append(h.type(x.dtype).mean(dim=(2, 3)))
+            h = th.cat(results, axis=-1)
+            return self.out(h)
+        else:
+            h = h.type(x.dtype)
+            return self.out(h)
diff --git a/ldm/__init__.py b/ldm/__init__.py
new file mode 100644
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diff --git a/ldm/__pycache__/__init__.cpython-310.pyc b/ldm/__pycache__/__init__.cpython-310.pyc
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diff --git a/ldm/data/__init__.py b/ldm/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/data/util.py b/ldm/data/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b60ceb2349e3bd7900ff325740e2022d2903b1c
--- /dev/null
+++ b/ldm/data/util.py
@@ -0,0 +1,24 @@
+import torch
+
+from ldm.modules.midas.api import load_midas_transform
+
+
+class AddMiDaS(object):
+    def __init__(self, model_type):
+        super().__init__()
+        self.transform = load_midas_transform(model_type)
+
+    def pt2np(self, x):
+        x = ((x + 1.0) * .5).detach().cpu().numpy()
+        return x
+
+    def np2pt(self, x):
+        x = torch.from_numpy(x) * 2 - 1.
+        return x
+
+    def __call__(self, sample):
+        # sample['jpg'] is tensor hwc in [-1, 1] at this point
+        x = self.pt2np(sample['jpg'])
+        x = self.transform({"image": x})["image"]
+        sample['midas_in'] = x
+        return sample
\ No newline at end of file
diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d122549995ce2cd64092c81a58419ed4a15a02fd
--- /dev/null
+++ b/ldm/models/autoencoder.py
@@ -0,0 +1,219 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from ldm.util import instantiate_from_config
+from ldm.modules.ema import LitEma
+
+
+class AutoencoderKL(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 ema_decay=None,
+                 learn_logvar=False
+                 ):
+        super().__init__()
+        self.learn_logvar = learn_logvar
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        assert ddconfig["double_z"]
+        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.embed_dim = embed_dim
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+
+        self.use_ema = ema_decay is not None
+        if self.use_ema:
+            self.ema_decay = ema_decay
+            assert 0. < ema_decay < 1.
+            self.model_ema = LitEma(self, decay=ema_decay)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path}")
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        posterior = DiagonalGaussianDistribution(moments)
+        return posterior
+
+    def decode(self, z):
+        z = self.post_quant_conv(z)
+        dec = self.decoder(z)
+        return dec
+
+    def forward(self, input, sample_posterior=True):
+        posterior = self.encode(input)
+        if sample_posterior:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+        dec = self.decode(z)
+        return dec, posterior
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+
+        if optimizer_idx == 0:
+            # train encoder+decoder+logvar
+            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # train the discriminator
+            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                                last_layer=self.get_last_layer(), split="train")
+
+            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, postfix=""):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+                                        last_layer=self.get_last_layer(), split="val"+postfix)
+
+        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+                                            last_layer=self.get_last_layer(), split="val"+postfix)
+
+        self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
+            self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
+        if self.learn_logvar:
+            print(f"{self.__class__.__name__}: Learning logvar")
+            ae_params_list.append(self.loss.logvar)
+        opt_ae = torch.optim.Adam(ae_params_list,
+                                  lr=lr, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr, betas=(0.5, 0.9))
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    @torch.no_grad()
+    def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if not only_inputs:
+            xrec, posterior = self(x)
+            if x.shape[1] > 3:
+                # colorize with random projection
+                assert xrec.shape[1] > 3
+                x = self.to_rgb(x)
+                xrec = self.to_rgb(xrec)
+            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+            log["reconstructions"] = xrec
+            if log_ema or self.use_ema:
+                with self.ema_scope():
+                    xrec_ema, posterior_ema = self(x)
+                    if x.shape[1] > 3:
+                        # colorize with random projection
+                        assert xrec_ema.shape[1] > 3
+                        xrec_ema = self.to_rgb(xrec_ema)
+                    log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
+                    log["reconstructions_ema"] = xrec_ema
+        log["inputs"] = x
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+    def __init__(self, *args, vq_interface=False, **kwargs):
+        self.vq_interface = vq_interface
+        super().__init__()
+
+    def encode(self, x, *args, **kwargs):
+        return x
+
+    def decode(self, x, *args, **kwargs):
+        return x
+
+    def quantize(self, x, *args, **kwargs):
+        if self.vq_interface:
+            return x, None, [None, None, None]
+        return x
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
diff --git a/ldm/models/diffusion/__init__.py b/ldm/models/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
new file mode 100644
index 0000000000000000000000000000000000000000..27ead0ea914c64c747b64e690662899fb3801144
--- /dev/null
+++ b/ldm/models/diffusion/ddim.py
@@ -0,0 +1,336 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
+
+
+class DDIMSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               dynamic_threshold=None,
+               ucg_schedule=None,
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                ctmp = conditioning[list(conditioning.keys())[0]]
+                while isinstance(ctmp, list): ctmp = ctmp[0]
+                cbs = ctmp.shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != batch_size:
+                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+        samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    dynamic_threshold=dynamic_threshold,
+                                                    ucg_schedule=ucg_schedule
+                                                    )
+        return samples, intermediates
+
+    @torch.no_grad()
+    def ddim_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+                      ucg_schedule=None):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            if ucg_schedule is not None:
+                assert len(ucg_schedule) == len(time_range)
+                unconditional_guidance_scale = ucg_schedule[i]
+
+            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      dynamic_threshold=dynamic_threshold)
+            img, pred_x0 = outs
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      dynamic_threshold=None):
+        b, *_, device = *x.shape, x.device
+
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            model_output = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = dict()
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [torch.cat([
+                            unconditional_conditioning[k][i],
+                            c[k][i]]) for i in range(len(c[k]))]
+                    else:
+                        c_in[k] = torch.cat([
+                                unconditional_conditioning[k],
+                                c[k]])
+            elif isinstance(c, list):
+                c_in = list()
+                assert isinstance(unconditional_conditioning, list)
+                for i in range(len(c)):
+                    c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
+            model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+        if self.model.parameterization == "v":
+            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+        else:
+            e_t = model_output
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps", 'not implemented'
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        if self.model.parameterization != "v":
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        else:
+            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+        if dynamic_threshold is not None:
+            raise NotImplementedError()
+
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    @torch.no_grad()
+    def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+               unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+        num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
+
+        assert t_enc <= num_reference_steps
+        num_steps = t_enc
+
+        if use_original_steps:
+            alphas_next = self.alphas_cumprod[:num_steps]
+            alphas = self.alphas_cumprod_prev[:num_steps]
+        else:
+            alphas_next = self.ddim_alphas[:num_steps]
+            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+        x_next = x0
+        intermediates = []
+        inter_steps = []
+        for i in tqdm(range(num_steps), desc='Encoding Image'):
+            t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
+            if unconditional_guidance_scale == 1.:
+                noise_pred = self.model.apply_model(x_next, t, c)
+            else:
+                assert unconditional_conditioning is not None
+                e_t_uncond, noise_pred = torch.chunk(
+                    self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+                                           torch.cat((unconditional_conditioning, c))), 2)
+                noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+            weighted_noise_pred = alphas_next[i].sqrt() * (
+                    (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+            x_next = xt_weighted + weighted_noise_pred
+            if return_intermediates and i % (
+                    num_steps // return_intermediates) == 0 and i < num_steps - 1:
+                intermediates.append(x_next)
+                inter_steps.append(i)
+            elif return_intermediates and i >= num_steps - 2:
+                intermediates.append(x_next)
+                inter_steps.append(i)
+            if callback: callback(i)
+
+        out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+        if return_intermediates:
+            out.update({'intermediates': intermediates})
+        return x_next, out
+
+    @torch.no_grad()
+    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+        # fast, but does not allow for exact reconstruction
+        # t serves as an index to gather the correct alphas
+        if use_original_steps:
+            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+        else:
+            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+        if noise is None:
+            noise = torch.randn_like(x0)
+        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+    @torch.no_grad()
+    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+               use_original_steps=False, callback=None):
+
+        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        timesteps = timesteps[:t_start]
+
+        time_range = np.flip(timesteps)
+        total_steps = timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+        x_dec = x_latent
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+                                          unconditional_guidance_scale=unconditional_guidance_scale,
+                                          unconditional_conditioning=unconditional_conditioning)
+            if callback: callback(i)
+        return x_dec
\ No newline at end of file
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f71a44af48c8cba8e97849b7e6813b3e6f9fe83c
--- /dev/null
+++ b/ldm/models/diffusion/ddpm.py
@@ -0,0 +1,1797 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 make_it_fit=False,
+                 ucg_training=None,
+                 reset_ema=False,
+                 reset_num_ema_updates=False,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapper(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        self.make_it_fit = make_it_fit
+        if reset_ema: assert exists(ckpt_path)
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+            if reset_ema:
+                assert self.use_ema
+                print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+                self.model_ema = LitEma(self.model)
+        if reset_num_ema_updates:
+            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+            assert self.use_ema
+            self.model_ema.reset_num_updates()
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+        else:
+            self.register_buffer('logvar', logvar)
+
+        self.ucg_training = ucg_training or dict()
+        if self.ucg_training:
+            self.ucg_prng = np.random.RandomState()
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        elif self.parameterization == "v":
+            lvlb_weights = torch.ones_like(self.betas ** 2 / (
+                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+        else:
+            raise NotImplementedError("mu not supported")
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    @torch.no_grad()
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        if self.make_it_fit:
+            n_params = len([name for name, _ in
+                            itertools.chain(self.named_parameters(),
+                                            self.named_buffers())])
+            for name, param in tqdm(
+                    itertools.chain(self.named_parameters(),
+                                    self.named_buffers()),
+                    desc="Fitting old weights to new weights",
+                    total=n_params
+            ):
+                if not name in sd:
+                    continue
+                old_shape = sd[name].shape
+                new_shape = param.shape
+                assert len(old_shape) == len(new_shape)
+                if len(new_shape) > 2:
+                    # we only modify first two axes
+                    assert new_shape[2:] == old_shape[2:]
+                # assumes first axis corresponds to output dim
+                if not new_shape == old_shape:
+                    new_param = param.clone()
+                    old_param = sd[name]
+                    if len(new_shape) == 1:
+                        for i in range(new_param.shape[0]):
+                            new_param[i] = old_param[i % old_shape[0]]
+                    elif len(new_shape) >= 2:
+                        for i in range(new_param.shape[0]):
+                            for j in range(new_param.shape[1]):
+                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
+
+                        n_used_old = torch.ones(old_shape[1])
+                        for j in range(new_param.shape[1]):
+                            n_used_old[j % old_shape[1]] += 1
+                        n_used_new = torch.zeros(new_shape[1])
+                        for j in range(new_param.shape[1]):
+                            n_used_new[j] = n_used_old[j % old_shape[1]]
+
+                        n_used_new = n_used_new[None, :]
+                        while len(n_used_new.shape) < len(new_shape):
+                            n_used_new = n_used_new.unsqueeze(-1)
+                        new_param /= n_used_new
+
+                    sd[name] = new_param
+
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys:\n {missing}")
+        if len(unexpected) > 0:
+            print(f"\nUnexpected Keys:\n {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def predict_start_from_z_and_v(self, x_t, t, v):
+        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+        )
+
+    def predict_eps_from_z_and_v(self, x_t, t, v):
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_v(self, x, noise, t):
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+        )
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "v":
+            target = self.get_v(x_start, noise, t)
+        else:
+            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        for k in self.ucg_training:
+            p = self.ucg_training[k]["p"]
+            val = self.ucg_training[k]["val"]
+            if val is None:
+                val = ""
+            for i in range(len(batch[k])):
+                if self.ucg_prng.choice(2, p=[1 - p, p]):
+                    batch[k][i] = val
+
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusion(DDPM):
+    """main class"""
+
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 force_null_conditioning=False,
+                 *args, **kwargs):
+        self.force_null_conditioning = force_null_conditioning
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        reset_ema = kwargs.pop("reset_ema", False)
+        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+            if reset_ema:
+                assert self.use_ema
+                print(
+                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+                self.model_ema = LitEma(self.model)
+        if reset_num_ema_updates:
+            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+            assert self.use_ema
+            self.model_ema.reset_num_updates()
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                       force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None, return_x=False):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+        if self.model.conditioning_key is not None and not self.force_null_conditioning:
+            if cond_key is None:
+                cond_key = self.cond_stage_key
+            if cond_key != self.first_stage_key:
+                if cond_key in ['caption', 'coordinates_bbox', "txt"]:
+                    xc = batch[cond_key]
+                elif cond_key in ['class_label', 'cls']:
+                    xc = batch
+                else:
+                    xc = super().get_input(batch, cond_key).to(self.device)
+            else:
+                xc = x
+            if not self.cond_stage_trainable or force_c_encode:
+                if isinstance(xc, dict) or isinstance(xc, list):
+                    c = self.get_learned_conditioning(xc)
+                else:
+                    c = self.get_learned_conditioning(xc.to(self.device))
+            else:
+                c = xc
+            if bs is not None:
+                c = c[:bs]
+
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                ckey = __conditioning_keys__[self.model.conditioning_key]
+                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+        else:
+            c = None
+            xc = None
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                c = {'pos_x': pos_x, 'pos_y': pos_y}
+        out = [z, c]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_x:
+            out.extend([x])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+        return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+        if isinstance(cond, dict):
+            # hybrid case, cond is expected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "v":
+            target = self.get_v(x_start, noise, t)
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None, **kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+                                                         shape, cond, verbose=False, **kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True, **kwargs)
+
+        return samples, intermediates
+
+    @torch.no_grad()
+    def get_unconditional_conditioning(self, batch_size, null_label=None):
+        if null_label is not None:
+            xc = null_label
+            if isinstance(xc, ListConfig):
+                xc = list(xc)
+            if isinstance(xc, dict) or isinstance(xc, list):
+                c = self.get_learned_conditioning(xc)
+            else:
+                if hasattr(xc, "to"):
+                    xc = xc.to(self.device)
+                c = self.get_learned_conditioning(xc)
+        else:
+            if self.cond_stage_key in ["class_label", "cls"]:
+                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+                return self.get_learned_conditioning(xc)
+            else:
+                raise NotImplementedError("todo")
+        if isinstance(c, list):  # in case the encoder gives us a list
+            for i in range(len(c)):
+                c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+        else:
+            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+        return c
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+                   use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ['class_label', "cls"]:
+                try:
+                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                    log['conditioning'] = xc
+                except KeyError:
+                    # probably no "human_label" in batch
+                    pass
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                             ddim_steps=ddim_steps, eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+        if unconditional_guidance_scale > 1.0:
+            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            if self.model.conditioning_key == "crossattn-adm":
+                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        if inpaint:
+            # make a simple center square
+            b, h, w = z.shape[0], z.shape[2], z.shape[3]
+            mask = torch.ones(N, h, w).to(self.device)
+            # zeros will be filled in
+            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+            mask = mask[:, None, ...]
+            with ema_scope("Plotting Inpaint"):
+                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+            x_samples = self.decode_first_stage(samples.to(self.device))
+            log["samples_inpainting"] = x_samples
+            log["mask"] = mask
+
+            # outpaint
+            mask = 1. - mask
+            with ema_scope("Plotting Outpaint"):
+                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+            x_samples = self.decode_first_stage(samples.to(self.device))
+            log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            if not self.sequential_cross_attn:
+                cc = torch.cat(c_crossattn, 1)
+            else:
+                cc = c_crossattn
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'hybrid-adm':
+            assert c_adm is not None
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+        elif self.conditioning_key == 'crossattn-adm':
+            assert c_adm is not None
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc, y=c_adm)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class LatentUpscaleDiffusion(LatentDiffusion):
+    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
+        assert not self.cond_stage_trainable
+        self.instantiate_low_stage(low_scale_config)
+        self.low_scale_key = low_scale_key
+        self.noise_level_key = noise_level_key
+
+    def instantiate_low_stage(self, config):
+        model = instantiate_from_config(config)
+        self.low_scale_model = model.eval()
+        self.low_scale_model.train = disabled_train
+        for param in self.low_scale_model.parameters():
+            param.requires_grad = False
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
+        if not log_mode:
+            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
+        else:
+            z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                                  force_c_encode=True, return_original_cond=True, bs=bs)
+        x_low = batch[self.low_scale_key][:bs]
+        x_low = rearrange(x_low, 'b h w c -> b c h w')
+        x_low = x_low.to(memory_format=torch.contiguous_format).float()
+        zx, noise_level = self.low_scale_model(x_low)
+        if self.noise_level_key is not None:
+            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
+            raise NotImplementedError('TODO')
+
+        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
+        if log_mode:
+            # TODO: maybe disable if too expensive
+            x_low_rec = self.low_scale_model.decode(zx)
+            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
+                   unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
+                                                                          log_mode=True)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        log["x_lr"] = x_low
+        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ['class_label', 'cls']:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+        if unconditional_guidance_scale > 1.0:
+            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            # TODO explore better "unconditional" choices for the other keys
+            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
+            uc = dict()
+            for k in c:
+                if k == "c_crossattn":
+                    assert isinstance(c[k], list) and len(c[k]) == 1
+                    uc[k] = [uc_tmp]
+                elif k == "c_adm":  # todo: only run with text-based guidance?
+                    assert isinstance(c[k], torch.Tensor)
+                    #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
+                    uc[k] = c[k]
+                elif isinstance(c[k], list):
+                    uc[k] = [c[k][i] for i in range(len(c[k]))]
+                else:
+                    uc[k] = c[k]
+
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        if plot_progressive_rows:
+            with ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        return log
+
+
+class LatentFinetuneDiffusion(LatentDiffusion):
+    """
+         Basis for different finetunas, such as inpainting or depth2image
+         To disable finetuning mode, set finetune_keys to None
+    """
+
+    def __init__(self,
+                 concat_keys: tuple,
+                 finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
+                                "model_ema.diffusion_modelinput_blocks00weight"
+                                ),
+                 keep_finetune_dims=4,
+                 # if model was trained without concat mode before and we would like to keep these channels
+                 c_concat_log_start=None,  # to log reconstruction of c_concat codes
+                 c_concat_log_end=None,
+                 *args, **kwargs
+                 ):
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", list())
+        super().__init__(*args, **kwargs)
+        self.finetune_keys = finetune_keys
+        self.concat_keys = concat_keys
+        self.keep_dims = keep_finetune_dims
+        self.c_concat_log_start = c_concat_log_start
+        self.c_concat_log_end = c_concat_log_end
+        if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
+        if exists(ckpt_path):
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+
+            # make it explicit, finetune by including extra input channels
+            if exists(self.finetune_keys) and k in self.finetune_keys:
+                new_entry = None
+                for name, param in self.named_parameters():
+                    if name in self.finetune_keys:
+                        print(
+                            f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
+                        new_entry = torch.zeros_like(param)  # zero init
+                assert exists(new_entry), 'did not find matching parameter to modify'
+                new_entry[:, :self.keep_dims, ...] = sd[k]
+                sd[k] = new_entry
+
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+                   use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
+        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ['class_label', 'cls']:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
+            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+                                                         batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+        if unconditional_guidance_scale > 1.0:
+            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            uc_cat = c_cat
+            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+                                                 batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc_full,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        return log
+
+
+class LatentInpaintDiffusion(LatentFinetuneDiffusion):
+    """
+    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
+    e.g. mask as concat and text via cross-attn.
+    To disable finetuning mode, set finetune_keys to None
+     """
+
+    def __init__(self,
+                 concat_keys=("mask", "masked_image"),
+                 masked_image_key="masked_image",
+                 *args, **kwargs
+                 ):
+        super().__init__(concat_keys, *args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        c_cat = list()
+        for ck in self.concat_keys:
+            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            bchw = z.shape
+            if ck != self.masked_image_key:
+                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+            else:
+                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, *args, **kwargs):
+        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
+        log["masked_image"] = rearrange(args[0]["masked_image"],
+                                        'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+        return log
+
+
+class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
+    """
+    condition on monocular depth estimation
+    """
+
+    def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
+        super().__init__(concat_keys=concat_keys, *args, **kwargs)
+        self.depth_model = instantiate_from_config(depth_stage_config)
+        self.depth_stage_key = concat_keys[0]
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        assert len(self.concat_keys) == 1
+        c_cat = list()
+        for ck in self.concat_keys:
+            cc = batch[ck]
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            cc = self.depth_model(cc)
+            cc = torch.nn.functional.interpolate(
+                cc,
+                size=z.shape[2:],
+                mode="bicubic",
+                align_corners=False,
+            )
+
+            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
+                                                                                           keepdim=True)
+            cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, *args, **kwargs):
+        log = super().log_images(*args, **kwargs)
+        depth = self.depth_model(args[0][self.depth_stage_key])
+        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
+                               torch.amax(depth, dim=[1, 2, 3], keepdim=True)
+        log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
+        return log
+
+
+class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
+    """
+        condition on low-res image (and optionally on some spatial noise augmentation)
+    """
+    def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
+                 low_scale_config=None, low_scale_key=None, *args, **kwargs):
+        super().__init__(concat_keys=concat_keys, *args, **kwargs)
+        self.reshuffle_patch_size = reshuffle_patch_size
+        self.low_scale_model = None
+        if low_scale_config is not None:
+            print("Initializing a low-scale model")
+            assert exists(low_scale_key)
+            self.instantiate_low_stage(low_scale_config)
+            self.low_scale_key = low_scale_key
+
+    def instantiate_low_stage(self, config):
+        model = instantiate_from_config(config)
+        self.low_scale_model = model.eval()
+        self.low_scale_model.train = disabled_train
+        for param in self.low_scale_model.parameters():
+            param.requires_grad = False
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        assert len(self.concat_keys) == 1
+        # optionally make spatial noise_level here
+        c_cat = list()
+        noise_level = None
+        for ck in self.concat_keys:
+            cc = batch[ck]
+            cc = rearrange(cc, 'b h w c -> b c h w')
+            if exists(self.reshuffle_patch_size):
+                assert isinstance(self.reshuffle_patch_size, int)
+                cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
+                               p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            if exists(self.low_scale_model) and ck == self.low_scale_key:
+                cc, noise_level = self.low_scale_model(cc)
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        if exists(noise_level):
+            all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
+        else:
+            all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, *args, **kwargs):
+        log = super().log_images(*args, **kwargs)
+        log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
+        return log
diff --git a/ldm/models/diffusion/dpm_solver/__init__.py b/ldm/models/diffusion/dpm_solver/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7427f38c07530afbab79154ea8aaf88c4bf70a08
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/__init__.py
@@ -0,0 +1 @@
+from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py
new file mode 100644
index 0000000000000000000000000000000000000000..095e5ba3ce0b1aa7f4b3f1e2e5d8fff7cfe6dc8c
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/dpm_solver.py
@@ -0,0 +1,1154 @@
+import torch
+import torch.nn.functional as F
+import math
+from tqdm import tqdm
+
+
+class NoiseScheduleVP:
+    def __init__(
+            self,
+            schedule='discrete',
+            betas=None,
+            alphas_cumprod=None,
+            continuous_beta_0=0.1,
+            continuous_beta_1=20.,
+    ):
+        """Create a wrapper class for the forward SDE (VP type).
+        ***
+        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+        ***
+        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+            log_alpha_t = self.marginal_log_mean_coeff(t)
+            sigma_t = self.marginal_std(t)
+            lambda_t = self.marginal_lambda(t)
+        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+            t = self.inverse_lambda(lambda_t)
+        ===============================================================
+        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+        1. For discrete-time DPMs:
+            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+                t_i = (i + 1) / N
+            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+            Args:
+                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
+                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
+                and
+                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+        2. For continuous-time DPMs:
+            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+            schedule are the default settings in DDPM and improved-DDPM:
+            Args:
+                beta_min: A `float` number. The smallest beta for the linear schedule.
+                beta_max: A `float` number. The largest beta for the linear schedule.
+                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+                T: A `float` number. The ending time of the forward process.
+        ===============================================================
+        Args:
+            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+                    'linear' or 'cosine' for continuous-time DPMs.
+        Returns:
+            A wrapper object of the forward SDE (VP type).
+
+        ===============================================================
+        Example:
+        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', betas=betas)
+        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+        # For continuous-time DPMs (VPSDE), linear schedule:
+        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+        """
+
+        if schedule not in ['discrete', 'linear', 'cosine']:
+            raise ValueError(
+                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
+                    schedule))
+
+        self.schedule = schedule
+        if schedule == 'discrete':
+            if betas is not None:
+                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+            else:
+                assert alphas_cumprod is not None
+                log_alphas = 0.5 * torch.log(alphas_cumprod)
+            self.total_N = len(log_alphas)
+            self.T = 1.
+            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+            self.log_alpha_array = log_alphas.reshape((1, -1,))
+        else:
+            self.total_N = 1000
+            self.beta_0 = continuous_beta_0
+            self.beta_1 = continuous_beta_1
+            self.cosine_s = 0.008
+            self.cosine_beta_max = 999.
+            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
+                        1. + self.cosine_s) / math.pi - self.cosine_s
+            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+            self.schedule = schedule
+            if schedule == 'cosine':
+                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+                self.T = 0.9946
+            else:
+                self.T = 1.
+
+    def marginal_log_mean_coeff(self, t):
+        """
+        Compute log(alpha_t) of a given continuous-time label t in [0, T].
+        """
+        if self.schedule == 'discrete':
+            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
+                                  self.log_alpha_array.to(t.device)).reshape((-1))
+        elif self.schedule == 'linear':
+            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+        elif self.schedule == 'cosine':
+            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
+            return log_alpha_t
+
+    def marginal_alpha(self, t):
+        """
+        Compute alpha_t of a given continuous-time label t in [0, T].
+        """
+        return torch.exp(self.marginal_log_mean_coeff(t))
+
+    def marginal_std(self, t):
+        """
+        Compute sigma_t of a given continuous-time label t in [0, T].
+        """
+        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+    def marginal_lambda(self, t):
+        """
+        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+        """
+        log_mean_coeff = self.marginal_log_mean_coeff(t)
+        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+        return log_mean_coeff - log_std
+
+    def inverse_lambda(self, lamb):
+        """
+        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+        """
+        if self.schedule == 'linear':
+            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            Delta = self.beta_0 ** 2 + tmp
+            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+        elif self.schedule == 'discrete':
+            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
+                               torch.flip(self.t_array.to(lamb.device), [1]))
+            return t.reshape((-1,))
+        else:
+            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
+                        1. + self.cosine_s) / math.pi - self.cosine_s
+            t = t_fn(log_alpha)
+            return t
+
+
+def model_wrapper(
+        model,
+        noise_schedule,
+        model_type="noise",
+        model_kwargs={},
+        guidance_type="uncond",
+        condition=None,
+        unconditional_condition=None,
+        guidance_scale=1.,
+        classifier_fn=None,
+        classifier_kwargs={},
+):
+    """Create a wrapper function for the noise prediction model.
+    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+    We support four types of the diffusion model by setting `model_type`:
+        1. "noise": noise prediction model. (Trained by predicting noise).
+        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+        3. "v": velocity prediction model. (Trained by predicting the velocity).
+            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+                arXiv preprint arXiv:2202.00512 (2022).
+            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+                arXiv preprint arXiv:2210.02303 (2022).
+
+        4. "score": marginal score function. (Trained by denoising score matching).
+            Note that the score function and the noise prediction model follows a simple relationship:
+            ```
+                noise(x_t, t) = -sigma_t * score(x_t, t)
+            ```
+    We support three types of guided sampling by DPMs by setting `guidance_type`:
+        1. "uncond": unconditional sampling by DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+            The input `classifier_fn` has the following format:
+            ``
+                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+            ``
+            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+            ``
+            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+                arXiv preprint arXiv:2207.12598 (2022).
+
+    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+    or continuous-time labels (i.e. epsilon to T).
+    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+    ``
+        def model_fn(x, t_continuous) -> noise:
+            t_input = get_model_input_time(t_continuous)
+            return noise_pred(model, x, t_input, **model_kwargs)
+    ``
+    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+    ===============================================================
+    Args:
+        model: A diffusion model with the corresponding format described above.
+        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+        model_type: A `str`. The parameterization type of the diffusion model.
+                    "noise" or "x_start" or "v" or "score".
+        model_kwargs: A `dict`. A dict for the other inputs of the model function.
+        guidance_type: A `str`. The type of the guidance for sampling.
+                    "uncond" or "classifier" or "classifier-free".
+        condition: A pytorch tensor. The condition for the guided sampling.
+                    Only used for "classifier" or "classifier-free" guidance type.
+        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+                    Only used for "classifier-free" guidance type.
+        guidance_scale: A `float`. The scale for the guided sampling.
+        classifier_fn: A classifier function. Only used for the classifier guidance.
+        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+    Returns:
+        A noise prediction model that accepts the noised data and the continuous time as the inputs.
+    """
+
+    def get_model_input_time(t_continuous):
+        """
+        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+        For continuous-time DPMs, we just use `t_continuous`.
+        """
+        if noise_schedule.schedule == 'discrete':
+            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+        else:
+            return t_continuous
+
+    def noise_pred_fn(x, t_continuous, cond=None):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        t_input = get_model_input_time(t_continuous)
+        if cond is None:
+            output = model(x, t_input, **model_kwargs)
+        else:
+            output = model(x, t_input, cond, **model_kwargs)
+        if model_type == "noise":
+            return output
+        elif model_type == "x_start":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+        elif model_type == "v":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+        elif model_type == "score":
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return -expand_dims(sigma_t, dims) * output
+
+    def cond_grad_fn(x, t_input):
+        """
+        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+        """
+        with torch.enable_grad():
+            x_in = x.detach().requires_grad_(True)
+            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+            return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+    def model_fn(x, t_continuous):
+        """
+        The noise predicition model function that is used for DPM-Solver.
+        """
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        if guidance_type == "uncond":
+            return noise_pred_fn(x, t_continuous)
+        elif guidance_type == "classifier":
+            assert classifier_fn is not None
+            t_input = get_model_input_time(t_continuous)
+            cond_grad = cond_grad_fn(x, t_input)
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            noise = noise_pred_fn(x, t_continuous)
+            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+        elif guidance_type == "classifier-free":
+            if guidance_scale == 1. or unconditional_condition is None:
+                return noise_pred_fn(x, t_continuous, cond=condition)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t_continuous] * 2)
+                c_in = torch.cat([unconditional_condition, condition])
+                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+                return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+    assert model_type in ["noise", "x_start", "v"]
+    assert guidance_type in ["uncond", "classifier", "classifier-free"]
+    return model_fn
+
+
+class DPM_Solver:
+    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
+        """Construct a DPM-Solver.
+        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
+        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
+        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
+            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
+            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
+        Args:
+            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
+                ``
+                def model_fn(x, t_continuous):
+                    return noise
+                ``
+            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
+            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
+            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
+
+        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
+        """
+        self.model = model_fn
+        self.noise_schedule = noise_schedule
+        self.predict_x0 = predict_x0
+        self.thresholding = thresholding
+        self.max_val = max_val
+
+    def noise_prediction_fn(self, x, t):
+        """
+        Return the noise prediction model.
+        """
+        return self.model(x, t)
+
+    def data_prediction_fn(self, x, t):
+        """
+        Return the data prediction model (with thresholding).
+        """
+        noise = self.noise_prediction_fn(x, t)
+        dims = x.dim()
+        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+        if self.thresholding:
+            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
+            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+            x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def model_fn(self, x, t):
+        """
+        Convert the model to the noise prediction model or the data prediction model.
+        """
+        if self.predict_x0:
+            return self.data_prediction_fn(x, t)
+        else:
+            return self.noise_prediction_fn(x, t)
+
+    def get_time_steps(self, skip_type, t_T, t_0, N, device):
+        """Compute the intermediate time steps for sampling.
+        Args:
+            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+                - 'logSNR': uniform logSNR for the time steps.
+                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            N: A `int`. The total number of the spacing of the time steps.
+            device: A torch device.
+        Returns:
+            A pytorch tensor of the time steps, with the shape (N + 1,).
+        """
+        if skip_type == 'logSNR':
+            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+            return self.noise_schedule.inverse_lambda(logSNR_steps)
+        elif skip_type == 'time_uniform':
+            return torch.linspace(t_T, t_0, N + 1).to(device)
+        elif skip_type == 'time_quadratic':
+            t_order = 2
+            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
+            return t
+        else:
+            raise ValueError(
+                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+        """
+        Get the order of each step for sampling by the singlestep DPM-Solver.
+        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
+        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
+            - If order == 1:
+                We take `steps` of DPM-Solver-1 (i.e. DDIM).
+            - If order == 2:
+                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
+                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
+                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
+            - If order == 3:
+                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
+                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
+                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
+        ============================================
+        Args:
+            order: A `int`. The max order for the solver (2 or 3).
+            steps: A `int`. The total number of function evaluations (NFE).
+            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+                - 'logSNR': uniform logSNR for the time steps.
+                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            device: A torch device.
+        Returns:
+            orders: A list of the solver order of each step.
+        """
+        if order == 3:
+            K = steps // 3 + 1
+            if steps % 3 == 0:
+                orders = [3, ] * (K - 2) + [2, 1]
+            elif steps % 3 == 1:
+                orders = [3, ] * (K - 1) + [1]
+            else:
+                orders = [3, ] * (K - 1) + [2]
+        elif order == 2:
+            if steps % 2 == 0:
+                K = steps // 2
+                orders = [2, ] * K
+            else:
+                K = steps // 2 + 1
+                orders = [2, ] * (K - 1) + [1]
+        elif order == 1:
+            K = 1
+            orders = [1, ] * steps
+        else:
+            raise ValueError("'order' must be '1' or '2' or '3'.")
+        if skip_type == 'logSNR':
+            # To reproduce the results in DPM-Solver paper
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+        else:
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
+                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
+        return timesteps_outer, orders
+
+    def denoise_to_zero_fn(self, x, s):
+        """
+        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+        """
+        return self.data_prediction_fn(x, s)
+
+    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
+        """
+        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_1 = torch.expm1(-h)
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_t = (
+                    expand_dims(sigma_t / sigma_s, dims) * x
+                    - expand_dims(alpha_t * phi_1, dims) * model_s
+            )
+            if return_intermediate:
+                return x_t, {'model_s': model_s}
+            else:
+                return x_t
+        else:
+            phi_1 = torch.expm1(h)
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                    - expand_dims(sigma_t * phi_1, dims) * model_s
+            )
+            if return_intermediate:
+                return x_t, {'model_s': model_s}
+            else:
+                return x_t
+
+    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
+                                            solver_type='dpm_solver'):
+        """
+        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            r1: A `float`. The hyperparameter of the second-order solver.
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        if r1 is None:
+            r1 = 0.5
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        lambda_s1 = lambda_s + r1 * h
+        s1 = ns.inverse_lambda(lambda_s1)
+        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
+            s1), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
+        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_11 = torch.expm1(-r1 * h)
+            phi_1 = torch.expm1(-h)
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_s1 = (
+                    expand_dims(sigma_s1 / sigma_s, dims) * x
+                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
+            )
+            model_s1 = self.model_fn(x_s1, s1)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
+                                    model_s1 - model_s)
+                )
+        else:
+            phi_11 = torch.expm1(r1 * h)
+            phi_1 = torch.expm1(h)
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_s1 = (
+                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
+            )
+            model_s1 = self.model_fn(x_s1, s1)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
+                )
+        if return_intermediate:
+            return x_t, {'model_s': model_s, 'model_s1': model_s1}
+        else:
+            return x_t
+
+    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
+                                           return_intermediate=False, solver_type='dpm_solver'):
+        """
+        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            r1: A `float`. The hyperparameter of the third-order solver.
+            r2: A `float`. The hyperparameter of the third-order solver.
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
+                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        if r1 is None:
+            r1 = 1. / 3.
+        if r2 is None:
+            r2 = 2. / 3.
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        lambda_s1 = lambda_s + r1 * h
+        lambda_s2 = lambda_s + r2 * h
+        s1 = ns.inverse_lambda(lambda_s1)
+        s2 = ns.inverse_lambda(lambda_s2)
+        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
+            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
+            s2), ns.marginal_std(t)
+        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_11 = torch.expm1(-r1 * h)
+            phi_12 = torch.expm1(-r2 * h)
+            phi_1 = torch.expm1(-h)
+            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
+            phi_2 = phi_1 / h + 1.
+            phi_3 = phi_2 / h - 0.5
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            if model_s1 is None:
+                x_s1 = (
+                        expand_dims(sigma_s1 / sigma_s, dims) * x
+                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
+                )
+                model_s1 = self.model_fn(x_s1, s1)
+            x_s2 = (
+                    expand_dims(sigma_s2 / sigma_s, dims) * x
+                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
+                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
+            )
+            model_s2 = self.model_fn(x_s2, s2)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
+                )
+            elif solver_type == 'taylor':
+                D1_0 = (1. / r1) * (model_s1 - model_s)
+                D1_1 = (1. / r2) * (model_s2 - model_s)
+                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        + expand_dims(alpha_t * phi_2, dims) * D1
+                        - expand_dims(alpha_t * phi_3, dims) * D2
+                )
+        else:
+            phi_11 = torch.expm1(r1 * h)
+            phi_12 = torch.expm1(r2 * h)
+            phi_1 = torch.expm1(h)
+            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
+            phi_2 = phi_1 / h - 1.
+            phi_3 = phi_2 / h - 0.5
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            if model_s1 is None:
+                x_s1 = (
+                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
+                )
+                model_s1 = self.model_fn(x_s1, s1)
+            x_s2 = (
+                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
+                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
+                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
+            )
+            model_s2 = self.model_fn(x_s2, s2)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
+                )
+            elif solver_type == 'taylor':
+                D1_0 = (1. / r1) * (model_s1 - model_s)
+                D1_1 = (1. / r2) * (model_s2 - model_s)
+                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - expand_dims(sigma_t * phi_2, dims) * D1
+                        - expand_dims(sigma_t * phi_3, dims) * D2
+                )
+
+        if return_intermediate:
+            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
+        else:
+            return x_t
+
+    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
+        """
+        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        ns = self.noise_schedule
+        dims = x.dim()
+        model_prev_1, model_prev_0 = model_prev_list
+        t_prev_1, t_prev_0 = t_prev_list
+        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
+            t_prev_0), ns.marginal_lambda(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h_0 = lambda_prev_0 - lambda_prev_1
+        h = lambda_t - lambda_prev_0
+        r0 = h_0 / h
+        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+        if self.predict_x0:
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(sigma_t / sigma_prev_0, dims) * x
+                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(sigma_t / sigma_prev_0, dims) * x
+                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
+                )
+        else:
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
+                )
+        return x_t
+
+    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
+        """
+        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        ns = self.noise_schedule
+        dims = x.dim()
+        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
+        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
+        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
+            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h_1 = lambda_prev_1 - lambda_prev_2
+        h_0 = lambda_prev_0 - lambda_prev_1
+        h = lambda_t - lambda_prev_0
+        r0, r1 = h_0 / h, h_1 / h
+        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
+        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
+        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
+        if self.predict_x0:
+            x_t = (
+                    expand_dims(sigma_t / sigma_prev_0, dims) * x
+                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
+                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
+            )
+        else:
+            x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
+                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
+            )
+        return x_t
+
+    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
+                                     r2=None):
+        """
+        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+            r1: A `float`. The hyperparameter of the second-order or third-order solver.
+            r2: A `float`. The hyperparameter of the third-order solver.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if order == 1:
+            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
+        elif order == 2:
+            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
+                                                            solver_type=solver_type, r1=r1)
+        elif order == 3:
+            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
+                                                           solver_type=solver_type, r1=r1, r2=r2)
+        else:
+            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
+        """
+        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if order == 1:
+            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
+        elif order == 2:
+            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+        elif order == 3:
+            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+        else:
+            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
+                            solver_type='dpm_solver'):
+        """
+        The adaptive step size solver based on singlestep DPM-Solver.
+        Args:
+            x: A pytorch tensor. The initial value at time `t_T`.
+            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            h_init: A `float`. The initial step size (for logSNR).
+            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
+            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
+            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
+            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
+                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_0: A pytorch tensor. The approximated solution at time `t_0`.
+        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
+        """
+        ns = self.noise_schedule
+        s = t_T * torch.ones((x.shape[0],)).to(x)
+        lambda_s = ns.marginal_lambda(s)
+        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
+        h = h_init * torch.ones_like(s).to(x)
+        x_prev = x
+        nfe = 0
+        if order == 2:
+            r1 = 0.5
+            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
+            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+                                                                                               solver_type=solver_type,
+                                                                                               **kwargs)
+        elif order == 3:
+            r1, r2 = 1. / 3., 2. / 3.
+            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+                                                                                    return_intermediate=True,
+                                                                                    solver_type=solver_type)
+            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
+                                                                                              solver_type=solver_type,
+                                                                                              **kwargs)
+        else:
+            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
+        while torch.abs((s - t_0)).mean() > t_err:
+            t = ns.inverse_lambda(lambda_s + h)
+            x_lower, lower_noise_kwargs = lower_update(x, s, t)
+            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
+            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
+            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
+            E = norm_fn((x_higher - x_lower) / delta).max()
+            if torch.all(E <= 1.):
+                x = x_higher
+                s = t
+                x_prev = x_lower
+                lambda_s = ns.marginal_lambda(s)
+            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
+            nfe += order
+        print('adaptive solver nfe', nfe)
+        return x
+
+    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+               method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+               atol=0.0078, rtol=0.05,
+               ):
+        """
+        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
+        =====================================================
+        We support the following algorithms for both noise prediction model and data prediction model:
+            - 'singlestep':
+                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
+                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
+                The total number of function evaluations (NFE) == `steps`.
+                Given a fixed NFE == `steps`, the sampling procedure is:
+                    - If `order` == 1:
+                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
+                    - If `order` == 2:
+                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
+                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
+                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+                    - If `order` == 3:
+                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
+                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
+            - 'multistep':
+                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
+                We initialize the first `order` values by lower order multistep solvers.
+                Given a fixed NFE == `steps`, the sampling procedure is:
+                    Denote K = steps.
+                    - If `order` == 1:
+                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
+                    - If `order` == 2:
+                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
+                    - If `order` == 3:
+                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
+            - 'singlestep_fixed':
+                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
+                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
+            - 'adaptive':
+                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
+                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
+                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
+                (NFE) and the sample quality.
+                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
+                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
+        =====================================================
+        Some advices for choosing the algorithm:
+            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
+                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
+                e.g.
+                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
+                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
+                            skip_type='time_uniform', method='singlestep')
+            - For **guided sampling with large guidance scale** by DPMs:
+                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
+                e.g.
+                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
+                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
+                            skip_type='time_uniform', method='multistep')
+        We support three types of `skip_type`:
+            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
+            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
+            - 'time_quadratic': quadratic time for the time steps.
+        =====================================================
+        Args:
+            x: A pytorch tensor. The initial value at time `t_start`
+                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
+            steps: A `int`. The total number of function evaluations (NFE).
+            t_start: A `float`. The starting time of the sampling.
+                If `T` is None, we use self.noise_schedule.T (default is 1.0).
+            t_end: A `float`. The ending time of the sampling.
+                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
+                e.g. if total_N == 1000, we have `t_end` == 1e-3.
+                For discrete-time DPMs:
+                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
+                For continuous-time DPMs:
+                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
+            order: A `int`. The order of DPM-Solver.
+            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
+            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
+            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
+                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
+                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
+                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
+                for diffusion models sampling by diffusion SDEs for low-resolutional images
+                (such as CIFAR-10). However, we observed that such trick does not matter for
+                high-resolutional images. As it needs an additional NFE, we do not recommend
+                it for high-resolutional images.
+            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
+                Only valid for `method=multistep` and `steps < 15`. We empirically find that
+                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
+                (especially for steps <= 10). So we recommend to set it to be `True`.
+            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
+            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+        Returns:
+            x_end: A pytorch tensor. The approximated solution at time `t_end`.
+        """
+        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+        t_T = self.noise_schedule.T if t_start is None else t_start
+        device = x.device
+        if method == 'adaptive':
+            with torch.no_grad():
+                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
+                                             solver_type=solver_type)
+        elif method == 'multistep':
+            assert steps >= order
+            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+            assert timesteps.shape[0] - 1 == steps
+            with torch.no_grad():
+                vec_t = timesteps[0].expand((x.shape[0]))
+                model_prev_list = [self.model_fn(x, vec_t)]
+                t_prev_list = [vec_t]
+                # Init the first `order` values by lower order multistep DPM-Solver.
+                for init_order in tqdm(range(1, order), desc="DPM init order"):
+                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
+                                                         solver_type=solver_type)
+                    model_prev_list.append(self.model_fn(x, vec_t))
+                    t_prev_list.append(vec_t)
+                # Compute the remaining values by `order`-th order multistep DPM-Solver.
+                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
+                    vec_t = timesteps[step].expand(x.shape[0])
+                    if lower_order_final and steps < 15:
+                        step_order = min(order, steps + 1 - step)
+                    else:
+                        step_order = order
+                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
+                                                         solver_type=solver_type)
+                    for i in range(order - 1):
+                        t_prev_list[i] = t_prev_list[i + 1]
+                        model_prev_list[i] = model_prev_list[i + 1]
+                    t_prev_list[-1] = vec_t
+                    # We do not need to evaluate the final model value.
+                    if step < steps:
+                        model_prev_list[-1] = self.model_fn(x, vec_t)
+        elif method in ['singlestep', 'singlestep_fixed']:
+            if method == 'singlestep':
+                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
+                                                                                              skip_type=skip_type,
+                                                                                              t_T=t_T, t_0=t_0,
+                                                                                              device=device)
+            elif method == 'singlestep_fixed':
+                K = steps // order
+                orders = [order, ] * K
+                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
+            for i, order in enumerate(orders):
+                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
+                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
+                                                      N=order, device=device)
+                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
+                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
+                h = lambda_inner[-1] - lambda_inner[0]
+                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
+                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
+                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
+        if denoise_to_zero:
+            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+        return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+    """
+    A piecewise linear function y = f(x), using xp and yp as keypoints.
+    We implement f(x) in a differentiable way (i.e. applicable for autograd).
+    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+    Args:
+        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+        yp: PyTorch tensor with shape [C, K].
+    Returns:
+        The function values f(x), with shape [N, C].
+    """
+    N, K = x.shape[0], xp.shape[1]
+    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+    x_idx = torch.argmin(x_indices, dim=2)
+    cand_start_idx = x_idx - 1
+    start_idx = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(1, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+    start_idx2 = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(0, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+    return cand
+
+
+def expand_dims(v, dims):
+    """
+    Expand the tensor `v` to the dim `dims`.
+    Args:
+        `v`: a PyTorch tensor with shape [N].
+        `dim`: a `int`.
+    Returns:
+        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+    """
+    return v[(...,) + (None,) * (dims - 1)]
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d137b8cf36718c1c58faa09f9dd919e5fb2977b
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/sampler.py
@@ -0,0 +1,87 @@
+"""SAMPLING ONLY."""
+import torch
+
+from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
+
+
+MODEL_TYPES = {
+    "eps": "noise",
+    "v": "v"
+}
+
+
+class DPMSolverSampler(object):
+    def __init__(self, model, **kwargs):
+        super().__init__()
+        self.model = model
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+
+        print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
+
+        device = self.model.betas.device
+        if x_T is None:
+            img = torch.randn(size, device=device)
+        else:
+            img = x_T
+
+        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+        model_fn = model_wrapper(
+            lambda x, t, c: self.model.apply_model(x, t, c),
+            ns,
+            model_type=MODEL_TYPES[self.model.parameterization],
+            guidance_type="classifier-free",
+            condition=conditioning,
+            unconditional_condition=unconditional_conditioning,
+            guidance_scale=unconditional_guidance_scale,
+        )
+
+        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
+        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+
+        return x.to(device), None
\ No newline at end of file
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
new file mode 100644
index 0000000000000000000000000000000000000000..7002a365d27168ced0a04e9a4d83e088f8284eae
--- /dev/null
+++ b/ldm/models/diffusion/plms.py
@@ -0,0 +1,244 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
+
+
+class PLMSSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        if ddim_eta != 0:
+            raise ValueError('ddim_eta must be 0 for PLMS')
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               dynamic_threshold=None,
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for PLMS sampling is {size}')
+
+        samples, intermediates = self.plms_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    dynamic_threshold=dynamic_threshold,
+                                                    )
+        return samples, intermediates
+
+    @torch.no_grad()
+    def plms_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      dynamic_threshold=None):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+        old_eps = []
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      old_eps=old_eps, t_next=ts_next,
+                                      dynamic_threshold=dynamic_threshold)
+            img, pred_x0, e_t = outs
+            old_eps.append(e_t)
+            if len(old_eps) >= 4:
+                old_eps.pop(0)
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
+                      dynamic_threshold=None):
+        b, *_, device = *x.shape, x.device
+
+        def get_model_output(x, t):
+            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                e_t = self.model.apply_model(x, t, c)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t] * 2)
+                c_in = torch.cat([unconditional_conditioning, c])
+                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+            if score_corrector is not None:
+                assert self.model.parameterization == "eps"
+                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+            return e_t
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+        def get_x_prev_and_pred_x0(e_t, index):
+            # select parameters corresponding to the currently considered timestep
+            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+            # current prediction for x_0
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+            if quantize_denoised:
+                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+            if dynamic_threshold is not None:
+                pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
+            # direction pointing to x_t
+            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+            if noise_dropout > 0.:
+                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+            return x_prev, pred_x0
+
+        e_t = get_model_output(x, t)
+        if len(old_eps) == 0:
+            # Pseudo Improved Euler (2nd order)
+            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+            e_t_next = get_model_output(x_prev, t_next)
+            e_t_prime = (e_t + e_t_next) / 2
+        elif len(old_eps) == 1:
+            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (3 * e_t - old_eps[-1]) / 2
+        elif len(old_eps) == 2:
+            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+        elif len(old_eps) >= 3:
+            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+        return x_prev, pred_x0, e_t
diff --git a/ldm/models/diffusion/sampling_util.py b/ldm/models/diffusion/sampling_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eff02be6d7c54d43ee6680636ac0698dd3b3f33
--- /dev/null
+++ b/ldm/models/diffusion/sampling_util.py
@@ -0,0 +1,22 @@
+import torch
+import numpy as np
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions.
+    From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def norm_thresholding(x0, value):
+    s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
+    return x0 * (value / s)
+
+
+def spatial_norm_thresholding(x0, value):
+    # b c h w
+    s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
+    return x0 * (value / s)
\ No newline at end of file
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diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5f67923e2b008d05dd69eab4cea8f6d5ce85451
--- /dev/null
+++ b/ldm/modules/attention.py
@@ -0,0 +1,778 @@
+from typing import List, Optional, Tuple
+
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from packaging import version
+from pdb import set_trace as st
+
+from ldm.modules.diffusionmodules.util import checkpoint
+
+# from torch.nn import LayerNorm
+try:
+    from apex.normalization import FusedRMSNorm as RMSNorm
+except:
+    from dit.norm import RMSNorm
+
+
+# CrossAttn precision handling
+import os
+_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
+from xformers.ops import MemoryEfficientAttentionFlashAttentionOp, MemoryEfficientAttentionCutlassOp
+# from xformers.ops import RMSNorm, fmha, rope_padded
+# import apex
+# from apex.normalization import FusedRMSNorm as RMSNorm
+
+if version.parse(torch.__version__) >= version.parse("2.0.0"):
+    SDP_IS_AVAILABLE = True
+    # from torch.backends.cuda import SDPBackend, sdp_kernel
+    from torch.nn.attention import sdpa_kernel, SDPBackend
+
+    BACKEND_MAP = {
+        SDPBackend.MATH: {
+            "enable_math": True,
+            "enable_flash": False,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.FLASH_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": True,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.EFFICIENT_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": False,
+            "enable_mem_efficient": True,
+        },
+        None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True},
+    }
+else:
+    from contextlib import nullcontext
+
+    SDP_IS_AVAILABLE = False
+    sdpa_kernel = nullcontext
+    BACKEND_MAP = {}
+    logpy.warn(
+        f"No SDP backend available, likely because you are running in pytorch "
+        f"versions < 2.0. In fact, you are using PyTorch {torch.__version__}. "
+        f"You might want to consider upgrading."
+    )
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+        k = k.softmax(dim=-1)  
+        context = torch.einsum('bhdn,bhen->bhde', k, v)
+        out = torch.einsum('bhde,bhdn->bhen', context, q)
+        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+        return self.to_out(out)
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = rearrange(q, 'b c h w -> b (h w) c')
+        k = rearrange(k, 'b c h w -> b c (h w)')
+        w_ = torch.einsum('bij,bjk->bik', q, k)
+
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, 'b c h w -> b c (h w)')
+        w_ = rearrange(w_, 'b i j -> b j i')
+        h_ = torch.einsum('bij,bjk->bik', v, w_)
+        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        query_dim,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        # backend=None,
+        backend=SDPBackend.FLASH_ATTENTION, # FA implemented by torch.
+        **kwargs,
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head**-0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.backend = backend
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        h = self.heads
+
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            n_cp = x.shape[0] // n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+
+        ## old
+        """
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+        del q, k
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        sim = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim, v)
+        """
+        ## new
+        # with sdpa_kernel(**BACKEND_MAP[self.backend]):
+        with sdpa_kernel([self.backend]): # new signature
+            # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
+            out = F.scaled_dot_product_attention(
+                q, k, v, attn_mask=mask
+            )  # scale is dim_head ** -0.5 per default
+
+        del q, k, v
+        out = rearrange(out, "b h n d -> b n (h d)", h=h)
+
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+# class CrossAttention(nn.Module):
+#     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+#         super().__init__()
+#         inner_dim = dim_head * heads
+#         context_dim = default(context_dim, query_dim)
+
+#         self.scale = dim_head ** -0.5
+#         self.heads = heads
+
+#         self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+#         self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+#         self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+#         self.to_out = nn.Sequential(
+#             nn.Linear(inner_dim, query_dim),
+#             nn.Dropout(dropout)
+#         )
+
+#     def forward(self, x, context=None, mask=None):
+#         h = self.heads
+
+#         q = self.to_q(x)
+#         context = default(context, x)
+#         k = self.to_k(context)
+#         v = self.to_v(context)
+
+#         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+#         sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+#         if exists(mask):
+#             mask = rearrange(mask, 'b ... -> b (...)')
+#             max_neg_value = -torch.finfo(sim.dtype).max
+#             mask = repeat(mask, 'b j -> (b h) () j', h=h)
+#             sim.masked_fill_(~mask, max_neg_value)
+
+#         # attention, what we cannot get enough of
+#         attn = sim.softmax(dim=-1)
+
+#         out = einsum('b i j, b j d -> b i d', attn, v)
+#         out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+#         return self.to_out(out)
+
+
+# class BasicTransformerBlock(nn.Module):
+#     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
+#         super().__init__()
+#         self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
+#         self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+#         self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
+#                                     heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+#         self.norm1 = nn.LayerNorm(dim)
+#         self.norm2 = nn.LayerNorm(dim)
+#         self.norm3 = nn.LayerNorm(dim)
+#         self.checkpoint = checkpoint
+
+#     def forward(self, x, context=None):
+#         return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+#     def _forward(self, x, context=None):
+#         x = self.attn1(self.norm1(x)) + x
+#         x = self.attn2(self.norm2(x), context=context) + x
+#         x = self.ff(self.norm3(x)) + x
+#         return x
+
+
+try:
+    # from xformers.triton import FusedLayerNorm as LayerNorm
+    import xformers
+    import xformers.ops
+    XFORMERS_IS_AVAILBLE = True
+except:
+    XFORMERS_IS_AVAILBLE = False
+
+from typing import Optional, Any
+
+class MemoryEfficientCrossAttention(nn.Module):
+    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, enable_rmsnorm=False, qk_norm=False, no_flash_op=False, enable_rope=False, qk_norm_fullseq=False,):
+        super().__init__()
+        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+              f"{heads} heads.") 
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.enable_rope = enable_rope
+
+        # if enable_rmsnorm:
+        # self.q_rmsnorm = RMSNorm(query_dim, eps=1e-5)
+        # self.k_rmsnorm = RMSNorm(context_dim, eps=1e-5)
+
+        if qk_norm_fullseq: # as in lumina
+            self.q_norm = RMSNorm(inner_dim, elementwise_affine=True) if qk_norm else nn.Identity()
+            self.k_norm = RMSNorm(inner_dim, elementwise_affine=True) if qk_norm else nn.Identity()
+        else:
+            self.q_norm = RMSNorm(self.dim_head, elementwise_affine=True) if qk_norm else nn.Identity()
+            self.k_norm = RMSNorm(self.dim_head, elementwise_affine=True) if qk_norm else nn.Identity()
+
+        # if not qk_norm:
+        #     logpy.warn(
+        #         f"No QK Norm activated, wish you good luck..."
+        #     )
+
+        # self.enable_rmsnorm = enable_rmsnorm
+
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+        # self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        # self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+        if no_flash_op:
+            self.attention_op = MemoryEfficientAttentionCutlassOp # force flash attention
+        else:
+            self.attention_op: Optional[Any] = None # enable 
+
+    @staticmethod
+    def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+        """
+        Reshape frequency tensor for broadcasting it with another tensor.
+
+        This function reshapes the frequency tensor to have the same shape as
+        the target tensor 'x' for the purpose of broadcasting the frequency
+        tensor during element-wise operations.
+
+        Args:
+            freqs_cis (torch.Tensor): Frequency tensor to be reshaped.
+            x (torch.Tensor): Target tensor for broadcasting compatibility.
+
+        Returns:
+            torch.Tensor: Reshaped frequency tensor.
+
+        Raises:
+            AssertionError: If the frequency tensor doesn't match the expected
+                shape.
+            AssertionError: If the target tensor 'x' doesn't have the expected
+                number of dimensions.
+        """
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis.view(*shape)
+
+    @staticmethod
+    def apply_rotary_emb(
+        xq: torch.Tensor,
+        xk: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Apply rotary embeddings to input tensors using the given frequency
+        tensor.
+
+        This function applies rotary embeddings to the given query 'xq' and
+        key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
+        input tensors are reshaped as complex numbers, and the frequency tensor
+        is reshaped for broadcasting compatibility. The resulting tensors
+        contain rotary embeddings and are returned as real tensors.
+
+        Args:
+            xq (torch.Tensor): Query tensor to apply rotary embeddings.
+            xk (torch.Tensor): Key tensor to apply rotary embeddings.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
+                exponentials.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
+                and key tensor with rotary embeddings.
+        """
+        with torch.cuda.amp.autocast(enabled=False):
+            xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+            xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+            freqs_cis = MemoryEfficientCrossAttention.reshape_for_broadcast(freqs_cis, xq_)
+            xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+            xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+            return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+    def forward(self, x, context=None, freqs_cis=None, mask=None):
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+
+        v = self.to_v(context)
+
+        dtype = q.dtype
+
+        b, _, _ = q.shape
+        if self.enable_rope:
+            q, k = self.q_norm(q), self.k_norm(k) # for stable amp training
+
+            q, k, v = map(
+                lambda t: t.unsqueeze(3)
+                .reshape(b, t.shape[1], self.heads, self.dim_head)
+                .permute(0, 2, 1, 3)
+                # .reshape(b * self.heads, t.shape[1], self.dim_head)
+                .reshape(b, self.heads, t.shape[1], self.dim_head)
+                .contiguous(),
+                (q, k, v),
+            )
+
+            assert freqs_cis is not None
+            q, k = MemoryEfficientCrossAttention.apply_rotary_emb(q, k, freqs_cis=freqs_cis)
+            q, k = q.to(dtype), k.to(dtype)
+            pass
+
+        else:
+            q, k, v = map(
+                lambda t: t.unsqueeze(3)
+                .reshape(b, t.shape[1], self.heads, self.dim_head)
+                .permute(0, 2, 1, 3)
+                .reshape(b * self.heads, t.shape[1], self.dim_head)
+                .contiguous(),
+                (q, k, v),
+            )
+            q, k = self.q_norm(q), self.k_norm(k) # for stable amp training
+
+        # actually compute the attention, what we cannot get enough of
+        # out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+        # actually compute the attention, what we cannot get enough of
+        if version.parse(xformers.__version__) >= version.parse("0.0.21"):
+            # NOTE: workaround for
+            # https://github.com/facebookresearch/xformers/issues/845
+            max_bs = 32768
+            N = q.shape[0]
+            n_batches = math.ceil(N / max_bs)
+            out = list()
+            for i_batch in range(n_batches):
+                batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs)
+                out.append(
+                    xformers.ops.memory_efficient_attention(
+                        q[batch],
+                        k[batch],
+                        v[batch],
+                        attn_bias=None,
+                        # op=self.attention_op,
+                    )
+                )
+            out = torch.cat(out, 0)
+        else:
+            out = xformers.ops.memory_efficient_attention(
+                q, k, v, attn_bias=None, op=self.attention_op
+            )
+
+        # TODO: Use this directly in the attention operation, as a bias
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        return self.to_out(out)
+
+
+
+class JointMemoryEfficientCrossAttention(nn.Module):
+    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+        super().__init__()
+        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+              f"{heads} heads.")
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_qkv_t = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_qkv_i = nn.Linear(query_dim, inner_dim, bias=False)
+
+        # self.to_k = nn.Linear(context_dim*2, inner_dim, bias=False)
+        # self.to_v = nn.Linear(context_dim*2, inner_dim, bias=False)
+        # self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        # self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+        self.attention_op: Optional[Any] = None
+        # self.attention_op: Optional[Any] = MemoryEfficientAttentionFlashAttentionOp
+
+        # TODO, add later for stable AMP training.
+        # self.rms_norm_t_q = RMSNorm(args.dim, eps=args.norm_eps)
+        # self.rms_norm_t_k = RMSNorm(args.dim, eps=args.norm_eps)
+        # self.rms_norm_i_q = RMSNorm(args.dim, eps=args.norm_eps)
+        # self.rms_norm_i_k = RMSNorm(args.dim, eps=args.norm_eps)
+
+
+    def forward(self, x, context=None, mask=None):
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        # actually compute the attention, what we cannot get enough of
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention
+    }
+    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
+                 disable_self_attn=False):
+        super().__init__()
+        attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
+        assert attn_mode in self.ATTENTION_MODES
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
+                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        # return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+        return self._forward(x, context)
+
+    def _forward(self, x, context=None):
+        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
+        x = self.attn2(self.norm2(x), context=context) + x
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    """
+    def __init__(self, in_channels, n_heads, d_head,
+                 depth=1, dropout=0., context_dim=None):
+        super().__init__()
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+
+        self.proj_in = nn.Conv2d(in_channels,
+                                 inner_dim,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+
+        self.transformer_blocks = nn.ModuleList(
+            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
+                for d in range(depth)]
+        )
+
+        self.proj_out = zero_module(nn.Conv2d(inner_dim,
+                                              in_channels,
+                                              kernel_size=1,
+                                              stride=1,
+                                              padding=0))
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        x = self.proj_in(x)
+        x = rearrange(x, 'b c h w -> b (h w) c')
+        for block in self.transformer_blocks:
+            x = block(x, context=context)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        x = self.proj_out(x)
+        return x + x_in
+
+
+
+class BasicTransformerBlock3D(BasicTransformerBlock):
+
+    def forward(self, x, context=None, num_frames=1):
+        # return checkpoint(self._forward, (x, context, num_frames), self.parameters(), self.checkpoint)
+        return self._forward(x, context, num_frames) # , self.parameters(), self.checkpoint
+
+    def _forward(self, x, context=None, num_frames=1):
+        x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous()
+        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
+        x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous()
+        x = self.attn2(self.norm2(x), context=context) + x
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class SpatialTransformer3D(nn.Module):
+    ''' 3D self-attention ''' 
+    def __init__(self, in_channels, n_heads, d_head,
+                 depth=1, dropout=0., context_dim=None,
+                 disable_self_attn=False, use_linear=False,
+                 use_checkpoint=True):
+        super().__init__()
+        if exists(context_dim) and not isinstance(context_dim, list):
+            context_dim = [context_dim]
+        elif context_dim is None:
+            context_dim = [None] * depth
+
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+        if not use_linear:
+            self.proj_in = nn.Conv2d(in_channels,
+                                     inner_dim,
+                                     kernel_size=1,
+                                     stride=1,
+                                     padding=0)
+        else:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [BasicTransformerBlock3D(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
+                for d in range(depth)]
+        )
+        if not use_linear:
+            self.proj_out = zero_module(nn.Conv2d(inner_dim,
+                                                  in_channels,
+                                                  kernel_size=1,
+                                                  stride=1,
+                                                  padding=0))
+        else:
+            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None, num_frames=1):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context]
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            x = block(x, context=context[i], num_frames=num_frames)
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        return x + x_in
\ No newline at end of file
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diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbeb067b7a2f174edafea1b64fbf4e1e100ce002
--- /dev/null
+++ b/ldm/modules/diffusionmodules/model.py
@@ -0,0 +1,1016 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+from typing import Optional, Any
+
+# from ldm.modules.attention import MemoryEfficientCrossAttention
+# from .modules.attention import MemoryEfficientCrossAttention
+from ldm.modules.attention import SpatialTransformer3D
+from pdb import set_trace as st
+from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
+from ldm.modules.attention import MemoryEfficientCrossAttention
+
+from nsr.volumetric_rendering.ray_sampler import RaySampler
+import kornia
+import point_cloud_utils as pcu
+
+try:
+    import xformers
+    import xformers.ops
+    XFORMERS_IS_AVAILBLE = True
+except:
+    XFORMERS_IS_AVAILBLE = False
+    print("No module 'xformers'. Proceeding without it.")
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models:
+    From Fairseq.
+    Build sinusoidal embeddings.
+    This matches the implementation in tensor2tensor, but differs slightly
+    from the description in Section 3.5 of "Attention Is All You Need".
+    """
+    assert len(timesteps.shape) == 1
+
+    half_dim = embedding_dim // 2
+    emb = math.log(10000) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+    emb = emb.to(device=timesteps.device)
+    emb = timesteps.float()[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0,1,0,0))
+    return emb
+
+
+def nonlinearity(x):
+    # swish
+    return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    # turn off amp autocast here
+    def forward(self, x):
+
+        # with torch.cuda.amp.autocast(enabled=False, dtype=torch.bfloat16, cache_enabled=True): # only handles the execusion, not data typeS
+        with torch.autocast(enabled=False, device_type='cuda'): # only handles the execusion, not data typeS
+            x = torch.nn.functional.interpolate(x.float(), scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=3,
+                                        stride=2,
+                                        padding=0)
+
+    def forward(self, x):
+        if self.with_conv:
+            pad = (0,1,0,1)
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+                 dropout, temb_channels=512):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels)
+        self.conv1 = torch.nn.Conv2d(in_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels,
+                                             out_channels)
+        self.norm2 = Normalize(out_channels)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(out_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv2d(in_channels,
+                                                     out_channels,
+                                                     kernel_size=3,
+                                                     stride=1,
+                                                     padding=1)
+            else:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels,
+                                                    out_channels,
+                                                    kernel_size=1,
+                                                    stride=1,
+                                                    padding=0)
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x+h
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = q.reshape(b,c,h*w)
+        q = q.permute(0,2,1)   # b,hw,c
+        k = k.reshape(b,c,h*w) # b,c,hw
+        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b,c,h*w)
+        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b,c,h,w)
+
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+class MemoryEfficientAttnBlock(nn.Module):
+    """
+        Uses xformers efficient implementation,
+        see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+        Note: this is a single-head self-attention operation
+    """
+    #
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+        self.attention_op: Optional[Any] = None
+        # self.attention_op: Optional[Any] = MemoryEfficientAttentionFlashAttentionOp
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        B, C, H, W = q.shape
+        q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
+
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(B, t.shape[1], 1, C)
+            .permute(0, 2, 1, 3)
+            .reshape(B * 1, t.shape[1], C)
+            .contiguous(),
+            (q, k, v),
+        )
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+        out = (
+            out.unsqueeze(0)
+            .reshape(B, 1, out.shape[1], C)
+            .permute(0, 2, 1, 3)
+            .reshape(B, out.shape[1], C)
+        )
+        out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
+        out = self.proj_out(out)
+        return x+out
+
+
+# class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
+#     def forward(self, x, context=None, mask=None):
+#         b, c, h, w = x.shape
+#         x = rearrange(x, 'b c h w -> b (h w) c')
+#         out = super().forward(x, context=context, mask=mask)
+#         out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
+#         return x + out
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+    assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none", "mv-vanilla"], f'attn_type {attn_type} unknown'
+    if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
+        attn_type = "vanilla-xformers"
+    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    if attn_type == "vanilla":
+        assert attn_kwargs is None
+        return AttnBlock(in_channels)
+    elif attn_type == "mv-vanilla":
+        assert attn_kwargs is not None
+        return SpatialTransformer3D(in_channels, **attn_kwargs) # TODO
+    elif attn_type == "vanilla-xformers":
+        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+        return MemoryEfficientAttnBlock(in_channels)
+    elif type == "memory-efficient-cross-attn":
+        attn_kwargs["query_dim"] = in_channels
+        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
+    elif attn_type == "none":
+        return nn.Identity(in_channels)
+    else:
+        raise NotImplementedError()
+
+
+class Model(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla", attn_kwargs={}):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = self.ch*4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList([
+                torch.nn.Linear(self.ch,
+                                self.temb_ch),
+                torch.nn.Linear(self.temb_ch,
+                                self.temb_ch),
+            ])
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, attn_kwargs=attn_kwargs))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, attn_kwargs=attn_kwargs)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            skip_in = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch*in_ch_mult[i_level]
+                block.append(ResnetBlock(in_channels=block_in+skip_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, attn_kwargs=attn_kwargs))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x, t=None, context=None):
+        #assert x.shape[2] == x.shape[3] == self.resolution
+        if context is not None:
+            # assume aligned context, cat along channel axis
+            x = torch.cat((x, context), dim=1)
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+    def get_last_layer(self):
+        return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, 
+                 z_channels, double_z=True, 
+                 use_linear_attn=False, attn_type="vanilla",
+                 attn_kwargs={},
+                 add_fusion_layer=False,
+                 **ignore_kwargs):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.z_channels = z_channels
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, attn_kwargs=attn_kwargs))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, attn_kwargs=attn_kwargs)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        2*z_channels if double_z else z_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+        # TODO: use attention-based? Later.
+        if add_fusion_layer: # fusion 4 frames
+            self.fusion_layer = torch.nn.Conv2d(2*z_channels*4 if double_z else z_channels*4,
+                                            2*z_channels if double_z else z_channels,
+                                            kernel_size=3,
+                                            stride=1,
+                                            padding=1)
+
+    def forward(self, x, **kwargs):
+        # timestep embedding
+        temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h, **kwargs)
+        h = self.mid.block_2(h, temb)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+class MVEncoder(Encoder):
+    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="mv-vanilla", **ignore_kwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=in_channels, resolution=resolution, z_channels=z_channels, double_z=double_z, use_linear_attn=use_linear_attn, attn_type=attn_type,
+            add_fusion_layer=True,
+         **ignore_kwargs)
+        self.num_frames = 4
+   
+ 
+    def forward(self, x):
+        h = super().forward(x, num_frames=self.num_frames)
+        # multi-view aggregation, as in pixel-nerf
+        h = h.chunk(x.shape[0] // self.num_frames) # features from the same single instance aggregated here
+        # h = [feat.max(keepdim=True, dim=0)[0] for feat in h] # max pooling
+        h = [self.fusion_layer(torch.cat(feat.chunk(feat.shape[0]), dim=1)) for feat in h] # conv pooling
+        return torch.cat(h, dim=0)
+        # return torch.cat(h, dim=0).to(torch.float32)
+
+
+class MVEncoderGS(Encoder):
+    # support pixle-aligned rendering
+    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="mv-vanilla", **ignore_kwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=in_channels, resolution=resolution, z_channels=z_channels, double_z=double_z, use_linear_attn=use_linear_attn, attn_type=attn_type,
+            add_fusion_layer=False,
+         **ignore_kwargs)
+        self.num_frames = 4
+   
+ 
+    def forward(self, x):
+        h = super().forward(x, num_frames=self.num_frames)
+
+        # multi-view aggregation, as in pixel-nerf
+        h = h.chunk(x.shape[0] // self.num_frames) # features from the same single instance aggregated here
+        # st()
+
+        # concat
+        # torch.cat(latent, 1)
+        h = [rearrange(latent, 'B C H W -> 1 (B C) H W') for latent in h] # basically concat
+        h = torch.cat(h, dim=0)
+
+        return h # B 16 H W when V=4, z_channels=2
+
+class MVEncoderGSDynamicInp(Encoder):
+    # support dynamic length input, e.g., up to 40 views during training/inference.
+    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="mv-vanilla", num_frames, **ignore_kwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=in_channels, resolution=resolution, z_channels=z_channels, double_z=double_z, use_linear_attn=use_linear_attn, attn_type=attn_type,
+            add_fusion_layer=False,
+            **ignore_kwargs)
+        self.num_frames = num_frames
+ 
+    def forward(self, x, num_frames=None):
+        h = super().forward(x, num_frames=self.num_frames)
+        # multi-view aggregation, as in pixel-nerf
+
+        if num_frames is None:
+            num_frames = self.num_frames # 4 for now, test later.        
+        
+        assert num_frames > 4
+
+        h = h.chunk(x.shape[0] // num_frames) # features from the same single instance aggregated here
+        h = [feat.mean(keepdim=True, dim=0) for feat in h] # average pooling, 1 C H W for each h
+
+        return torch.cat(h, dim=0)
+
+# unproject VAE latent here, since SD-VAE latent 0almost pixel aligned.
+class MVEncoderUnprojRGB(Encoder):
+    # support dynamic length input, e.g., up to 40 views during training/inference.
+    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="mv-vanilla", num_frames, latent_num=768*3, **ignore_kwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=in_channels, resolution=resolution, z_channels=z_channels, double_z=double_z, use_linear_attn=use_linear_attn, attn_type=attn_type,
+            add_fusion_layer=False,
+            **ignore_kwargs)
+        self.num_frames = num_frames
+        # self.ray_sampler = RaySampler()
+        self.mean_filter = lambda x: kornia.filters.box_blur(x, (8,8)) # f=8 
+        self.conv_out = nn.Identity()
+        self.latent_num = latent_num # 768 * 3 by default
+    
+    
+    def forward(self, x, c, depth, num_frames=None):
+        if num_frames is None:
+            num_frames = self.num_frames
+        assert num_frames >=6
+
+        h = super().forward(x, num_frames=self.num_frames) # ! support data augmentation, different FPS different latent corresponding to the same instance?
+
+        # 1. unproj tokens
+
+        # query = 
+
+
+        # 2. fps sampling, 768*3 latents here from 32x32x9 overall tokens & record the xyz.
+
+        _, fps_idx = pytorch3d.ops.sample_farthest_points(
+            gt_pos.unsqueeze(0), K=self.latent_num)
+
+        # 2.5 Cross attend.
+        # 3. add vit TX (5 layers, concat xyz-PE)
+        # 4. tokens apply VAE (separate? check later.)
+        
+        st()
+
+
+
+class MVEncoderGSDynamicInp_CA(Encoder):
+    # support dynamic length input, e.g., up to 40 views during training/inference.
+    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="mv-vanilla", num_frames, **ignore_kwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=in_channels, resolution=resolution, z_channels=z_channels, double_z=double_z, use_linear_attn=use_linear_attn, attn_type=attn_type,
+            add_fusion_layer=False,
+            **ignore_kwargs)
+        self.num_frames = num_frames
+        query_dim = z_channels*(1+double_z)
+
+        self.readout_ca = MemoryEfficientCrossAttention(
+            query_dim,
+            2*z_channels if double_z else z_channels,
+        )
+        self.latent_embedding = nn.Parameter(
+            torch.randn(1, 32 * 32 * 3, query_dim))
+ 
+    def forward(self, x, num_frames=None):
+        x = super().forward(x, num_frames=self.num_frames)
+        # multi-view aggregation, as in pixel-nerf
+
+        if num_frames is None:
+            num_frames = self.num_frames # 4 for now, test later.        
+        
+        x = rearrange(x, '(B V) C H W -> B (V H W) C', V=self.num_frames) # for cross-attention
+        x = self.readout_ca(self.latent_embedding.repeat(x.shape[0], 1, 1), x)
+
+        x = rearrange(x, 'B (N H W) C -> B C (N H) W', H=32, W=32, N=3)
+
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+                 attn_type="vanilla-xformers", **ignorekwargs):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,)+tuple(ch_mult)
+        block_in = ch*ch_mult[self.num_resolutions-1]
+        curr_res = resolution // 2**(self.num_resolutions-1)
+        self.z_shape = (1,z_channels,curr_res,curr_res)
+        print("Working with z of shape {} = {} dimensions.".format(
+            self.z_shape, np.prod(self.z_shape)))
+
+        # z to block_in
+        self.conv_in = torch.nn.Conv2d(z_channels,
+                                       block_in,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, z):
+        #assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
+        # timestep embedding
+        temb = None
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](h, temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        if self.tanh_out:
+            h = torch.tanh(h)
+        return h
+
+
+class SimpleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, *args, **kwargs):
+        super().__init__()
+        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+                                     ResnetBlock(in_channels=in_channels,
+                                                 out_channels=2 * in_channels,
+                                                 temb_channels=0, dropout=0.0),
+                                     ResnetBlock(in_channels=2 * in_channels,
+                                                out_channels=4 * in_channels,
+                                                temb_channels=0, dropout=0.0),
+                                     ResnetBlock(in_channels=4 * in_channels,
+                                                out_channels=2 * in_channels,
+                                                temb_channels=0, dropout=0.0),
+                                     nn.Conv2d(2*in_channels, in_channels, 1),
+                                     Upsample(in_channels, with_conv=True)])
+        # end
+        self.norm_out = Normalize(in_channels)
+        self.conv_out = torch.nn.Conv2d(in_channels,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        for i, layer in enumerate(self.model):
+            if i in [1,2,3]:
+                x = layer(x, None)
+            else:
+                x = layer(x)
+
+        h = self.norm_out(x)
+        h = nonlinearity(h)
+        x = self.conv_out(h)
+        return x
+
+
+class UpsampleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+                 ch_mult=(2,2), dropout=0.0):
+        super().__init__()
+        # upsampling
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        block_in = in_channels
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.res_blocks = nn.ModuleList()
+        self.upsample_blocks = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            res_block = []
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                res_block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+            self.res_blocks.append(nn.ModuleList(res_block))
+            if i_level != self.num_resolutions - 1:
+                self.upsample_blocks.append(Upsample(block_in, True))
+                curr_res = curr_res * 2
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        # upsampling
+        h = x
+        for k, i_level in enumerate(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.res_blocks[i_level][i_block](h, None)
+            if i_level != self.num_resolutions - 1:
+                h = self.upsample_blocks[k](h)
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class LatentRescaler(nn.Module):
+    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+        super().__init__()
+        # residual block, interpolate, residual block
+        self.factor = factor
+        self.conv_in = nn.Conv2d(in_channels,
+                                 mid_channels,
+                                 kernel_size=3,
+                                 stride=1,
+                                 padding=1)
+        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+                                                     out_channels=mid_channels,
+                                                     temb_channels=0,
+                                                     dropout=0.0) for _ in range(depth)])
+        self.attn = AttnBlock(mid_channels)
+        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+                                                     out_channels=mid_channels,
+                                                     temb_channels=0,
+                                                     dropout=0.0) for _ in range(depth)])
+
+        self.conv_out = nn.Conv2d(mid_channels,
+                                  out_channels,
+                                  kernel_size=1,
+                                  )
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        for block in self.res_block1:
+            x = block(x, None)
+        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+        x = self.attn(x)
+        for block in self.res_block2:
+            x = block(x, None)
+        x = self.conv_out(x)
+        return x
+
+
+class MergedRescaleEncoder(nn.Module):
+    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
+                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+        super().__init__()
+        intermediate_chn = ch * ch_mult[-1]
+        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
+                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+                               out_ch=None)
+        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.rescaler(x)
+        return x
+
+
+class MergedRescaleDecoder(nn.Module):
+    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+        super().__init__()
+        tmp_chn = z_channels*ch_mult[-1]
+        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+                               ch_mult=ch_mult, resolution=resolution, ch=ch)
+        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+                                       out_channels=tmp_chn, depth=rescale_module_depth)
+
+    def forward(self, x):
+        x = self.rescaler(x)
+        x = self.decoder(x)
+        return x
+
+
+class Upsampler(nn.Module):
+    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+        super().__init__()
+        assert out_size >= in_size
+        num_blocks = int(np.log2(out_size//in_size))+1
+        factor_up = 1.+ (out_size % in_size)
+        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+                                       out_channels=in_channels)
+        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+                               attn_resolutions=[], in_channels=None, ch=in_channels,
+                               ch_mult=[ch_mult for _ in range(num_blocks)])
+
+    def forward(self, x):
+        x = self.rescaler(x)
+        x = self.decoder(x)
+        return x
+
+
+class Resize(nn.Module):
+    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+        super().__init__()
+        self.with_conv = learned
+        self.mode = mode
+        if self.with_conv:
+            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+            raise NotImplementedError()
+            assert in_channels is not None
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=4,
+                                        stride=2,
+                                        padding=1)
+
+    def forward(self, x, scale_factor=1.0):
+        if scale_factor==1.0:
+            return x
+        else:
+            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+        return x
+
+
+# ! lgm unet
\ No newline at end of file
diff --git a/ldm/modules/diffusionmodules/mv_unet.py b/ldm/modules/diffusionmodules/mv_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..309f370995d782362656dccb5645068f365b9965
--- /dev/null
+++ b/ldm/modules/diffusionmodules/mv_unet.py
@@ -0,0 +1,456 @@
+from numpy import sqrt
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import numpy as np
+from typing import Tuple, Literal
+from functools import partial
+
+from pdb import set_trace as st
+
+# from core.attention import MemEffAttention
+from vit.vision_transformer import MemEffAttention
+
+
+class MVAttention(nn.Module):
+
+    def __init__(
+            self,
+            dim: int,
+            num_heads: int = 8,
+            qkv_bias: bool = False,
+            proj_bias: bool = True,
+            attn_drop: float = 0.0,
+            proj_drop: float = 0.0,
+            groups: int = 32,
+            eps: float = 1e-5,
+            residual: bool = True,
+            skip_scale: float = 1,
+            num_frames: int = 4,  # WARN: hardcoded!
+    ):
+        super().__init__()
+
+        self.residual = residual
+        self.skip_scale = skip_scale
+        self.num_frames = num_frames
+
+        self.norm = nn.GroupNorm(num_groups=groups,
+                                 num_channels=dim,
+                                 eps=eps,
+                                 affine=True)
+        self.attn = MemEffAttention(dim, num_heads, qkv_bias, proj_bias,
+                                    attn_drop, proj_drop)
+
+    def forward(self, x):
+        # x: [B*V, C, H, W]
+        BV, C, H, W = x.shape
+        B = BV // self.num_frames  # assert BV % self.num_frames == 0
+
+        res = x
+        x = self.norm(x)
+
+        x = x.reshape(B, self.num_frames, C, H,
+                      W).permute(0, 1, 3, 4, 2).reshape(B, -1, C)
+        x = self.attn(x)
+        x = x.reshape(B, self.num_frames, H, W,
+                      C).permute(0, 1, 4, 2, 3).reshape(BV, C, H, W)
+
+        if self.residual:
+            x = (x + res) * self.skip_scale
+        return x
+
+
+class ResnetBlock(nn.Module):
+
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            resample: Literal['default', 'up', 'down'] = 'default',
+            groups: int = 32,
+            eps: float = 1e-5,
+            skip_scale: float = 1,  # multiplied to output
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.skip_scale = skip_scale
+
+        self.norm1 = nn.GroupNorm(num_groups=groups,
+                                  num_channels=in_channels,
+                                  eps=eps,
+                                  affine=True)
+        self.conv1 = nn.Conv2d(in_channels,
+                               out_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1)
+
+        self.norm2 = nn.GroupNorm(num_groups=groups,
+                                  num_channels=out_channels,
+                                  eps=eps,
+                                  affine=True)
+        self.conv2 = nn.Conv2d(out_channels,
+                               out_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1)
+
+        self.act = F.silu
+
+        self.resample = None
+        if resample == 'up':
+            self.resample = partial(F.interpolate,
+                                    scale_factor=2.0,
+                                    mode="nearest")
+        elif resample == 'down':
+            self.resample = nn.AvgPool2d(kernel_size=2, stride=2)
+
+        self.shortcut = nn.Identity()
+        if self.in_channels != self.out_channels:
+            self.shortcut = nn.Conv2d(in_channels,
+                                      out_channels,
+                                      kernel_size=1,
+                                      bias=True)
+
+    def forward(self, x):
+        res = x
+
+        x = self.norm1(x)
+        x = self.act(x)
+
+        if self.resample:
+            res = self.resample(res)
+            x = self.resample(x)
+
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.act(x)
+        x = self.conv2(x)
+
+        x = (x + self.shortcut(res)) * self.skip_scale
+
+        return x
+
+
+class DownBlock(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        downsample: bool = True,
+        attention: bool = True,
+        attention_heads: int = 16,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        nets = []
+        attns = []
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            nets.append(
+                ResnetBlock(in_channels, out_channels, skip_scale=skip_scale))
+            if attention:
+                attns.append(
+                    MVAttention(out_channels,
+                                attention_heads,
+                                skip_scale=skip_scale))
+            else:
+                attns.append(None)
+        self.nets = nn.ModuleList(nets)
+        self.attns = nn.ModuleList(attns)
+
+        self.downsample = None
+        if downsample:
+            self.downsample = nn.Conv2d(out_channels,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=2,
+                                        padding=1)
+
+    def forward(self, x):
+        xs = []
+
+        for attn, net in zip(self.attns, self.nets):
+            x = net(x)
+            if attn:
+                x = attn(x)
+            xs.append(x)
+
+        if self.downsample:
+            x = self.downsample(x)
+            xs.append(x)
+
+        return x, xs
+
+
+class MidBlock(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        num_layers: int = 1,
+        attention: bool = True,
+        attention_heads: int = 16,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        nets = []
+        attns = []
+        # first layer
+        nets.append(
+            ResnetBlock(in_channels, in_channels, skip_scale=skip_scale))
+        # more layers
+        for i in range(num_layers):
+            nets.append(
+                ResnetBlock(in_channels, in_channels, skip_scale=skip_scale))
+            if attention:
+                attns.append(
+                    MVAttention(in_channels,
+                                attention_heads,
+                                skip_scale=skip_scale))
+            else:
+                attns.append(None)
+        self.nets = nn.ModuleList(nets)
+        self.attns = nn.ModuleList(attns)
+
+    def forward(self, x):
+        x = self.nets[0](x)
+        for attn, net in zip(self.attns, self.nets[1:]):
+            if attn:
+                x = attn(x)
+            x = net(x)
+        return x
+
+
+class UpBlock(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        prev_out_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        upsample: bool = True,
+        attention: bool = True,
+        attention_heads: int = 16,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        nets = []
+        attns = []
+        for i in range(num_layers):
+            cin = in_channels if i == 0 else out_channels
+            cskip = prev_out_channels if (i == num_layers -
+                                          1) else out_channels
+
+            nets.append(
+                ResnetBlock(cin + cskip, out_channels, skip_scale=skip_scale))
+            if attention:
+                attns.append(
+                    MVAttention(out_channels,
+                                attention_heads,
+                                skip_scale=skip_scale))
+            else:
+                attns.append(None)
+        self.nets = nn.ModuleList(nets)
+        self.attns = nn.ModuleList(attns)
+
+        self.upsample = None
+        if upsample:
+            self.upsample = nn.Conv2d(out_channels,
+                                      out_channels,
+                                      kernel_size=3,
+                                      stride=1,
+                                      padding=1)
+
+    def forward(self, x, xs):
+
+        for attn, net in zip(self.attns, self.nets):
+            res_x = xs[-1]
+            xs = xs[:-1]
+            x = torch.cat([x, res_x], dim=1)
+            x = net(x)
+            if attn:
+                x = attn(x)
+
+        if self.upsample:
+            x = F.interpolate(x, scale_factor=2.0, mode='nearest')
+            x = self.upsample(x)
+
+        return x
+
+
+# it could be asymmetric!
+class MVUNet(nn.Module):
+
+    def __init__(
+            self,
+            in_channels: int = 3,
+            out_channels: int = 3,
+            down_channels: Tuple[int, ...] = (64, 128, 256, 512, 1024),
+            down_attention: Tuple[bool,
+                                  ...] = (False, False, False, True, True),
+            mid_attention: bool = True,
+            up_channels: Tuple[int, ...] = (1024, 512, 256),
+            up_attention: Tuple[bool, ...] = (True, True, False),
+            layers_per_block: int = 2,
+            skip_scale: float = np.sqrt(0.5),
+    ):
+        super().__init__()
+
+        # first
+        self.conv_in = nn.Conv2d(in_channels,
+                                 down_channels[0],
+                                 kernel_size=3,
+                                 stride=1,
+                                 padding=1)
+
+        # down
+        down_blocks = []
+        cout = down_channels[0]
+        for i in range(len(down_channels)):
+            cin = cout
+            cout = down_channels[i]
+
+            down_blocks.append(
+                DownBlock(
+                    cin,
+                    cout,
+                    num_layers=layers_per_block,
+                    downsample=(i
+                                != len(down_channels) - 1),  # not final layer
+                    attention=down_attention[i],
+                    skip_scale=skip_scale,
+                ))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+        # mid
+        self.mid_block = MidBlock(down_channels[-1],
+                                  attention=mid_attention,
+                                  skip_scale=skip_scale)
+
+        # up
+        up_blocks = []
+        cout = up_channels[0]
+        for i in range(len(up_channels)):
+            cin = cout
+            cout = up_channels[i]
+            cskip = down_channels[max(-2 - i,
+                                      -len(down_channels))]  # for assymetric
+
+            up_blocks.append(
+                UpBlock(
+                    cin,
+                    cskip,
+                    cout,
+                    num_layers=layers_per_block + 1,  # one more layer for up
+                    upsample=(i != len(up_channels) - 1),  # not final layer
+                    attention=up_attention[i],
+                    skip_scale=skip_scale,
+                ))
+        self.up_blocks = nn.ModuleList(up_blocks)
+
+        # last
+        self.norm_out = nn.GroupNorm(num_channels=up_channels[-1],
+                                     num_groups=32,
+                                     eps=1e-5)
+        self.conv_out = nn.Conv2d(up_channels[-1],
+                                  out_channels,
+                                  kernel_size=3,
+                                  stride=1,
+                                  padding=1)
+
+    def forward(self, x):
+        # x: [B, Cin, H, W]
+
+        # first
+        x = self.conv_in(x)
+
+        # down
+        xss = [x]
+        for block in self.down_blocks:
+            x, xs = block(x)
+            xss.extend(xs)
+
+        # mid
+        x = self.mid_block(x)  # 32 (B V) 1024 16 16
+
+        # up
+        for block in self.up_blocks:
+            xs = xss[-len(block.nets):]
+            xss = xss[:-len(block.nets)]
+            x = block(x, xs)
+
+        # last
+        x = self.norm_out(x)
+        x = F.silu(x)
+        x = self.conv_out(x)  # [B, Cout, H', W']
+
+        return x
+
+
+class LGM_MVEncoder(MVUNet):
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        down_channels: Tuple[int] = (64, 128, 256, 512, 1024),
+        down_attention: Tuple[bool] = (False, False, False, True, True),
+        mid_attention: bool = True,
+        up_channels: Tuple[int] = (1024, 512, 256),
+        up_attention: Tuple[bool] = (True, True, False),
+        layers_per_block: int = 2,
+        skip_scale: float = np.sqrt(0.5),
+        z_channels=4,
+        double_z=True,
+        add_fusion_layer=True,
+    ):
+        super().__init__(in_channels, out_channels, down_channels,
+                         down_attention, mid_attention, up_channels,
+                         up_attention, layers_per_block, skip_scale)
+        del self.up_blocks
+
+        self.conv_out = torch.nn.Conv2d(up_channels[0],
+                                        2 *
+                                        z_channels if double_z else z_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+        if add_fusion_layer:  # fusion 4 frames
+            self.fusion_layer = torch.nn.Conv2d(
+                2 * z_channels * 4 if double_z else z_channels * 4,
+                2 * z_channels if double_z else z_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1)
+
+        self.num_frames = 4 # !hard coded
+    
+    def forward(self, x):
+        # first
+        x = self.conv_in(x)
+
+        # down
+        xss = [x]
+        for block in self.down_blocks:
+            x, xs = block(x)
+            xss.extend(xs)
+
+        # mid
+        x = self.mid_block(x)  # 32 (B V) 1024 16 16
+
+        # multi-view aggregation, as in pixel-nerf
+        x = x.chunk(x.shape[0] // self.num_frames) # features from the same single instance aggregated here
+        # h = [feat.max(keepdim=True, dim=0)[0] for feat in h] # max pooling
+        x = [self.fusion_layer(torch.cat(feat.chunk(feat.shape[0]), dim=1)) for feat in x] # conv pooling
+        st()
+        return torch.cat(x, dim=0)
\ No newline at end of file
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab9b7d0bdb1841de703e0fe037b9d4a4ff987594
--- /dev/null
+++ b/ldm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,1245 @@
+from abc import abstractmethod
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ldm.modules.diffusionmodules.util import (
+    checkpoint,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    zero_module,
+    normalization,
+    timestep_embedding,
+)
+from ..attention import SpatialTransformer, SpatialTransformer3D, exists
+from ldm.util import exists
+
+from pdb import set_trace as st
+
+
+# dummy replace
+def convert_module_to_f16(x):
+    pass
+
+def convert_module_to_f32(x):
+    pass
+
+
+## go
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, context=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, SpatialTransformer):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+class TransposedUpsample(nn.Module):
+    'Learned 2x upsampling without padding'
+    def __init__(self, channels, out_channels=None, ks=5):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+
+        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+    def forward(self,x):
+        return self.up(x)
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, (x, emb), self.parameters(), self.use_checkpoint
+        )
+
+
+    def _forward(self, x, emb):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+        #return pt_checkpoint(self._forward, x)  # pytorch
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        use_spatial_transformer=False,    # custom transformer support
+        transformer_depth=1,              # custom transformer support
+        context_dim=None,                 # custom transformer support
+        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+        disable_self_attentions=None,
+        num_attention_blocks=None,
+        disable_middle_self_attn=False,
+        use_linear_in_transformer=False,
+    ):
+        super().__init__()
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                  f"attention will still not be set.")
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+            elif self.num_classes == "continuous":
+                print("setting up linear c_adm embedding layer")
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            else:
+                raise ValueError()
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+                            use_checkpoint=use_checkpoint
+                        ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(self.num_res_blocks[level] + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads_upsample,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                if level and i == self.num_res_blocks[level]:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+        if self.predict_codebook_ids:
+            self.id_predictor = nn.Sequential(
+            normalization(ch),
+            conv_nd(dims, model_channels, n_embed, 1),
+            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.num_classes is not None:
+            assert y.shape[0] == x.shape[0]
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+        h = h.type(x.dtype)
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            return self.out(h)
+
+
+
+class MultiViewUNetModel(nn.Module):
+    """
+    The full multi-view UNet model with attention, timestep embedding and camera embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    :param camera_dim: dimensionality of camera input.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        use_bf16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        use_spatial_transformer=False,    # custom transformer support
+        transformer_depth=1,              # custom transformer support
+        context_dim=None,                 # custom transformer support
+        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+        disable_self_attentions=None,
+        num_attention_blocks=None,
+        disable_middle_self_attn=False,
+        use_linear_in_transformer=False,
+        adm_in_channels=None,
+        camera_dim=None,
+    ):
+        super().__init__()
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                  f"attention will still not be set.")
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.dtype = th.bfloat16 if use_bf16 else self.dtype
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if camera_dim is not None:
+            time_embed_dim = model_channels * 4
+            self.camera_embed = nn.Sequential(
+                linear(camera_dim, time_embed_dim),
+                nn.SiLU(),
+                linear(time_embed_dim, time_embed_dim),
+            )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+            elif self.num_classes == "continuous":
+                print("setting up linear c_adm embedding layer")
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            elif self.num_classes == "sequential":
+                assert adm_in_channels is not None
+                self.label_emb = nn.Sequential(
+                    nn.Sequential(
+                        linear(adm_in_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    )
+                )
+            else:
+                raise ValueError()
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer3D(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer3D(  # always uses a self-attn
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+                            use_checkpoint=use_checkpoint
+                        ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(self.num_res_blocks[level] + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads_upsample,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer3D(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                if level and i == self.num_res_blocks[level]:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+        if self.predict_codebook_ids:
+            self.id_predictor = nn.Sequential(
+            normalization(ch),
+            conv_nd(dims, model_channels, n_embed, 1),
+            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps=None, context=None, y=None, camera=None, num_frames=1, **kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views).
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :param num_frames: a integer indicating number of frames for tensor reshaping.
+        :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views).
+        """
+        assert x.shape[0] % num_frames == 0, "[UNet] input batch size must be dividable by num_frames!"
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.num_classes is not None:
+            assert y.shape[0] == x.shape[0]
+            emb = emb + self.label_emb(y)
+
+        # Add camera embeddings
+        if camera is not None:
+            assert camera.shape[0] == emb.shape[0]
+            emb = emb + self.camera_embed(camera)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, context, num_frames=num_frames)
+            hs.append(h)
+        h = self.middle_block(h, emb, context, num_frames=num_frames)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context, num_frames=num_frames)
+        h = h.type(x.dtype)
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            return self.out(h)
+
+class MultiViewUNetModel_Encoder(MultiViewUNetModel):
+    def __init__(self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, use_bf16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, transformer_depth=1, context_dim=None, n_embed=None, legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, camera_dim=None):
+        super().__init__(image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout, channel_mult, conv_resample, dims, num_classes, use_checkpoint, use_fp16, use_bf16, num_heads, num_head_channels, num_heads_upsample, use_scale_shift_norm, resblock_updown, use_new_attention_order, use_spatial_transformer, transformer_depth, context_dim, n_embed, legacy, disable_self_attentions, num_attention_blocks, disable_middle_self_attn, use_linear_in_transformer, adm_in_channels, camera_dim)
+        del self.time_embed, self.camera_embed
+        del self.output_blocks
+    
+    def forward(self, x, timesteps=None, context=None, y=None, camera=None, num_frames=1, **kwargs):
+        # return super().forward(x, timesteps, context, y, camera, num_frames, **kwargs)
+
+        """
+        Apply the model to an input batch.
+        :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views).
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :param num_frames: a integer indicating number of frames for tensor reshaping.
+        :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views).
+        """
+        assert x.shape[0] % num_frames == 0, "[UNet] input batch size must be dividable by num_frames!"
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        # t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        # emb = self.time_embed(t_emb)
+
+        # if self.num_classes is not None:
+        #     assert y.shape[0] == x.shape[0]
+        #     emb = emb + self.label_emb(y)
+
+        # Add camera embeddings
+        # if camera is not None:
+        #     assert camera.shape[0] == emb.shape[0]
+        emb = self.camera_embed(camera) # ! TODO?
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, context, num_frames=num_frames)
+            hs.append(h)
+        h = self.middle_block(h, emb, context, num_frames=num_frames)
+        # TODO, add a concat -> pooling to fuse multiple frames; change it to 3D-aware tokens 
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context, num_frames=num_frames)
+        h = h.type(x.dtype)
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            return self.out(h)
\ No newline at end of file
diff --git a/ldm/modules/diffusionmodules/upscaling.py b/ldm/modules/diffusionmodules/upscaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..03816662098ce1ffac79bd939b892e867ab91988
--- /dev/null
+++ b/ldm/modules/diffusionmodules/upscaling.py
@@ -0,0 +1,81 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
+from ldm.util import default
+
+
+class AbstractLowScaleModel(nn.Module):
+    # for concatenating a downsampled image to the latent representation
+    def __init__(self, noise_schedule_config=None):
+        super(AbstractLowScaleModel, self).__init__()
+        if noise_schedule_config is not None:
+            self.register_schedule(**noise_schedule_config)
+
+    def register_schedule(self, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                   cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def forward(self, x):
+        return x, None
+
+    def decode(self, x):
+        return x
+
+
+class SimpleImageConcat(AbstractLowScaleModel):
+    # no noise level conditioning
+    def __init__(self):
+        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
+        self.max_noise_level = 0
+
+    def forward(self, x):
+        # fix to constant noise level
+        return x, torch.zeros(x.shape[0], device=x.device).long()
+
+
+class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
+    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
+        super().__init__(noise_schedule_config=noise_schedule_config)
+        self.max_noise_level = max_noise_level
+
+    def forward(self, x, noise_level=None):
+        if noise_level is None:
+            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+        else:
+            assert isinstance(noise_level, torch.Tensor)
+        z = self.q_sample(x, noise_level)
+        return z, noise_level
+
+
+
diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1245db832d567e251e66e03cb27c312276c8174
--- /dev/null
+++ b/ldm/modules/diffusionmodules/util.py
@@ -0,0 +1,274 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+
+from ldm.util import instantiate_from_config
+
+from pdb import set_trace as st
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+    if schedule == "linear":
+        betas = (
+                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+        )
+
+    elif schedule == "cosine":
+        timesteps = (
+                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+        )
+        alphas = timesteps / (1 + cosine_s) * np.pi / 2
+        alphas = torch.cos(alphas).pow(2)
+        alphas = alphas / alphas[0]
+        betas = 1 - alphas[1:] / alphas[:-1]
+        betas = np.clip(betas, a_min=0, a_max=0.999)
+
+    elif schedule == "sqrt_linear":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+    elif schedule == "sqrt":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+    else:
+        raise ValueError(f"schedule '{schedule}' unknown.")
+    return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+    if ddim_discr_method == 'uniform':
+        c = num_ddpm_timesteps // num_ddim_timesteps
+        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+    elif ddim_discr_method == 'quad':
+        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+    else:
+        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+    # add one to get the final alpha values right (the ones from first scale to data during sampling)
+    steps_out = ddim_timesteps + 1
+    if verbose:
+        print(f'Selected timesteps for ddim sampler: {steps_out}')
+    return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+    # select alphas for computing the variance schedule
+    alphas = alphacums[ddim_timesteps]
+    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+    if verbose:
+        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+        print(f'For the chosen value of eta, which is {eta}, '
+              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+    return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+@torch.autocast(device_type="cuda")
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        args = tuple(inputs) + tuple(params)
+        return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, length, *args):
+        # st()
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
+                                   "dtype": torch.get_autocast_gpu_dtype(),
+                                   "cache_enabled": torch.is_autocast_cache_enabled()}
+        with torch.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with torch.enable_grad(), \
+                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    else:
+        embedding = repeat(timesteps, 'b -> b d', d=dim)
+    return embedding
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+    def __init__(self, c_concat_config, c_crossattn_config):
+        super().__init__()
+        self.concat_conditioner = instantiate_from_config(c_concat_config)
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+    def forward(self, c_concat, c_crossattn):
+        c_concat = self.concat_conditioner(c_concat)
+        c_crossattn = self.crossattn_conditioner(c_crossattn)
+        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/ldm/modules/distributions/__init__.py b/ldm/modules/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9
--- /dev/null
+++ b/ldm/modules/distributions/distributions.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )
diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4
--- /dev/null
+++ b/ldm/modules/ema.py
@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+
+        self.m_name2s_name = {}
+        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
+        else torch.tensor(-1, dtype=torch.int))
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                # remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.', '')
+                self.m_name2s_name.update({name: s_name})
+                self.register_buffer(s_name, p.clone().detach().data)
+
+        self.collected_params = []
+
+    def reset_num_updates(self):
+        del self.num_updates
+        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
+
+    def forward(self, model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)
diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py
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diff --git a/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc b/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc
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diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..52acef3f8d84ea982d50be49bf08b50c3e6f9a47
--- /dev/null
+++ b/ldm/modules/encoders/modules.py
@@ -0,0 +1,570 @@
+import torch
+from typing import Dict, List, Optional, Tuple, Union
+import functools
+import fsspec
+import os
+import open_clip
+import torch.nn as nn
+from functools import partial
+import clip
+from einops import rearrange, repeat
+import kornia
+import numpy as np
+from inspect import isfunction
+
+from pdb import set_trace as st
+# from transformers import CLIPTokenizer, CLIPTextModel
+
+from ...util import (append_dims, autocast, count_params, default,
+                     disabled_train, expand_dims_like, instantiate_from_config)
+
+from ..x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+
+
+class AbstractEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key='class'):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+
+    def forward(self, batch, key=None):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        c = self.embedding(c)
+        return c
+
+
+class TransformerEmbedder(AbstractEncoder):
+    """Some transformer encoder layers"""
+    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+        super().__init__()
+        self.device = device
+        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
+
+    def forward(self, tokens):
+        tokens = tokens.to(self.device)  # meh
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, x):
+        return self(x)
+
+
+class BERTTokenizer(AbstractEncoder):
+    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+    def __init__(self, device="cuda", vq_interface=True, max_length=77):
+        super().__init__()
+        from transformers import BertTokenizerFast  # TODO: add to reuquirements
+        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
+        self.device = device
+        self.vq_interface = vq_interface
+        self.max_length = max_length
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        return tokens
+
+    @torch.no_grad()
+    def encode(self, text):
+        tokens = self(text)
+        if not self.vq_interface:
+            return tokens
+        return None, None, [None, None, tokens]
+
+    def decode(self, text):
+        return text
+
+
+class BERTEmbedder(AbstractEncoder):
+    """Uses the BERT tokenizr model and add some transformer encoder layers"""
+    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
+                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+        super().__init__()
+        self.use_tknz_fn = use_tokenizer
+        if self.use_tknz_fn:
+            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+        self.device = device
+        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
+                                              emb_dropout=embedding_dropout)
+
+    def forward(self, text):
+        if self.use_tknz_fn:
+            tokens = self.tknz_fn(text)#.to(self.device)
+        else:
+            tokens = text
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, text):
+        # output of length 77
+        return self(text)
+
+
+class SpatialRescaler(nn.Module):
+    def __init__(self,
+                 n_stages=1,
+                 method='bilinear',
+                 multiplier=0.5,
+                 in_channels=3,
+                 out_channels=None,
+                 bias=False):
+        super().__init__()
+        self.n_stages = n_stages
+        assert self.n_stages >= 0
+        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
+        self.multiplier = multiplier
+        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+        self.remap_output = out_channels is not None
+        if self.remap_output:
+            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
+            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
+
+    def forward(self,x):
+        for stage in range(self.n_stages):
+            x = self.interpolator(x, scale_factor=self.multiplier)
+
+
+        if self.remap_output:
+            x = self.channel_mapper(x)
+        return x
+
+    def encode(self, x):
+        return self(x)
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, use_eos_feature=False):
+        super().__init__()
+        from transformers import CLIPTokenizer, CLIPTextModel
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+
+        self.device = device
+        self.max_length = max_length
+        self.freeze()
+        self.use_eos_feature = use_eos_feature
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
+
+        if self.use_eos_feature: # for DiT
+            z = outputs.pooler_output # N 77 C
+        else:
+            z = outputs.last_hidden_state # N 77 C
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+class TextEmbedder(nn.Module):
+    """
+    Embeds text prompt into vector representations. Also handles text dropout for classifier-free guidance.
+    """
+    def __init__(self, dropout_prob=0.1, use_eos_feature=False):
+        super().__init__()
+        self.text_encodder = FrozenCLIPEmbedder(use_eos_feature=use_eos_feature) # no normalization projection
+        self.dropout_prob = dropout_prob
+    
+    def token_drop(self, text_prompts, force_drop_ids=None):
+        """
+        Drops text to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = np.random.uniform(0, 1, len(text_prompts)) < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = list(np.where(drop_ids, "None", text_prompts))
+        # print(labels)
+        return labels
+
+    def forward(self, text_prompts, train, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            text_prompts = self.token_drop(text_prompts, force_drop_ids)
+        embeddings = self.text_encodder(text_prompts)
+        return embeddings
+
+class FrozenCLIPTextEmbedder(nn.Module):
+    """
+    Uses the CLIP transformer encoder for text.
+    """
+    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True, dropout_prob=0., scale_clip_encoding=None):
+        super().__init__()
+        self.model, _ = clip.load(version, jit=False, device=device)
+        self.device = device
+        self.max_length = max_length
+        self.n_repeat = n_repeat
+        self.normalize = normalize
+        self.dropout_prob = dropout_prob
+        self.scale_clip_encoding = scale_clip_encoding
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = clip.tokenize(text).to(self.device)
+        z = self.model.encode_text(tokens)
+        if self.normalize:
+            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
+
+            if self.scale_clip_encoding is not None:
+                z = z * self.scale_clip_encoding
+
+        return z
+
+    def token_drop(self, text_prompts, force_drop_ids=None):
+        """
+        Drops text to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = np.random.uniform(0, 1, len(text_prompts)) < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = list(np.where(drop_ids, "None", text_prompts))
+        # print(labels)
+        return labels
+
+
+    def encode(self, text):
+        z = self(text)
+
+        if z.ndim==2: # match cross attention shape
+            z = z[:, None, :]
+        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
+
+        return z
+
+
+class FrozenClipImageEmbedder(nn.Module):
+    """
+        Uses the CLIP image encoder.
+        """
+    def __init__(
+            self,
+            model,
+            jit=False,
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+            antialias=False,
+            n_repeat=1,
+            dropout_prob=0.2, # follow Rodin
+            normalize_encoding=False,
+            scale_clip_encoding=1.0,
+        ):
+        super().__init__()
+        self.model, _ = clip.load(name=model, device=device, jit=jit)
+        self.n_repeat = n_repeat
+        self.normalize_encoding = normalize_encoding
+        self.scale_clip_encoding = torch.tensor(scale_clip_encoding, dtype=torch.float32, device=device)
+
+        self.antialias = antialias
+
+        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
+        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+
+        self.dropout_prob = dropout_prob
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(x, (224, 224),
+                                   interpolation='bicubic',align_corners=True,
+                                   antialias=self.antialias)
+        x = (x + 1.) / 2.
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std) # type: ignore
+        return x
+
+    def token_drop(self, z):
+        """
+        zero the image encoding to enable classifier-free guidance.
+        """
+        drop_ids = np.random.uniform(0, 1, z.shape[0]) < self.dropout_prob # idx token to drop
+        drop_ids = torch.from_numpy(drop_ids).unsqueeze(1).expand_as(z).bool().to(z.device)
+        z = torch.where(drop_ids, torch.zeros_like(z), z)
+        return z
+
+
+    def forward(self, x):
+        # x is assumed to be in range [-1,1]
+        # return self.model.encode_image(self.preprocess(x))
+        z = self.model.encode_image(self.preprocess(x))
+
+        # ? normalized features, seems not working?
+        if self.normalize_encoding:
+            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
+            if self.scale_clip_encoding:
+                # st()
+                z = z * self.scale_clip_encoding
+        
+        if self.dropout_prob>0: # for cfg
+            z = self.token_drop(z)
+
+        if z.ndim==2:
+            # repeat 1 dim, for context shape compatability.
+            z = z[:, None, :]
+        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
+        return z
+
+
+class AbstractEmbModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self._is_trainable = None
+        self._ucg_rate = None
+        self._input_key = None
+
+    @property
+    def is_trainable(self) -> bool:
+        return self._is_trainable
+
+    @property
+    def ucg_rate(self) -> Union[float, torch.Tensor]:
+        return self._ucg_rate
+
+    @property
+    def input_key(self) -> str:
+        return self._input_key
+
+    @is_trainable.setter
+    def is_trainable(self, value: bool):
+        self._is_trainable = value
+
+    @ucg_rate.setter
+    def ucg_rate(self, value: Union[float, torch.Tensor]):
+        self._ucg_rate = value
+
+    @input_key.setter
+    def input_key(self, value: str):
+        self._input_key = value
+
+    @is_trainable.deleter
+    def is_trainable(self):
+        del self._is_trainable
+
+    @ucg_rate.deleter
+    def ucg_rate(self):
+        del self._ucg_rate
+
+    @input_key.deleter
+    def input_key(self):
+        del self._input_key
+
+
+
+class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
+    """
+    Uses the OpenCLIP vision transformer encoder for images
+    """
+
+    def __init__(
+        self,
+        arch="ViT-H-14",
+        version="laion2b_s32b_b79k",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        antialias=True,
+        ucg_rate=0.0,
+        unsqueeze_dim=False,
+        repeat_to_max_len=False,
+        num_image_crops=0,
+        output_tokens=False,
+        init_device=None,
+    ):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch,
+            device=torch.device(default(init_device, "cpu")),
+            pretrained=version,
+        )
+        del model.transformer
+        self.model = model
+        self.max_crops = num_image_crops
+        self.pad_to_max_len = self.max_crops > 0
+        self.repeat_to_max_len = repeat_to_max_len and (not self.pad_to_max_len)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+        self.antialias = antialias
+
+        self.register_buffer(
+            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
+        )
+        self.register_buffer(
+            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
+        )
+        self.ucg_rate = ucg_rate
+        self.unsqueeze_dim = unsqueeze_dim
+        self.stored_batch = None
+        self.model.visual.output_tokens = output_tokens
+        self.output_tokens = output_tokens
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(
+            x,
+            (224, 224),
+            interpolation="bicubic",
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, image, no_dropout=False):
+        z = self.encode_with_vision_transformer(image)
+        tokens = None
+        if self.output_tokens:
+            z, tokens = z[0], z[1]
+        z = z.to(image.dtype)
+        if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
+            z = (
+                torch.bernoulli(
+                    (1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
+                )[:, None]
+                * z
+            )
+            if tokens is not None:
+                tokens = (
+                    expand_dims_like(
+                        torch.bernoulli(
+                            (1.0 - self.ucg_rate)
+                            * torch.ones(tokens.shape[0], device=tokens.device)
+                        ),
+                        tokens,
+                    )
+                    * tokens
+                )
+        if self.unsqueeze_dim:
+            z = z[:, None, :]
+        if self.output_tokens:
+            assert not self.repeat_to_max_len
+            assert not self.pad_to_max_len
+            return tokens, z
+        if self.repeat_to_max_len:
+            if z.dim() == 2:
+                z_ = z[:, None, :]
+            else:
+                z_ = zsgm/configs/txt2img-clipl-compat.yaml
+            return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
+        elif self.pad_to_max_len:
+            assert z.dim() == 3
+            z_pad = torch.cat(
+                (
+                    z,
+                    torch.zeros(
+                        z.shape[0],
+                        self.max_length - z.shape[1],
+                        z.shape[2],
+                        device=z.device,
+                    ),
+                ),
+                1,
+            )
+            return z_pad, z_pad[:, 0, ...]
+        return z
+
+    def encode_with_vision_transformer(self, img):
+        # if self.max_crops > 0:
+        #    img = self.preprocess_by_cropping(img)
+        if img.dim() == 5:
+            assert self.max_crops == img.shape[1]
+            img = rearrange(img, "b n c h w -> (b n) c h w")
+        img = self.preprocess(img)
+        if not self.output_tokens:
+            assert not self.model.visual.output_tokens
+            x = self.model.visual(img)
+            tokens = None
+        else:
+            assert self.model.visual.output_tokens
+            x, tokens = self.model.visual(img)
+        if self.max_crops > 0:
+            x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
+            # drop out between 0 and all along the sequence axis
+            x = (
+                torch.bernoulli(
+                    (1.0 - self.ucg_rate)
+                    * torch.ones(x.shape[0], x.shape[1], 1, device=x.device)
+                )
+                * x
+            )
+            if tokens is not None:
+                tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
+                print(
+                    f"You are running very experimental token-concat in {self.__class__.__name__}. "
+                    f"Check what you are doing, and then remove this message."
+                )
+        if self.output_tokens:
+            return x, tokens
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+class FrozenOpenCLIPImagePredictionEmbedder(AbstractEmbModel):
+    def __init__(
+        self,
+        # open_clip_embedding_config: Dict,
+        n_cond_frames: int,
+        n_copies: int,
+        open_clip_module,
+    ):
+        super().__init__()
+
+        self.n_cond_frames = n_cond_frames
+        self.n_copies = n_copies
+        # self.open_clip = instantiate_from_config(open_clip_embedding_config)
+        self.open_clip = open_clip_module
+
+    def forward(self, vid):
+        vid = self.open_clip(vid)
+        vid = rearrange(vid, "(b t) d -> b t d", t=self.n_cond_frames)
+        vid = repeat(vid, "b t d -> (b s) t d", s=self.n_copies)
+
+        return vid
+
+
+if __name__ == "__main__":
+    from ldm.util import count_params
+    model = FrozenCLIPEmbedder()
+    count_params(model, verbose=True)
diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7836cada81f90ded99c58d5942eea4c3477f58fc
--- /dev/null
+++ b/ldm/modules/image_degradation/__init__.py
@@ -0,0 +1,2 @@
+from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
+from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..32ef56169978e550090261cddbcf5eb611a6173b
--- /dev/null
+++ b/ldm/modules/image_degradation/bsrgan.py
@@ -0,0 +1,730 @@
+# -*- coding: utf-8 -*-
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+
+def modcrop_np(img, sf):
+    '''
+    Args:
+        img: numpy image, WxH or WxHxC
+        sf: scale factor
+    Return:
+        cropped image
+    '''
+    w, h = img.shape[:2]
+    im = np.copy(img)
+    return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+    k_size = k.shape[0]
+    # Calculate the big kernels size
+    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+    # Loop over the small kernel to fill the big one
+    for r in range(k_size):
+        for c in range(k_size):
+            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+    crop = k_size // 2
+    cropped_big_k = big_k[crop:-crop, crop:-crop]
+    # Normalize to 1
+    return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+    """ generate an anisotropic Gaussian kernel
+    Args:
+        ksize : e.g., 15, kernel size
+        theta : [0,  pi], rotation angle range
+        l1    : [0.1,50], scaling of eigenvalues
+        l2    : [0.1,l1], scaling of eigenvalues
+        If l1 = l2, will get an isotropic Gaussian kernel.
+    Returns:
+        k     : kernel
+    """
+
+    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+    D = np.array([[l1, 0], [0, l2]])
+    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+    return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+    center = size / 2.0 + 0.5
+    k = np.zeros([size, size])
+    for y in range(size):
+        for x in range(size):
+            cy = y - center + 1
+            cx = x - center + 1
+            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+    k = k / np.sum(k)
+    return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+    """shift pixel for super-resolution with different scale factors
+    Args:
+        x: WxHxC or WxH
+        sf: scale factor
+        upper_left: shift direction
+    """
+    h, w = x.shape[:2]
+    shift = (sf - 1) * 0.5
+    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+    if upper_left:
+        x1 = xv + shift
+        y1 = yv + shift
+    else:
+        x1 = xv - shift
+        y1 = yv - shift
+
+    x1 = np.clip(x1, 0, w - 1)
+    y1 = np.clip(y1, 0, h - 1)
+
+    if x.ndim == 2:
+        x = interp2d(xv, yv, x)(x1, y1)
+    if x.ndim == 3:
+        for i in range(x.shape[-1]):
+            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+    return x
+
+
+def blur(x, k):
+    '''
+    x: image, NxcxHxW
+    k: kernel, Nx1xhxw
+    '''
+    n, c = x.shape[:2]
+    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+    k = k.repeat(1, c, 1, 1)
+    k = k.view(-1, 1, k.shape[2], k.shape[3])
+    x = x.view(1, -1, x.shape[2], x.shape[3])
+    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+    x = x.view(n, c, x.shape[2], x.shape[3])
+
+    return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+    """"
+    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+    # Kai Zhang
+    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
+    # max_var = 2.5 * sf
+    """
+    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+    theta = np.random.rand() * np.pi  # random theta
+    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+    # Set COV matrix using Lambdas and Theta
+    LAMBDA = np.diag([lambda_1, lambda_2])
+    Q = np.array([[np.cos(theta), -np.sin(theta)],
+                  [np.sin(theta), np.cos(theta)]])
+    SIGMA = Q @ LAMBDA @ Q.T
+    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+    # Set expectation position (shifting kernel for aligned image)
+    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
+    MU = MU[None, None, :, None]
+
+    # Create meshgrid for Gaussian
+    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+    Z = np.stack([X, Y], 2)[:, :, :, None]
+
+    # Calcualte Gaussian for every pixel of the kernel
+    ZZ = Z - MU
+    ZZ_t = ZZ.transpose(0, 1, 3, 2)
+    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+    # shift the kernel so it will be centered
+    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+    # Normalize the kernel and return
+    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+    kernel = raw_kernel / np.sum(raw_kernel)
+    return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+    hsize = [hsize, hsize]
+    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+    std = sigma
+    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+    arg = -(x * x + y * y) / (2 * std * std)
+    h = np.exp(arg)
+    h[h < scipy.finfo(float).eps * h.max()] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h = h / sumh
+    return h
+
+
+def fspecial_laplacian(alpha):
+    alpha = max([0, min([alpha, 1])])
+    h1 = alpha / (alpha + 1)
+    h2 = (1 - alpha) / (alpha + 1)
+    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+    h = np.array(h)
+    return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+    '''
+    python code from:
+    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+    '''
+    if filter_type == 'gaussian':
+        return fspecial_gaussian(*args, **kwargs)
+    if filter_type == 'laplacian':
+        return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+    '''
+    Args:
+        x: HxWxC image, [0, 1]
+        sf: down-scale factor
+    Return:
+        bicubicly downsampled LR image
+    '''
+    x = util.imresize_np(x, scale=1 / sf)
+    return x
+
+
+def srmd_degradation(x, k, sf=3):
+    ''' blur + bicubic downsampling
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2018learning,
+          title={Learning a single convolutional super-resolution network for multiple degradations},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={3262--3271},
+          year={2018}
+        }
+    '''
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
+    x = bicubic_degradation(x, sf=sf)
+    return x
+
+
+def dpsr_degradation(x, k, sf=3):
+    ''' bicubic downsampling + blur
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2019deep,
+          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={1671--1681},
+          year={2019}
+        }
+    '''
+    x = bicubic_degradation(x, sf=sf)
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    return x
+
+
+def classical_degradation(x, k, sf=3):
+    ''' blur + downsampling
+    Args:
+        x: HxWxC image, [0, 1]/[0, 255]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    '''
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+    st = 0
+    return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+    """USM sharpening. borrowed from real-ESRGAN
+    Input image: I; Blurry image: B.
+    1. K = I + weight * (I - B)
+    2. Mask = 1 if abs(I - B) > threshold, else: 0
+    3. Blur mask:
+    4. Out = Mask * K + (1 - Mask) * I
+    Args:
+        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+        weight (float): Sharp weight. Default: 1.
+        radius (float): Kernel size of Gaussian blur. Default: 50.
+        threshold (int):
+    """
+    if radius % 2 == 0:
+        radius += 1
+    blur = cv2.GaussianBlur(img, (radius, radius), 0)
+    residual = img - blur
+    mask = np.abs(residual) * 255 > threshold
+    mask = mask.astype('float32')
+    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+    K = img + weight * residual
+    K = np.clip(K, 0, 1)
+    return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+    wd2 = 4.0 + sf
+    wd = 2.0 + 0.2 * sf
+    if random.random() < 0.5:
+        l1 = wd2 * random.random()
+        l2 = wd2 * random.random()
+        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+    else:
+        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
+    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+    return img
+
+
+def add_resize(img, sf=4):
+    rnum = np.random.rand()
+    if rnum > 0.8:  # up
+        sf1 = random.uniform(1, 2)
+    elif rnum < 0.7:  # down
+        sf1 = random.uniform(0.5 / sf, 1)
+    else:
+        sf1 = 1.0
+    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+    img = np.clip(img, 0.0, 1.0)
+
+    return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+#     noise_level = random.randint(noise_level1, noise_level2)
+#     rnum = np.random.rand()
+#     if rnum > 0.6:  # add color Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+#     elif rnum < 0.4:  # add grayscale Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+#     else:  # add  noise
+#         L = noise_level2 / 255.
+#         D = np.diag(np.random.rand(3))
+#         U = orth(np.random.rand(3, 3))
+#         conv = np.dot(np.dot(np.transpose(U), D), U)
+#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+#     img = np.clip(img, 0.0, 1.0)
+#     return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    rnum = np.random.rand()
+    if rnum > 0.6:  # add color Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:  # add grayscale Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:  # add  noise
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    img = np.clip(img, 0.0, 1.0)
+    rnum = random.random()
+    if rnum > 0.6:
+        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:
+        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_Poisson_noise(img):
+    img = np.clip((img * 255.0).round(), 0, 255) / 255.
+    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
+    if random.random() < 0.5:
+        img = np.random.poisson(img * vals).astype(np.float32) / vals
+    else:
+        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+        img += noise_gray[:, :, np.newaxis]
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_JPEG_noise(img):
+    quality_factor = random.randint(30, 95)
+    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+    img = cv2.imdecode(encimg, 1)
+    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+    return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+    h, w = lq.shape[:2]
+    rnd_h = random.randint(0, h - lq_patchsize)
+    rnd_w = random.randint(0, w - lq_patchsize)
+    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+    return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = img.shape[:2]
+    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = img.shape[:2]
+
+    if h < lq_patchsize * sf or w < lq_patchsize * sf:
+        raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+    hq = img.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+                             interpolation=random.choice([1, 2, 3]))
+        else:
+            img = util.imresize_np(img, 1 / 2, True)
+        img = np.clip(img, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            img = add_blur(img, sf=sf)
+
+        elif i == 1:
+            img = add_blur(img, sf=sf)
+
+        elif i == 2:
+            a, b = img.shape[1], img.shape[0]
+            # downsample2
+            if random.random() < 0.75:
+                sf1 = random.uniform(1, 2 * sf)
+                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+                                 interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                img = img[0::sf, 0::sf, ...]  # nearest downsampling
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                img = add_JPEG_noise(img)
+
+        elif i == 6:
+            # add processed camera sensor noise
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    img = add_JPEG_noise(img)
+
+    # random crop
+    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+    return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    image = util.uint2single(image)
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = image.shape[:2]
+    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = image.shape[:2]
+
+    hq = image.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+                               interpolation=random.choice([1, 2, 3]))
+        else:
+            image = util.imresize_np(image, 1 / 2, True)
+        image = np.clip(image, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            image = add_blur(image, sf=sf)
+
+        elif i == 1:
+            image = add_blur(image, sf=sf)
+
+        elif i == 2:
+            a, b = image.shape[1], image.shape[0]
+            # downsample2
+            if random.random() < 0.75:
+                sf1 = random.uniform(1, 2 * sf)
+                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+                                   interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                image = image[0::sf, 0::sf, ...]  # nearest downsampling
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                image = add_JPEG_noise(image)
+
+        # elif i == 6:
+        #     # add processed camera sensor noise
+        #     if random.random() < isp_prob and isp_model is not None:
+        #         with torch.no_grad():
+        #             img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    image = add_JPEG_noise(image)
+    image = util.single2uint(image)
+    example = {"image":image}
+    return example
+
+
+# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
+def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
+    """
+    This is an extended degradation model by combining
+    the degradation models of BSRGAN and Real-ESRGAN
+    ----------
+    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+    sf: scale factor
+    use_shuffle: the degradation shuffle
+    use_sharp: sharpening the img
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+
+    h1, w1 = img.shape[:2]
+    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = img.shape[:2]
+
+    if h < lq_patchsize * sf or w < lq_patchsize * sf:
+        raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+    if use_sharp:
+        img = add_sharpening(img)
+    hq = img.copy()
+
+    if random.random() < shuffle_prob:
+        shuffle_order = random.sample(range(13), 13)
+    else:
+        shuffle_order = list(range(13))
+        # local shuffle for noise, JPEG is always the last one
+        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
+        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
+
+    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
+
+    for i in shuffle_order:
+        if i == 0:
+            img = add_blur(img, sf=sf)
+        elif i == 1:
+            img = add_resize(img, sf=sf)
+        elif i == 2:
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+        elif i == 3:
+            if random.random() < poisson_prob:
+                img = add_Poisson_noise(img)
+        elif i == 4:
+            if random.random() < speckle_prob:
+                img = add_speckle_noise(img)
+        elif i == 5:
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+        elif i == 6:
+            img = add_JPEG_noise(img)
+        elif i == 7:
+            img = add_blur(img, sf=sf)
+        elif i == 8:
+            img = add_resize(img, sf=sf)
+        elif i == 9:
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+        elif i == 10:
+            if random.random() < poisson_prob:
+                img = add_Poisson_noise(img)
+        elif i == 11:
+            if random.random() < speckle_prob:
+                img = add_speckle_noise(img)
+        elif i == 12:
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+        else:
+            print('check the shuffle!')
+
+    # resize to desired size
+    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
+                     interpolation=random.choice([1, 2, 3]))
+
+    # add final JPEG compression noise
+    img = add_JPEG_noise(img)
+
+    # random crop
+    img, hq = random_crop(img, hq, sf, lq_patchsize)
+
+    return img, hq
+
+
+if __name__ == '__main__':
+	print("hey")
+	img = util.imread_uint('utils/test.png', 3)
+	print(img)
+	img = util.uint2single(img)
+	print(img)
+	img = img[:448, :448]
+	h = img.shape[0] // 4
+	print("resizing to", h)
+	sf = 4
+	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+	for i in range(20):
+		print(i)
+		img_lq = deg_fn(img)
+		print(img_lq)
+		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
+		print(img_lq.shape)
+		print("bicubic", img_lq_bicubic.shape)
+		print(img_hq.shape)
+		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+		                        interpolation=0)
+		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+		                        interpolation=0)
+		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+		util.imsave(img_concat, str(i) + '.png')
+
+
diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py
new file mode 100644
index 0000000000000000000000000000000000000000..808c7f882cb75e2ba2340d5b55881d11927351f0
--- /dev/null
+++ b/ldm/modules/image_degradation/bsrgan_light.py
@@ -0,0 +1,651 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+def modcrop_np(img, sf):
+    '''
+    Args:
+        img: numpy image, WxH or WxHxC
+        sf: scale factor
+    Return:
+        cropped image
+    '''
+    w, h = img.shape[:2]
+    im = np.copy(img)
+    return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+    k_size = k.shape[0]
+    # Calculate the big kernels size
+    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+    # Loop over the small kernel to fill the big one
+    for r in range(k_size):
+        for c in range(k_size):
+            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+    crop = k_size // 2
+    cropped_big_k = big_k[crop:-crop, crop:-crop]
+    # Normalize to 1
+    return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+    """ generate an anisotropic Gaussian kernel
+    Args:
+        ksize : e.g., 15, kernel size
+        theta : [0,  pi], rotation angle range
+        l1    : [0.1,50], scaling of eigenvalues
+        l2    : [0.1,l1], scaling of eigenvalues
+        If l1 = l2, will get an isotropic Gaussian kernel.
+    Returns:
+        k     : kernel
+    """
+
+    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+    D = np.array([[l1, 0], [0, l2]])
+    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+    return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+    center = size / 2.0 + 0.5
+    k = np.zeros([size, size])
+    for y in range(size):
+        for x in range(size):
+            cy = y - center + 1
+            cx = x - center + 1
+            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+    k = k / np.sum(k)
+    return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+    """shift pixel for super-resolution with different scale factors
+    Args:
+        x: WxHxC or WxH
+        sf: scale factor
+        upper_left: shift direction
+    """
+    h, w = x.shape[:2]
+    shift = (sf - 1) * 0.5
+    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+    if upper_left:
+        x1 = xv + shift
+        y1 = yv + shift
+    else:
+        x1 = xv - shift
+        y1 = yv - shift
+
+    x1 = np.clip(x1, 0, w - 1)
+    y1 = np.clip(y1, 0, h - 1)
+
+    if x.ndim == 2:
+        x = interp2d(xv, yv, x)(x1, y1)
+    if x.ndim == 3:
+        for i in range(x.shape[-1]):
+            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+    return x
+
+
+def blur(x, k):
+    '''
+    x: image, NxcxHxW
+    k: kernel, Nx1xhxw
+    '''
+    n, c = x.shape[:2]
+    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+    k = k.repeat(1, c, 1, 1)
+    k = k.view(-1, 1, k.shape[2], k.shape[3])
+    x = x.view(1, -1, x.shape[2], x.shape[3])
+    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+    x = x.view(n, c, x.shape[2], x.shape[3])
+
+    return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+    """"
+    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+    # Kai Zhang
+    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
+    # max_var = 2.5 * sf
+    """
+    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+    theta = np.random.rand() * np.pi  # random theta
+    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+    # Set COV matrix using Lambdas and Theta
+    LAMBDA = np.diag([lambda_1, lambda_2])
+    Q = np.array([[np.cos(theta), -np.sin(theta)],
+                  [np.sin(theta), np.cos(theta)]])
+    SIGMA = Q @ LAMBDA @ Q.T
+    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+    # Set expectation position (shifting kernel for aligned image)
+    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
+    MU = MU[None, None, :, None]
+
+    # Create meshgrid for Gaussian
+    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+    Z = np.stack([X, Y], 2)[:, :, :, None]
+
+    # Calcualte Gaussian for every pixel of the kernel
+    ZZ = Z - MU
+    ZZ_t = ZZ.transpose(0, 1, 3, 2)
+    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+    # shift the kernel so it will be centered
+    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+    # Normalize the kernel and return
+    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+    kernel = raw_kernel / np.sum(raw_kernel)
+    return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+    hsize = [hsize, hsize]
+    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+    std = sigma
+    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+    arg = -(x * x + y * y) / (2 * std * std)
+    h = np.exp(arg)
+    h[h < scipy.finfo(float).eps * h.max()] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h = h / sumh
+    return h
+
+
+def fspecial_laplacian(alpha):
+    alpha = max([0, min([alpha, 1])])
+    h1 = alpha / (alpha + 1)
+    h2 = (1 - alpha) / (alpha + 1)
+    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+    h = np.array(h)
+    return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+    '''
+    python code from:
+    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+    '''
+    if filter_type == 'gaussian':
+        return fspecial_gaussian(*args, **kwargs)
+    if filter_type == 'laplacian':
+        return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+    '''
+    Args:
+        x: HxWxC image, [0, 1]
+        sf: down-scale factor
+    Return:
+        bicubicly downsampled LR image
+    '''
+    x = util.imresize_np(x, scale=1 / sf)
+    return x
+
+
+def srmd_degradation(x, k, sf=3):
+    ''' blur + bicubic downsampling
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2018learning,
+          title={Learning a single convolutional super-resolution network for multiple degradations},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={3262--3271},
+          year={2018}
+        }
+    '''
+    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
+    x = bicubic_degradation(x, sf=sf)
+    return x
+
+
+def dpsr_degradation(x, k, sf=3):
+    ''' bicubic downsampling + blur
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2019deep,
+          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={1671--1681},
+          year={2019}
+        }
+    '''
+    x = bicubic_degradation(x, sf=sf)
+    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    return x
+
+
+def classical_degradation(x, k, sf=3):
+    ''' blur + downsampling
+    Args:
+        x: HxWxC image, [0, 1]/[0, 255]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    '''
+    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+    st = 0
+    return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+    """USM sharpening. borrowed from real-ESRGAN
+    Input image: I; Blurry image: B.
+    1. K = I + weight * (I - B)
+    2. Mask = 1 if abs(I - B) > threshold, else: 0
+    3. Blur mask:
+    4. Out = Mask * K + (1 - Mask) * I
+    Args:
+        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+        weight (float): Sharp weight. Default: 1.
+        radius (float): Kernel size of Gaussian blur. Default: 50.
+        threshold (int):
+    """
+    if radius % 2 == 0:
+        radius += 1
+    blur = cv2.GaussianBlur(img, (radius, radius), 0)
+    residual = img - blur
+    mask = np.abs(residual) * 255 > threshold
+    mask = mask.astype('float32')
+    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+    K = img + weight * residual
+    K = np.clip(K, 0, 1)
+    return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+    wd2 = 4.0 + sf
+    wd = 2.0 + 0.2 * sf
+
+    wd2 = wd2/4
+    wd = wd/4
+
+    if random.random() < 0.5:
+        l1 = wd2 * random.random()
+        l2 = wd2 * random.random()
+        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+    else:
+        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
+    img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+    return img
+
+
+def add_resize(img, sf=4):
+    rnum = np.random.rand()
+    if rnum > 0.8:  # up
+        sf1 = random.uniform(1, 2)
+    elif rnum < 0.7:  # down
+        sf1 = random.uniform(0.5 / sf, 1)
+    else:
+        sf1 = 1.0
+    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+    img = np.clip(img, 0.0, 1.0)
+
+    return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+#     noise_level = random.randint(noise_level1, noise_level2)
+#     rnum = np.random.rand()
+#     if rnum > 0.6:  # add color Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+#     elif rnum < 0.4:  # add grayscale Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+#     else:  # add  noise
+#         L = noise_level2 / 255.
+#         D = np.diag(np.random.rand(3))
+#         U = orth(np.random.rand(3, 3))
+#         conv = np.dot(np.dot(np.transpose(U), D), U)
+#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+#     img = np.clip(img, 0.0, 1.0)
+#     return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    rnum = np.random.rand()
+    if rnum > 0.6:  # add color Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:  # add grayscale Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:  # add  noise
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    img = np.clip(img, 0.0, 1.0)
+    rnum = random.random()
+    if rnum > 0.6:
+        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:
+        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_Poisson_noise(img):
+    img = np.clip((img * 255.0).round(), 0, 255) / 255.
+    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
+    if random.random() < 0.5:
+        img = np.random.poisson(img * vals).astype(np.float32) / vals
+    else:
+        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+        img += noise_gray[:, :, np.newaxis]
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_JPEG_noise(img):
+    quality_factor = random.randint(80, 95)
+    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+    img = cv2.imdecode(encimg, 1)
+    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+    return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+    h, w = lq.shape[:2]
+    rnd_h = random.randint(0, h - lq_patchsize)
+    rnd_w = random.randint(0, w - lq_patchsize)
+    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+    return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = img.shape[:2]
+    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = img.shape[:2]
+
+    if h < lq_patchsize * sf or w < lq_patchsize * sf:
+        raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+    hq = img.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+                             interpolation=random.choice([1, 2, 3]))
+        else:
+            img = util.imresize_np(img, 1 / 2, True)
+        img = np.clip(img, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            img = add_blur(img, sf=sf)
+
+        elif i == 1:
+            img = add_blur(img, sf=sf)
+
+        elif i == 2:
+            a, b = img.shape[1], img.shape[0]
+            # downsample2
+            if random.random() < 0.75:
+                sf1 = random.uniform(1, 2 * sf)
+                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+                                 interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                img = img[0::sf, 0::sf, ...]  # nearest downsampling
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                img = add_JPEG_noise(img)
+
+        elif i == 6:
+            # add processed camera sensor noise
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    img = add_JPEG_noise(img)
+
+    # random crop
+    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+    return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    image = util.uint2single(image)
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = image.shape[:2]
+    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = image.shape[:2]
+
+    hq = image.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+                               interpolation=random.choice([1, 2, 3]))
+        else:
+            image = util.imresize_np(image, 1 / 2, True)
+        image = np.clip(image, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            image = add_blur(image, sf=sf)
+
+        # elif i == 1:
+        #     image = add_blur(image, sf=sf)
+
+        if i == 0:
+            pass
+
+        elif i == 2:
+            a, b = image.shape[1], image.shape[0]
+            # downsample2
+            if random.random() < 0.8:
+                sf1 = random.uniform(1, 2 * sf)
+                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+                                   interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                image = image[0::sf, 0::sf, ...]  # nearest downsampling
+
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                image = add_JPEG_noise(image)
+        #
+        # elif i == 6:
+        #     # add processed camera sensor noise
+        #     if random.random() < isp_prob and isp_model is not None:
+        #         with torch.no_grad():
+        #             img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    image = add_JPEG_noise(image)
+    image = util.single2uint(image)
+    if up:
+        image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC)  # todo: random, as above? want to condition on it then
+    example = {"image": image}
+    return example
+
+
+
+
+if __name__ == '__main__':
+    print("hey")
+    img = util.imread_uint('utils/test.png', 3)
+    img = img[:448, :448]
+    h = img.shape[0] // 4
+    print("resizing to", h)
+    sf = 4
+    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+    for i in range(20):
+        print(i)
+        img_hq = img
+        img_lq = deg_fn(img)["image"]
+        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
+        print(img_lq)
+        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
+        print(img_lq.shape)
+        print("bicubic", img_lq_bicubic.shape)
+        print(img_hq.shape)
+        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+                                interpolation=0)
+        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
+                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+                                        interpolation=0)
+        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+        util.imsave(img_concat, str(i) + '.png')
diff --git a/ldm/modules/image_degradation/utils/test.png b/ldm/modules/image_degradation/utils/test.png
new file mode 100644
index 0000000000000000000000000000000000000000..4249b43de0f22707758d13c240268a401642f6e6
Binary files /dev/null and b/ldm/modules/image_degradation/utils/test.png differ
diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..0175f155ad900ae33c3c46ed87f49b352e3faf98
--- /dev/null
+++ b/ldm/modules/image_degradation/utils_image.py
@@ -0,0 +1,916 @@
+import os
+import math
+import random
+import numpy as np
+import torch
+import cv2
+from torchvision.utils import make_grid
+from datetime import datetime
+#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
+
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+
+'''
+# --------------------------------------------
+# Kai Zhang (github: https://github.com/cszn)
+# 03/Mar/2019
+# --------------------------------------------
+# https://github.com/twhui/SRGAN-pyTorch
+# https://github.com/xinntao/BasicSR
+# --------------------------------------------
+'''
+
+
+IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
+
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def get_timestamp():
+    return datetime.now().strftime('%y%m%d-%H%M%S')
+
+
+def imshow(x, title=None, cbar=False, figsize=None):
+    plt.figure(figsize=figsize)
+    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
+    if title:
+        plt.title(title)
+    if cbar:
+        plt.colorbar()
+    plt.show()
+
+
+def surf(Z, cmap='rainbow', figsize=None):
+    plt.figure(figsize=figsize)
+    ax3 = plt.axes(projection='3d')
+
+    w, h = Z.shape[:2]
+    xx = np.arange(0,w,1)
+    yy = np.arange(0,h,1)
+    X, Y = np.meshgrid(xx, yy)
+    ax3.plot_surface(X,Y,Z,cmap=cmap)
+    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
+    plt.show()
+
+
+'''
+# --------------------------------------------
+# get image pathes
+# --------------------------------------------
+'''
+
+
+def get_image_paths(dataroot):
+    paths = None  # return None if dataroot is None
+    if dataroot is not None:
+        paths = sorted(_get_paths_from_images(dataroot))
+    return paths
+
+
+def _get_paths_from_images(path):
+    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
+    images = []
+    for dirpath, _, fnames in sorted(os.walk(path)):
+        for fname in sorted(fnames):
+            if is_image_file(fname):
+                img_path = os.path.join(dirpath, fname)
+                images.append(img_path)
+    assert images, '{:s} has no valid image file'.format(path)
+    return images
+
+
+'''
+# --------------------------------------------
+# split large images into small images 
+# --------------------------------------------
+'''
+
+
+def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
+    w, h = img.shape[:2]
+    patches = []
+    if w > p_max and h > p_max:
+        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
+        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
+        w1.append(w-p_size)
+        h1.append(h-p_size)
+#        print(w1)
+#        print(h1)
+        for i in w1:
+            for j in h1:
+                patches.append(img[i:i+p_size, j:j+p_size,:])
+    else:
+        patches.append(img)
+
+    return patches
+
+
+def imssave(imgs, img_path):
+    """
+    imgs: list, N images of size WxHxC
+    """
+    img_name, ext = os.path.splitext(os.path.basename(img_path))
+
+    for i, img in enumerate(imgs):
+        if img.ndim == 3:
+            img = img[:, :, [2, 1, 0]]
+        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
+        cv2.imwrite(new_path, img)
+
+
+def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
+    """
+    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
+    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
+    will be splitted.
+    Args:
+        original_dataroot:
+        taget_dataroot:
+        p_size: size of small images
+        p_overlap: patch size in training is a good choice
+        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
+    """
+    paths = get_image_paths(original_dataroot)
+    for img_path in paths:
+        # img_name, ext = os.path.splitext(os.path.basename(img_path))
+        img = imread_uint(img_path, n_channels=n_channels)
+        patches = patches_from_image(img, p_size, p_overlap, p_max)
+        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
+        #if original_dataroot == taget_dataroot:
+        #del img_path
+
+'''
+# --------------------------------------------
+# makedir
+# --------------------------------------------
+'''
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+def mkdirs(paths):
+    if isinstance(paths, str):
+        mkdir(paths)
+    else:
+        for path in paths:
+            mkdir(path)
+
+
+def mkdir_and_rename(path):
+    if os.path.exists(path):
+        new_name = path + '_archived_' + get_timestamp()
+        print('Path already exists. Rename it to [{:s}]'.format(new_name))
+        os.rename(path, new_name)
+    os.makedirs(path)
+
+
+'''
+# --------------------------------------------
+# read image from path
+# opencv is fast, but read BGR numpy image
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# get uint8 image of size HxWxn_channles (RGB)
+# --------------------------------------------
+def imread_uint(path, n_channels=3):
+    #  input: path
+    # output: HxWx3(RGB or GGG), or HxWx1 (G)
+    if n_channels == 1:
+        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
+        img = np.expand_dims(img, axis=2)  # HxWx1
+    elif n_channels == 3:
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
+        if img.ndim == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
+        else:
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
+    return img
+
+
+# --------------------------------------------
+# matlab's imwrite
+# --------------------------------------------
+def imsave(img, img_path):
+    img = np.squeeze(img)
+    if img.ndim == 3:
+        img = img[:, :, [2, 1, 0]]
+    cv2.imwrite(img_path, img)
+
+def imwrite(img, img_path):
+    img = np.squeeze(img)
+    if img.ndim == 3:
+        img = img[:, :, [2, 1, 0]]
+    cv2.imwrite(img_path, img)
+
+
+
+# --------------------------------------------
+# get single image of size HxWxn_channles (BGR)
+# --------------------------------------------
+def read_img(path):
+    # read image by cv2
+    # return: Numpy float32, HWC, BGR, [0,1]
+    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
+    img = img.astype(np.float32) / 255.
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    # some images have 4 channels
+    if img.shape[2] > 3:
+        img = img[:, :, :3]
+    return img
+
+
+'''
+# --------------------------------------------
+# image format conversion
+# --------------------------------------------
+# numpy(single) <--->  numpy(unit)
+# numpy(single) <--->  tensor
+# numpy(unit)   <--->  tensor
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# numpy(single) [0, 1] <--->  numpy(unit)
+# --------------------------------------------
+
+
+def uint2single(img):
+
+    return np.float32(img/255.)
+
+
+def single2uint(img):
+
+    return np.uint8((img.clip(0, 1)*255.).round())
+
+
+def uint162single(img):
+
+    return np.float32(img/65535.)
+
+
+def single2uint16(img):
+
+    return np.uint16((img.clip(0, 1)*65535.).round())
+
+
+# --------------------------------------------
+# numpy(unit) (HxWxC or HxW) <--->  tensor
+# --------------------------------------------
+
+
+# convert uint to 4-dimensional torch tensor
+def uint2tensor4(img):
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
+
+
+# convert uint to 3-dimensional torch tensor
+def uint2tensor3(img):
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
+
+
+# convert 2/3/4-dimensional torch tensor to uint
+def tensor2uint(img):
+    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
+    if img.ndim == 3:
+        img = np.transpose(img, (1, 2, 0))
+    return np.uint8((img*255.0).round())
+
+
+# --------------------------------------------
+# numpy(single) (HxWxC) <--->  tensor
+# --------------------------------------------
+
+
+# convert single (HxWxC) to 3-dimensional torch tensor
+def single2tensor3(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
+
+
+# convert single (HxWxC) to 4-dimensional torch tensor
+def single2tensor4(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
+
+
+# convert torch tensor to single
+def tensor2single(img):
+    img = img.data.squeeze().float().cpu().numpy()
+    if img.ndim == 3:
+        img = np.transpose(img, (1, 2, 0))
+
+    return img
+
+# convert torch tensor to single
+def tensor2single3(img):
+    img = img.data.squeeze().float().cpu().numpy()
+    if img.ndim == 3:
+        img = np.transpose(img, (1, 2, 0))
+    elif img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    return img
+
+
+def single2tensor5(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
+
+
+def single32tensor5(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
+
+
+def single42tensor4(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+
+
+# from skimage.io import imread, imsave
+def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
+    '''
+    Converts a torch Tensor into an image Numpy array of BGR channel order
+    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
+    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
+    '''
+    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
+    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
+    n_dim = tensor.dim()
+    if n_dim == 4:
+        n_img = len(tensor)
+        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
+        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
+    elif n_dim == 3:
+        img_np = tensor.numpy()
+        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
+    elif n_dim == 2:
+        img_np = tensor.numpy()
+    else:
+        raise TypeError(
+            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
+    if out_type == np.uint8:
+        img_np = (img_np * 255.0).round()
+        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
+    return img_np.astype(out_type)
+
+
+'''
+# --------------------------------------------
+# Augmentation, flipe and/or rotate
+# --------------------------------------------
+# The following two are enough.
+# (1) augmet_img: numpy image of WxHxC or WxH
+# (2) augment_img_tensor4: tensor image 1xCxWxH
+# --------------------------------------------
+'''
+
+
+def augment_img(img, mode=0):
+    '''Kai Zhang (github: https://github.com/cszn)
+    '''
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return np.flipud(np.rot90(img))
+    elif mode == 2:
+        return np.flipud(img)
+    elif mode == 3:
+        return np.rot90(img, k=3)
+    elif mode == 4:
+        return np.flipud(np.rot90(img, k=2))
+    elif mode == 5:
+        return np.rot90(img)
+    elif mode == 6:
+        return np.rot90(img, k=2)
+    elif mode == 7:
+        return np.flipud(np.rot90(img, k=3))
+
+
+def augment_img_tensor4(img, mode=0):
+    '''Kai Zhang (github: https://github.com/cszn)
+    '''
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return img.rot90(1, [2, 3]).flip([2])
+    elif mode == 2:
+        return img.flip([2])
+    elif mode == 3:
+        return img.rot90(3, [2, 3])
+    elif mode == 4:
+        return img.rot90(2, [2, 3]).flip([2])
+    elif mode == 5:
+        return img.rot90(1, [2, 3])
+    elif mode == 6:
+        return img.rot90(2, [2, 3])
+    elif mode == 7:
+        return img.rot90(3, [2, 3]).flip([2])
+
+
+def augment_img_tensor(img, mode=0):
+    '''Kai Zhang (github: https://github.com/cszn)
+    '''
+    img_size = img.size()
+    img_np = img.data.cpu().numpy()
+    if len(img_size) == 3:
+        img_np = np.transpose(img_np, (1, 2, 0))
+    elif len(img_size) == 4:
+        img_np = np.transpose(img_np, (2, 3, 1, 0))
+    img_np = augment_img(img_np, mode=mode)
+    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
+    if len(img_size) == 3:
+        img_tensor = img_tensor.permute(2, 0, 1)
+    elif len(img_size) == 4:
+        img_tensor = img_tensor.permute(3, 2, 0, 1)
+
+    return img_tensor.type_as(img)
+
+
+def augment_img_np3(img, mode=0):
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return img.transpose(1, 0, 2)
+    elif mode == 2:
+        return img[::-1, :, :]
+    elif mode == 3:
+        img = img[::-1, :, :]
+        img = img.transpose(1, 0, 2)
+        return img
+    elif mode == 4:
+        return img[:, ::-1, :]
+    elif mode == 5:
+        img = img[:, ::-1, :]
+        img = img.transpose(1, 0, 2)
+        return img
+    elif mode == 6:
+        img = img[:, ::-1, :]
+        img = img[::-1, :, :]
+        return img
+    elif mode == 7:
+        img = img[:, ::-1, :]
+        img = img[::-1, :, :]
+        img = img.transpose(1, 0, 2)
+        return img
+
+
+def augment_imgs(img_list, hflip=True, rot=True):
+    # horizontal flip OR rotate
+    hflip = hflip and random.random() < 0.5
+    vflip = rot and random.random() < 0.5
+    rot90 = rot and random.random() < 0.5
+
+    def _augment(img):
+        if hflip:
+            img = img[:, ::-1, :]
+        if vflip:
+            img = img[::-1, :, :]
+        if rot90:
+            img = img.transpose(1, 0, 2)
+        return img
+
+    return [_augment(img) for img in img_list]
+
+
+'''
+# --------------------------------------------
+# modcrop and shave
+# --------------------------------------------
+'''
+
+
+def modcrop(img_in, scale):
+    # img_in: Numpy, HWC or HW
+    img = np.copy(img_in)
+    if img.ndim == 2:
+        H, W = img.shape
+        H_r, W_r = H % scale, W % scale
+        img = img[:H - H_r, :W - W_r]
+    elif img.ndim == 3:
+        H, W, C = img.shape
+        H_r, W_r = H % scale, W % scale
+        img = img[:H - H_r, :W - W_r, :]
+    else:
+        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
+    return img
+
+
+def shave(img_in, border=0):
+    # img_in: Numpy, HWC or HW
+    img = np.copy(img_in)
+    h, w = img.shape[:2]
+    img = img[border:h-border, border:w-border]
+    return img
+
+
+'''
+# --------------------------------------------
+# image processing process on numpy image
+# channel_convert(in_c, tar_type, img_list):
+# rgb2ycbcr(img, only_y=True):
+# bgr2ycbcr(img, only_y=True):
+# ycbcr2rgb(img):
+# --------------------------------------------
+'''
+
+
+def rgb2ycbcr(img, only_y=True):
+    '''same as matlab rgb2ycbcr
+    only_y: only return Y channel
+    Input:
+        uint8, [0, 255]
+        float, [0, 1]
+    '''
+    in_img_type = img.dtype
+    img.astype(np.float32)
+    if in_img_type != np.uint8:
+        img *= 255.
+    # convert
+    if only_y:
+        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
+    else:
+        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
+                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
+    if in_img_type == np.uint8:
+        rlt = rlt.round()
+    else:
+        rlt /= 255.
+    return rlt.astype(in_img_type)
+
+
+def ycbcr2rgb(img):
+    '''same as matlab ycbcr2rgb
+    Input:
+        uint8, [0, 255]
+        float, [0, 1]
+    '''
+    in_img_type = img.dtype
+    img.astype(np.float32)
+    if in_img_type != np.uint8:
+        img *= 255.
+    # convert
+    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
+                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
+    if in_img_type == np.uint8:
+        rlt = rlt.round()
+    else:
+        rlt /= 255.
+    return rlt.astype(in_img_type)
+
+
+def bgr2ycbcr(img, only_y=True):
+    '''bgr version of rgb2ycbcr
+    only_y: only return Y channel
+    Input:
+        uint8, [0, 255]
+        float, [0, 1]
+    '''
+    in_img_type = img.dtype
+    img.astype(np.float32)
+    if in_img_type != np.uint8:
+        img *= 255.
+    # convert
+    if only_y:
+        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
+    else:
+        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
+                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
+    if in_img_type == np.uint8:
+        rlt = rlt.round()
+    else:
+        rlt /= 255.
+    return rlt.astype(in_img_type)
+
+
+def channel_convert(in_c, tar_type, img_list):
+    # conversion among BGR, gray and y
+    if in_c == 3 and tar_type == 'gray':  # BGR to gray
+        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
+        return [np.expand_dims(img, axis=2) for img in gray_list]
+    elif in_c == 3 and tar_type == 'y':  # BGR to y
+        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
+        return [np.expand_dims(img, axis=2) for img in y_list]
+    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
+        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
+    else:
+        return img_list
+
+
+'''
+# --------------------------------------------
+# metric, PSNR and SSIM
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# PSNR
+# --------------------------------------------
+def calculate_psnr(img1, img2, border=0):
+    # img1 and img2 have range [0, 255]
+    #img1 = img1.squeeze()
+    #img2 = img2.squeeze()
+    if not img1.shape == img2.shape:
+        raise ValueError('Input images must have the same dimensions.')
+    h, w = img1.shape[:2]
+    img1 = img1[border:h-border, border:w-border]
+    img2 = img2[border:h-border, border:w-border]
+
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+    mse = np.mean((img1 - img2)**2)
+    if mse == 0:
+        return float('inf')
+    return 20 * math.log10(255.0 / math.sqrt(mse))
+
+
+# --------------------------------------------
+# SSIM
+# --------------------------------------------
+def calculate_ssim(img1, img2, border=0):
+    '''calculate SSIM
+    the same outputs as MATLAB's
+    img1, img2: [0, 255]
+    '''
+    #img1 = img1.squeeze()
+    #img2 = img2.squeeze()
+    if not img1.shape == img2.shape:
+        raise ValueError('Input images must have the same dimensions.')
+    h, w = img1.shape[:2]
+    img1 = img1[border:h-border, border:w-border]
+    img2 = img2[border:h-border, border:w-border]
+
+    if img1.ndim == 2:
+        return ssim(img1, img2)
+    elif img1.ndim == 3:
+        if img1.shape[2] == 3:
+            ssims = []
+            for i in range(3):
+                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
+            return np.array(ssims).mean()
+        elif img1.shape[2] == 1:
+            return ssim(np.squeeze(img1), np.squeeze(img2))
+    else:
+        raise ValueError('Wrong input image dimensions.')
+
+
+def ssim(img1, img2):
+    C1 = (0.01 * 255)**2
+    C2 = (0.03 * 255)**2
+
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+    kernel = cv2.getGaussianKernel(11, 1.5)
+    window = np.outer(kernel, kernel.transpose())
+
+    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
+    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+    mu1_sq = mu1**2
+    mu2_sq = mu2**2
+    mu1_mu2 = mu1 * mu2
+    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
+    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
+    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
+                                                            (sigma1_sq + sigma2_sq + C2))
+    return ssim_map.mean()
+
+
+'''
+# --------------------------------------------
+# matlab's bicubic imresize (numpy and torch) [0, 1]
+# --------------------------------------------
+'''
+
+
+# matlab 'imresize' function, now only support 'bicubic'
+def cubic(x):
+    absx = torch.abs(x)
+    absx2 = absx**2
+    absx3 = absx**3
+    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
+        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
+
+
+def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+    if (scale < 1) and (antialiasing):
+        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
+        kernel_width = kernel_width / scale
+
+    # Output-space coordinates
+    x = torch.linspace(1, out_length, out_length)
+
+    # Input-space coordinates. Calculate the inverse mapping such that 0.5
+    # in output space maps to 0.5 in input space, and 0.5+scale in output
+    # space maps to 1.5 in input space.
+    u = x / scale + 0.5 * (1 - 1 / scale)
+
+    # What is the left-most pixel that can be involved in the computation?
+    left = torch.floor(u - kernel_width / 2)
+
+    # What is the maximum number of pixels that can be involved in the
+    # computation?  Note: it's OK to use an extra pixel here; if the
+    # corresponding weights are all zero, it will be eliminated at the end
+    # of this function.
+    P = math.ceil(kernel_width) + 2
+
+    # The indices of the input pixels involved in computing the k-th output
+    # pixel are in row k of the indices matrix.
+    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
+        1, P).expand(out_length, P)
+
+    # The weights used to compute the k-th output pixel are in row k of the
+    # weights matrix.
+    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
+    # apply cubic kernel
+    if (scale < 1) and (antialiasing):
+        weights = scale * cubic(distance_to_center * scale)
+    else:
+        weights = cubic(distance_to_center)
+    # Normalize the weights matrix so that each row sums to 1.
+    weights_sum = torch.sum(weights, 1).view(out_length, 1)
+    weights = weights / weights_sum.expand(out_length, P)
+
+    # If a column in weights is all zero, get rid of it. only consider the first and last column.
+    weights_zero_tmp = torch.sum((weights == 0), 0)
+    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
+        indices = indices.narrow(1, 1, P - 2)
+        weights = weights.narrow(1, 1, P - 2)
+    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
+        indices = indices.narrow(1, 0, P - 2)
+        weights = weights.narrow(1, 0, P - 2)
+    weights = weights.contiguous()
+    indices = indices.contiguous()
+    sym_len_s = -indices.min() + 1
+    sym_len_e = indices.max() - in_length
+    indices = indices + sym_len_s - 1
+    return weights, indices, int(sym_len_s), int(sym_len_e)
+
+
+# --------------------------------------------
+# imresize for tensor image [0, 1]
+# --------------------------------------------
+def imresize(img, scale, antialiasing=True):
+    # Now the scale should be the same for H and W
+    # input: img: pytorch tensor, CHW or HW [0,1]
+    # output: CHW or HW [0,1] w/o round
+    need_squeeze = True if img.dim() == 2 else False
+    if need_squeeze:
+        img.unsqueeze_(0)
+    in_C, in_H, in_W = img.size()
+    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+    kernel_width = 4
+    kernel = 'cubic'
+
+    # Return the desired dimension order for performing the resize.  The
+    # strategy is to perform the resize first along the dimension with the
+    # smallest scale factor.
+    # Now we do not support this.
+
+    # get weights and indices
+    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+        in_H, out_H, scale, kernel, kernel_width, antialiasing)
+    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+        in_W, out_W, scale, kernel, kernel_width, antialiasing)
+    # process H dimension
+    # symmetric copying
+    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
+    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
+
+    sym_patch = img[:, :sym_len_Hs, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+    sym_patch = img[:, -sym_len_He:, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+    out_1 = torch.FloatTensor(in_C, out_H, in_W)
+    kernel_width = weights_H.size(1)
+    for i in range(out_H):
+        idx = int(indices_H[i][0])
+        for j in range(out_C):
+            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
+
+    # process W dimension
+    # symmetric copying
+    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
+    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
+
+    sym_patch = out_1[:, :, :sym_len_Ws]
+    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(2, inv_idx)
+    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+    sym_patch = out_1[:, :, -sym_len_We:]
+    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(2, inv_idx)
+    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+    out_2 = torch.FloatTensor(in_C, out_H, out_W)
+    kernel_width = weights_W.size(1)
+    for i in range(out_W):
+        idx = int(indices_W[i][0])
+        for j in range(out_C):
+            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
+    if need_squeeze:
+        out_2.squeeze_()
+    return out_2
+
+
+# --------------------------------------------
+# imresize for numpy image [0, 1]
+# --------------------------------------------
+def imresize_np(img, scale, antialiasing=True):
+    # Now the scale should be the same for H and W
+    # input: img: Numpy, HWC or HW [0,1]
+    # output: HWC or HW [0,1] w/o round
+    img = torch.from_numpy(img)
+    need_squeeze = True if img.dim() == 2 else False
+    if need_squeeze:
+        img.unsqueeze_(2)
+
+    in_H, in_W, in_C = img.size()
+    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+    kernel_width = 4
+    kernel = 'cubic'
+
+    # Return the desired dimension order for performing the resize.  The
+    # strategy is to perform the resize first along the dimension with the
+    # smallest scale factor.
+    # Now we do not support this.
+
+    # get weights and indices
+    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+        in_H, out_H, scale, kernel, kernel_width, antialiasing)
+    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+        in_W, out_W, scale, kernel, kernel_width, antialiasing)
+    # process H dimension
+    # symmetric copying
+    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
+    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
+
+    sym_patch = img[:sym_len_Hs, :, :]
+    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(0, inv_idx)
+    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+    sym_patch = img[-sym_len_He:, :, :]
+    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(0, inv_idx)
+    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+    out_1 = torch.FloatTensor(out_H, in_W, in_C)
+    kernel_width = weights_H.size(1)
+    for i in range(out_H):
+        idx = int(indices_H[i][0])
+        for j in range(out_C):
+            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
+
+    # process W dimension
+    # symmetric copying
+    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
+    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
+
+    sym_patch = out_1[:, :sym_len_Ws, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+    sym_patch = out_1[:, -sym_len_We:, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+    out_2 = torch.FloatTensor(out_H, out_W, in_C)
+    kernel_width = weights_W.size(1)
+    for i in range(out_W):
+        idx = int(indices_W[i][0])
+        for j in range(out_C):
+            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
+    if need_squeeze:
+        out_2.squeeze_()
+
+    return out_2.numpy()
+
+
+if __name__ == '__main__':
+    print('---')
+#    img = imread_uint('test.bmp', 3)
+#    img = uint2single(img)
+#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/ldm/modules/midas/__init__.py b/ldm/modules/midas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/midas/api.py b/ldm/modules/midas/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b58ebbffd942a2fc22264f0ab47e400c26b9f41c
--- /dev/null
+++ b/ldm/modules/midas/api.py
@@ -0,0 +1,170 @@
+# based on https://github.com/isl-org/MiDaS
+
+import cv2
+import torch
+import torch.nn as nn
+from torchvision.transforms import Compose
+
+from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
+from ldm.modules.midas.midas.midas_net import MidasNet
+from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
+from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
+
+
+ISL_PATHS = {
+    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
+    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
+    "midas_v21": "",
+    "midas_v21_small": "",
+}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def load_midas_transform(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load transform only
+    if model_type == "dpt_large":  # DPT-Large
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    elif model_type == "midas_v21_small":
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    else:
+        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return transform
+
+
+def load_model(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load network
+    model_path = ISL_PATHS[model_type]
+    if model_type == "dpt_large":  # DPT-Large
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitl16_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitb_rn50_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        model = MidasNet(model_path, non_negative=True)
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    elif model_type == "midas_v21_small":
+        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
+                               non_negative=True, blocks={'expand': True})
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    else:
+        print(f"model_type '{model_type}' not implemented, use: --model_type large")
+        assert False
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return model.eval(), transform
+
+
+class MiDaSInference(nn.Module):
+    MODEL_TYPES_TORCH_HUB = [
+        "DPT_Large",
+        "DPT_Hybrid",
+        "MiDaS_small"
+    ]
+    MODEL_TYPES_ISL = [
+        "dpt_large",
+        "dpt_hybrid",
+        "midas_v21",
+        "midas_v21_small",
+    ]
+
+    def __init__(self, model_type):
+        super().__init__()
+        assert (model_type in self.MODEL_TYPES_ISL)
+        model, _ = load_model(model_type)
+        self.model = model
+        self.model.train = disabled_train
+
+    def forward(self, x):
+        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
+        # NOTE: we expect that the correct transform has been called during dataloading.
+        with torch.no_grad():
+            prediction = self.model(x)
+            prediction = torch.nn.functional.interpolate(
+                prediction.unsqueeze(1),
+                size=x.shape[2:],
+                mode="bicubic",
+                align_corners=False,
+            )
+        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
+        return prediction
+
diff --git a/ldm/modules/midas/midas/__init__.py b/ldm/modules/midas/midas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/midas/midas/base_model.py b/ldm/modules/midas/midas/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cf430239b47ec5ec07531263f26f5c24a2311cd
--- /dev/null
+++ b/ldm/modules/midas/midas/base_model.py
@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+    def load(self, path):
+        """Load model from file.
+
+        Args:
+            path (str): file path
+        """
+        parameters = torch.load(path, map_location=torch.device('cpu'))
+
+        if "optimizer" in parameters:
+            parameters = parameters["model"]
+
+        self.load_state_dict(parameters)
diff --git a/ldm/modules/midas/midas/blocks.py b/ldm/modules/midas/midas/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..2145d18fa98060a618536d9a64fe6589e9be4f78
--- /dev/null
+++ b/ldm/modules/midas/midas/blocks.py
@@ -0,0 +1,342 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+    _make_pretrained_vitb_rn50_384,
+    _make_pretrained_vitl16_384,
+    _make_pretrained_vitb16_384,
+    forward_vit,
+)
+
+def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
+    if backbone == "vitl16_384":
+        pretrained = _make_pretrained_vitl16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [256, 512, 1024, 1024], features, groups=groups, expand=expand
+        )  # ViT-L/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb_rn50_384":
+        pretrained = _make_pretrained_vitb_rn50_384(
+            use_pretrained,
+            hooks=hooks,
+            use_vit_only=use_vit_only,
+            use_readout=use_readout,
+        )
+        scratch = _make_scratch(
+            [256, 512, 768, 768], features, groups=groups, expand=expand
+        )  # ViT-H/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb16_384":
+        pretrained = _make_pretrained_vitb16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )  # ViT-B/16 - 84.6% Top1 (backbone)
+    elif backbone == "resnext101_wsl":
+        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
+    elif backbone == "efficientnet_lite3":
+        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
+        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
+    else:
+        print(f"Backbone '{backbone}' not implemented")
+        assert False
+        
+    return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand==True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape*2
+        out_shape3 = out_shape*4
+        out_shape4 = out_shape*8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+
+    return scratch
+
+
+def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
+    efficientnet = torch.hub.load(
+        "rwightman/gen-efficientnet-pytorch",
+        "tf_efficientnet_lite3",
+        pretrained=use_pretrained,
+        exportable=exportable
+    )
+    return _make_efficientnet_backbone(efficientnet)
+
+
+def _make_efficientnet_backbone(effnet):
+    pretrained = nn.Module()
+
+    pretrained.layer1 = nn.Sequential(
+        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
+    )
+    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
+    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
+    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
+
+    return pretrained
+    
+
+def _make_resnet_backbone(resnet):
+    pretrained = nn.Module()
+    pretrained.layer1 = nn.Sequential(
+        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+    )
+
+    pretrained.layer2 = resnet.layer2
+    pretrained.layer3 = resnet.layer3
+    pretrained.layer4 = resnet.layer4
+
+    return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+    return _make_resnet_backbone(resnet)
+
+
+
+class Interpolate(nn.Module):
+    """Interpolation module.
+    """
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
+        )
+
+        return x
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        out = self.relu(x)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.resConfUnit1 = ResidualConvUnit(features)
+        self.resConfUnit2 = ResidualConvUnit(features)
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            output += self.resConfUnit1(xs[1])
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=True
+        )
+
+        return output
+
+
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+        
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+
+        if self.bn==True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn==True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn==True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+        # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+
diff --git a/ldm/modules/midas/midas/dpt_depth.py b/ldm/modules/midas/midas/dpt_depth.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e9aab5d2767dffea39da5b3f30e2798688216f1
--- /dev/null
+++ b/ldm/modules/midas/midas/dpt_depth.py
@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+    FeatureFusionBlock,
+    FeatureFusionBlock_custom,
+    Interpolate,
+    _make_encoder,
+    forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+    )
+
+
+class DPT(BaseModel):
+    def __init__(
+        self,
+        head,
+        features=256,
+        backbone="vitb_rn50_384",
+        readout="project",
+        channels_last=False,
+        use_bn=False,
+    ):
+
+        super(DPT, self).__init__()
+
+        self.channels_last = channels_last
+
+        hooks = {
+            "vitb_rn50_384": [0, 1, 8, 11],
+            "vitb16_384": [2, 5, 8, 11],
+            "vitl16_384": [5, 11, 17, 23],
+        }
+
+        # Instantiate backbone and reassemble blocks
+        self.pretrained, self.scratch = _make_encoder(
+            backbone,
+            features,
+            False, # Set to true of you want to train from scratch, uses ImageNet weights
+            groups=1,
+            expand=False,
+            exportable=False,
+            hooks=hooks[backbone],
+            use_readout=readout,
+        )
+
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        self.scratch.output_conv = head
+
+
+    def forward(self, x):
+        if self.channels_last == True:
+            x.contiguous(memory_format=torch.channels_last)
+
+        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return out
+
+
+class DPTDepthModel(DPT):
+    def __init__(self, path=None, non_negative=True, **kwargs):
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+
+        super().__init__(head, **kwargs)
+
+        if path is not None:
+           self.load(path)
+
+    def forward(self, x):
+        return super().forward(x).squeeze(dim=1)
+
diff --git a/ldm/modules/midas/midas/midas_net.py b/ldm/modules/midas/midas/midas_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a954977800b0a0f48807e80fa63041910e33c1f
--- /dev/null
+++ b/ldm/modules/midas/midas/midas_net.py
@@ -0,0 +1,76 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet(BaseModel):
+    """Network for monocular depth estimation.
+    """
+
+    def __init__(self, path=None, features=256, non_negative=True):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet, self).__init__()
+
+        use_pretrained = False if path is None else True
+
+        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
+
+        self.scratch.refinenet4 = FeatureFusionBlock(features)
+        self.scratch.refinenet3 = FeatureFusionBlock(features)
+        self.scratch.refinenet2 = FeatureFusionBlock(features)
+        self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+        )
+
+        if path:
+            self.load(path)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)
diff --git a/ldm/modules/midas/midas/midas_net_custom.py b/ldm/modules/midas/midas/midas_net_custom.py
new file mode 100644
index 0000000000000000000000000000000000000000..50e4acb5e53d5fabefe3dde16ab49c33c2b7797c
--- /dev/null
+++ b/ldm/modules/midas/midas/midas_net_custom.py
@@ -0,0 +1,128 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
+
+
+class MidasNet_small(BaseModel):
+    """Network for monocular depth estimation.
+    """
+
+    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
+        blocks={'expand': True}):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet_small, self).__init__()
+
+        use_pretrained = False if path else True
+                
+        self.channels_last = channels_last
+        self.blocks = blocks
+        self.backbone = backbone
+
+        self.groups = 1
+
+        features1=features
+        features2=features
+        features3=features
+        features4=features
+        self.expand = False
+        if "expand" in self.blocks and self.blocks['expand'] == True:
+            self.expand = True
+            features1=features
+            features2=features*2
+            features3=features*4
+            features4=features*8
+
+        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
+  
+        self.scratch.activation = nn.ReLU(False)    
+
+        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
+
+        
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
+            self.scratch.activation,
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+        
+        if path:
+            self.load(path)
+
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+        if self.channels_last==True:
+            print("self.channels_last = ", self.channels_last)
+            x.contiguous(memory_format=torch.channels_last)
+
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)
+
+
+
+def fuse_model(m):
+    prev_previous_type = nn.Identity()
+    prev_previous_name = ''
+    previous_type = nn.Identity()
+    previous_name = ''
+    for name, module in m.named_modules():
+        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
+            # print("FUSED ", prev_previous_name, previous_name, name)
+            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
+        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
+            # print("FUSED ", prev_previous_name, previous_name)
+            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
+        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
+        #    print("FUSED ", previous_name, name)
+        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
+
+        prev_previous_type = previous_type
+        prev_previous_name = previous_name
+        previous_type = type(module)
+        previous_name = name
\ No newline at end of file
diff --git a/ldm/modules/midas/midas/transforms.py b/ldm/modules/midas/midas/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..350cbc11662633ad7f8968eb10be2e7de6e384e9
--- /dev/null
+++ b/ldm/modules/midas/midas/transforms.py
@@ -0,0 +1,234 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            sample["mask"] = cv2.resize(
+                sample["mask"].astype(np.float32),
+                (width, height),
+                interpolation=cv2.INTER_NEAREST,
+            )
+            sample["mask"] = sample["mask"].astype(bool)
+
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+        if "disparity" in sample:
+            disparity = sample["disparity"].astype(np.float32)
+            sample["disparity"] = np.ascontiguousarray(disparity)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+
+        return sample
diff --git a/ldm/modules/midas/midas/vit.py b/ldm/modules/midas/midas/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea46b1be88b261b0dec04f3da0256f5f66f88a74
--- /dev/null
+++ b/ldm/modules/midas/midas/vit.py
@@ -0,0 +1,491 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+class Slice(nn.Module):
+    def __init__(self, start_index=1):
+        super(Slice, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+    def __init__(self, start_index=1):
+        super(AddReadout, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        if self.start_index == 2:
+            readout = (x[:, 0] + x[:, 1]) / 2
+        else:
+            readout = x[:, 0]
+        return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+    def __init__(self, in_features, start_index=1):
+        super(ProjectReadout, self).__init__()
+        self.start_index = start_index
+
+        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+    def forward(self, x):
+        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+        features = torch.cat((x[:, self.start_index :], readout), -1)
+
+        return self.project(features)
+
+
+class Transpose(nn.Module):
+    def __init__(self, dim0, dim1):
+        super(Transpose, self).__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x):
+        x = x.transpose(self.dim0, self.dim1)
+        return x
+
+
+def forward_vit(pretrained, x):
+    b, c, h, w = x.shape
+
+    glob = pretrained.model.forward_flex(x)
+
+    layer_1 = pretrained.activations["1"]
+    layer_2 = pretrained.activations["2"]
+    layer_3 = pretrained.activations["3"]
+    layer_4 = pretrained.activations["4"]
+
+    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+    unflatten = nn.Sequential(
+        nn.Unflatten(
+            2,
+            torch.Size(
+                [
+                    h // pretrained.model.patch_size[1],
+                    w // pretrained.model.patch_size[0],
+                ]
+            ),
+        )
+    )
+
+    if layer_1.ndim == 3:
+        layer_1 = unflatten(layer_1)
+    if layer_2.ndim == 3:
+        layer_2 = unflatten(layer_2)
+    if layer_3.ndim == 3:
+        layer_3 = unflatten(layer_3)
+    if layer_4.ndim == 3:
+        layer_4 = unflatten(layer_4)
+
+    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+    return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+    posemb_tok, posemb_grid = (
+        posemb[:, : self.start_index],
+        posemb[0, self.start_index :],
+    )
+
+    gs_old = int(math.sqrt(len(posemb_grid)))
+
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+    return posemb
+
+
+def forward_flex(self, x):
+    b, c, h, w = x.shape
+
+    pos_embed = self._resize_pos_embed(
+        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+    )
+
+    B = x.shape[0]
+
+    if hasattr(self.patch_embed, "backbone"):
+        x = self.patch_embed.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+
+    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+    if getattr(self, "dist_token", None) is not None:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        dist_token = self.dist_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, dist_token, x), dim=1)
+    else:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+
+    x = x + pos_embed
+    x = self.pos_drop(x)
+
+    for blk in self.blocks:
+        x = blk(x)
+
+    x = self.norm(x)
+
+    return x
+
+
+activations = {}
+
+
+def get_activation(name):
+    def hook(model, input, output):
+        activations[name] = output
+
+    return hook
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+    if use_readout == "ignore":
+        readout_oper = [Slice(start_index)] * len(features)
+    elif use_readout == "add":
+        readout_oper = [AddReadout(start_index)] * len(features)
+    elif use_readout == "project":
+        readout_oper = [
+            ProjectReadout(vit_features, start_index) for out_feat in features
+        ]
+    else:
+        assert (
+            False
+        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+    return readout_oper
+
+
+def _make_vit_b16_backbone(
+    model,
+    features=[96, 192, 384, 768],
+    size=[384, 384],
+    hooks=[2, 5, 8, 11],
+    vit_features=768,
+    use_readout="ignore",
+    start_index=1,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    # 32, 48, 136, 384
+    pretrained.act_postprocess1 = nn.Sequential(
+        readout_oper[0],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[0],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[0],
+            out_channels=features[0],
+            kernel_size=4,
+            stride=4,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess2 = nn.Sequential(
+        readout_oper[1],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[1],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[1],
+            out_channels=features[1],
+            kernel_size=2,
+            stride=2,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+    hooks = [5, 11, 17, 23] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[256, 512, 1024, 1024],
+        hooks=hooks,
+        vit_features=1024,
+        use_readout=use_readout,
+    )
+
+
+def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+    )
+
+
+def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+    )
+
+
+def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model(
+        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+    )
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        start_index=2,
+    )
+
+
+def _make_vit_b_rn50_backbone(
+    model,
+    features=[256, 512, 768, 768],
+    size=[384, 384],
+    hooks=[0, 1, 8, 11],
+    vit_features=768,
+    use_vit_only=False,
+    use_readout="ignore",
+    start_index=1,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+
+    if use_vit_only == True:
+        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    else:
+        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+            get_activation("1")
+        )
+        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+            get_activation("2")
+        )
+
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    if use_vit_only == True:
+        pretrained.act_postprocess1 = nn.Sequential(
+            readout_oper[0],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[0],
+                out_channels=features[0],
+                kernel_size=4,
+                stride=4,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+
+        pretrained.act_postprocess2 = nn.Sequential(
+            readout_oper[1],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[1],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[1],
+                out_channels=features[1],
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+    else:
+        pretrained.act_postprocess1 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+        pretrained.act_postprocess2 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
+):
+    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+
+    hooks = [0, 1, 8, 11] if hooks == None else hooks
+    return _make_vit_b_rn50_backbone(
+        model,
+        features=[256, 512, 768, 768],
+        size=[384, 384],
+        hooks=hooks,
+        use_vit_only=use_vit_only,
+        use_readout=use_readout,
+    )
diff --git a/ldm/modules/midas/utils.py b/ldm/modules/midas/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a9d3b5b66370fa98da9e067ba53ead848ea9a59
--- /dev/null
+++ b/ldm/modules/midas/utils.py
@@ -0,0 +1,189 @@
+"""Utils for monoDepth."""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+
+
+def read_pfm(path):
+    """Read pfm file.
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        tuple: (data, scale)
+    """
+    with open(path, "rb") as file:
+
+        color = None
+        width = None
+        height = None
+        scale = None
+        endian = None
+
+        header = file.readline().rstrip()
+        if header.decode("ascii") == "PF":
+            color = True
+        elif header.decode("ascii") == "Pf":
+            color = False
+        else:
+            raise Exception("Not a PFM file: " + path)
+
+        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+        if dim_match:
+            width, height = list(map(int, dim_match.groups()))
+        else:
+            raise Exception("Malformed PFM header.")
+
+        scale = float(file.readline().decode("ascii").rstrip())
+        if scale < 0:
+            # little-endian
+            endian = "<"
+            scale = -scale
+        else:
+            # big-endian
+            endian = ">"
+
+        data = np.fromfile(file, endian + "f")
+        shape = (height, width, 3) if color else (height, width)
+
+        data = np.reshape(data, shape)
+        data = np.flipud(data)
+
+        return data, scale
+
+
+def write_pfm(path, image, scale=1):
+    """Write pfm file.
+
+    Args:
+        path (str): pathto file
+        image (array): data
+        scale (int, optional): Scale. Defaults to 1.
+    """
+
+    with open(path, "wb") as file:
+        color = None
+
+        if image.dtype.name != "float32":
+            raise Exception("Image dtype must be float32.")
+
+        image = np.flipud(image)
+
+        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
+            color = True
+        elif (
+            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+        ):  # greyscale
+            color = False
+        else:
+            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+        file.write("PF\n" if color else "Pf\n".encode())
+        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+        endian = image.dtype.byteorder
+
+        if endian == "<" or endian == "=" and sys.byteorder == "little":
+            scale = -scale
+
+        file.write("%f\n".encode() % scale)
+
+        image.tofile(file)
+
+
+def read_image(path):
+    """Read image and output RGB image (0-1).
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        array: RGB image (0-1)
+    """
+    img = cv2.imread(path)
+
+    if img.ndim == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+
+    return img
+
+
+def resize_image(img):
+    """Resize image and make it fit for network.
+
+    Args:
+        img (array): image
+
+    Returns:
+        tensor: data ready for network
+    """
+    height_orig = img.shape[0]
+    width_orig = img.shape[1]
+
+    if width_orig > height_orig:
+        scale = width_orig / 384
+    else:
+        scale = height_orig / 384
+
+    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+
+    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+
+    img_resized = (
+        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+    )
+    img_resized = img_resized.unsqueeze(0)
+
+    return img_resized
+
+
+def resize_depth(depth, width, height):
+    """Resize depth map and bring to CPU (numpy).
+
+    Args:
+        depth (tensor): depth
+        width (int): image width
+        height (int): image height
+
+    Returns:
+        array: processed depth
+    """
+    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+
+    depth_resized = cv2.resize(
+        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+    )
+
+    return depth_resized
+
+def write_depth(path, depth, bits=1):
+    """Write depth map to pfm and png file.
+
+    Args:
+        path (str): filepath without extension
+        depth (array): depth
+    """
+    write_pfm(path + ".pfm", depth.astype(np.float32))
+
+    depth_min = depth.min()
+    depth_max = depth.max()
+
+    max_val = (2**(8*bits))-1
+
+    if depth_max - depth_min > np.finfo("float").eps:
+        out = max_val * (depth - depth_min) / (depth_max - depth_min)
+    else:
+        out = np.zeros(depth.shape, dtype=depth.type)
+
+    if bits == 1:
+        cv2.imwrite(path + ".png", out.astype("uint8"))
+    elif bits == 2:
+        cv2.imwrite(path + ".png", out.astype("uint16"))
+
+    return
diff --git a/ldm/modules/x_transformer.py b/ldm/modules/x_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fc15bf9cfe0111a910e7de33d04ffdec3877576
--- /dev/null
+++ b/ldm/modules/x_transformer.py
@@ -0,0 +1,641 @@
+"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from functools import partial
+from inspect import isfunction
+from collections import namedtuple
+from einops import rearrange, repeat, reduce
+
+# constants
+
+DEFAULT_DIM_HEAD = 64
+
+Intermediates = namedtuple('Intermediates', [
+    'pre_softmax_attn',
+    'post_softmax_attn'
+])
+
+LayerIntermediates = namedtuple('Intermediates', [
+    'hiddens',
+    'attn_intermediates'
+])
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.emb = nn.Embedding(max_seq_len, dim)
+        self.init_()
+
+    def init_(self):
+        nn.init.normal_(self.emb.weight, std=0.02)
+
+    def forward(self, x):
+        n = torch.arange(x.shape[1], device=x.device)
+        return self.emb(n)[None, :, :]
+
+
+class FixedPositionalEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, x, seq_dim=1, offset=0):
+        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
+        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
+        return emb[None, :, :]
+
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def always(val):
+    def inner(*args, **kwargs):
+        return val
+    return inner
+
+
+def not_equals(val):
+    def inner(x):
+        return x != val
+    return inner
+
+
+def equals(val):
+    def inner(x):
+        return x == val
+    return inner
+
+
+def max_neg_value(tensor):
+    return -torch.finfo(tensor.dtype).max
+
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(), dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
+
+
+# classes
+class Scale(nn.Module):
+    def __init__(self, value, fn):
+        super().__init__()
+        self.value = value
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.value, *rest)
+
+
+class Rezero(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, **kwargs):
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.g, *rest)
+
+
+class ScaleNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-8):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class Residual(nn.Module):
+    def forward(self, x, residual):
+        return x + residual
+
+
+class GRUGating(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gru = nn.GRUCell(dim, dim)
+
+    def forward(self, x, residual):
+        gated_output = self.gru(
+            rearrange(x, 'b n d -> (b n) d'),
+            rearrange(residual, 'b n d -> (b n) d')
+        )
+
+        return gated_output.reshape_as(x)
+
+
+# feedforward
+
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+# attention.
+class Attention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_head=DEFAULT_DIM_HEAD,
+            heads=8,
+            causal=False,
+            mask=None,
+            talking_heads=False,
+            sparse_topk=None,
+            use_entmax15=False,
+            num_mem_kv=0,
+            dropout=0.,
+            on_attn=False
+    ):
+        super().__init__()
+        if use_entmax15:
+            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+        self.causal = causal
+        self.mask = mask
+
+        inner_dim = dim_head * heads
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(dim, inner_dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+        # talking heads
+        self.talking_heads = talking_heads
+        if talking_heads:
+            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+
+        # explicit topk sparse attention
+        self.sparse_topk = sparse_topk
+
+        # entmax
+        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+        self.attn_fn = F.softmax
+
+        # add memory key / values
+        self.num_mem_kv = num_mem_kv
+        if num_mem_kv > 0:
+            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+
+        # attention on attention
+        self.attn_on_attn = on_attn
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            rel_pos=None,
+            sinusoidal_emb=None,
+            prev_attn=None,
+            mem=None
+    ):
+        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+        kv_input = default(context, x)
+
+        q_input = x
+        k_input = kv_input
+        v_input = kv_input
+
+        if exists(mem):
+            k_input = torch.cat((mem, k_input), dim=-2)
+            v_input = torch.cat((mem, v_input), dim=-2)
+
+        if exists(sinusoidal_emb):
+            # in shortformer, the query would start at a position offset depending on the past cached memory
+            offset = k_input.shape[-2] - q_input.shape[-2]
+            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
+            k_input = k_input + sinusoidal_emb(k_input)
+
+        q = self.to_q(q_input)
+        k = self.to_k(k_input)
+        v = self.to_v(v_input)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+
+        input_mask = None
+        if any(map(exists, (mask, context_mask))):
+            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+            k_mask = q_mask if not exists(context) else context_mask
+            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+            q_mask = rearrange(q_mask, 'b i -> b () i ()')
+            k_mask = rearrange(k_mask, 'b j -> b () () j')
+            input_mask = q_mask * k_mask
+
+        if self.num_mem_kv > 0:
+            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+            k = torch.cat((mem_k, k), dim=-2)
+            v = torch.cat((mem_v, v), dim=-2)
+            if exists(input_mask):
+                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+        mask_value = max_neg_value(dots)
+
+        if exists(prev_attn):
+            dots = dots + prev_attn
+
+        pre_softmax_attn = dots
+
+        if talking_heads:
+            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+
+        if exists(rel_pos):
+            dots = rel_pos(dots)
+
+        if exists(input_mask):
+            dots.masked_fill_(~input_mask, mask_value)
+            del input_mask
+
+        if self.causal:
+            i, j = dots.shape[-2:]
+            r = torch.arange(i, device=device)
+            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+            mask = F.pad(mask, (j - i, 0), value=False)
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
+            top, _ = dots.topk(self.sparse_topk, dim=-1)
+            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
+            mask = dots < vk
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        attn = self.attn_fn(dots, dim=-1)
+        post_softmax_attn = attn
+
+        attn = self.dropout(attn)
+
+        if talking_heads:
+            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+
+        intermediates = Intermediates(
+            pre_softmax_attn=pre_softmax_attn,
+            post_softmax_attn=post_softmax_attn
+        )
+
+        return self.to_out(out), intermediates
+
+
+class AttentionLayers(nn.Module):
+    def __init__(
+            self,
+            dim,
+            depth,
+            heads=8,
+            causal=False,
+            cross_attend=False,
+            only_cross=False,
+            use_scalenorm=False,
+            use_rmsnorm=False,
+            use_rezero=False,
+            rel_pos_num_buckets=32,
+            rel_pos_max_distance=128,
+            position_infused_attn=False,
+            custom_layers=None,
+            sandwich_coef=None,
+            par_ratio=None,
+            residual_attn=False,
+            cross_residual_attn=False,
+            macaron=False,
+            pre_norm=True,
+            gate_residual=False,
+            **kwargs
+    ):
+        super().__init__()
+        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
+        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
+
+        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
+
+        self.dim = dim
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        self.has_pos_emb = position_infused_attn
+        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+        self.rotary_pos_emb = always(None)
+
+        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+        self.rel_pos = None
+
+        self.pre_norm = pre_norm
+
+        self.residual_attn = residual_attn
+        self.cross_residual_attn = cross_residual_attn
+
+        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
+        norm_class = RMSNorm if use_rmsnorm else norm_class
+        norm_fn = partial(norm_class, dim)
+
+        norm_fn = nn.Identity if use_rezero else norm_fn
+        branch_fn = Rezero if use_rezero else None
+
+        if cross_attend and not only_cross:
+            default_block = ('a', 'c', 'f')
+        elif cross_attend and only_cross:
+            default_block = ('c', 'f')
+        else:
+            default_block = ('a', 'f')
+
+        if macaron:
+            default_block = ('f',) + default_block
+
+        if exists(custom_layers):
+            layer_types = custom_layers
+        elif exists(par_ratio):
+            par_depth = depth * len(default_block)
+            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
+            default_block = tuple(filter(not_equals('f'), default_block))
+            par_attn = par_depth // par_ratio
+            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
+            par_width = (depth_cut + depth_cut // par_attn) // par_attn
+            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
+            par_block = default_block + ('f',) * (par_width - len(default_block))
+            par_head = par_block * par_attn
+            layer_types = par_head + ('f',) * (par_depth - len(par_head))
+        elif exists(sandwich_coef):
+            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
+            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+        else:
+            layer_types = default_block * depth
+
+        self.layer_types = layer_types
+        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
+
+        for layer_type in self.layer_types:
+            if layer_type == 'a':
+                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+            elif layer_type == 'c':
+                layer = Attention(dim, heads=heads, **attn_kwargs)
+            elif layer_type == 'f':
+                layer = FeedForward(dim, **ff_kwargs)
+                layer = layer if not macaron else Scale(0.5, layer)
+            else:
+                raise Exception(f'invalid layer type {layer_type}')
+
+            if isinstance(layer, Attention) and exists(branch_fn):
+                layer = branch_fn(layer)
+
+            if gate_residual:
+                residual_fn = GRUGating(dim)
+            else:
+                residual_fn = Residual()
+
+            self.layers.append(nn.ModuleList([
+                norm_fn(),
+                layer,
+                residual_fn
+            ]))
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            mems=None,
+            return_hiddens=False
+    ):
+        hiddens = []
+        intermediates = []
+        prev_attn = None
+        prev_cross_attn = None
+
+        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+
+        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+            is_last = ind == (len(self.layers) - 1)
+
+            if layer_type == 'a':
+                hiddens.append(x)
+                layer_mem = mems.pop(0)
+
+            residual = x
+
+            if self.pre_norm:
+                x = norm(x)
+
+            if layer_type == 'a':
+                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
+                                   prev_attn=prev_attn, mem=layer_mem)
+            elif layer_type == 'c':
+                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+            elif layer_type == 'f':
+                out = block(x)
+
+            x = residual_fn(out, residual)
+
+            if layer_type in ('a', 'c'):
+                intermediates.append(inter)
+
+            if layer_type == 'a' and self.residual_attn:
+                prev_attn = inter.pre_softmax_attn
+            elif layer_type == 'c' and self.cross_residual_attn:
+                prev_cross_attn = inter.pre_softmax_attn
+
+            if not self.pre_norm and not is_last:
+                x = norm(x)
+
+        if return_hiddens:
+            intermediates = LayerIntermediates(
+                hiddens=hiddens,
+                attn_intermediates=intermediates
+            )
+
+            return x, intermediates
+
+        return x
+
+
+class Encoder(AttentionLayers):
+    def __init__(self, **kwargs):
+        assert 'causal' not in kwargs, 'cannot set causality on encoder'
+        super().__init__(causal=False, **kwargs)
+
+
+
+class TransformerWrapper(nn.Module):
+    def __init__(
+            self,
+            *,
+            num_tokens,
+            max_seq_len,
+            attn_layers,
+            emb_dim=None,
+            max_mem_len=0.,
+            emb_dropout=0.,
+            num_memory_tokens=None,
+            tie_embedding=False,
+            use_pos_emb=True
+    ):
+        super().__init__()
+        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+        dim = attn_layers.dim
+        emb_dim = default(emb_dim, dim)
+
+        self.max_seq_len = max_seq_len
+        self.max_mem_len = max_mem_len
+        self.num_tokens = num_tokens
+
+        self.token_emb = nn.Embedding(num_tokens, emb_dim)
+        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
+                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+        self.emb_dropout = nn.Dropout(emb_dropout)
+
+        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+        self.attn_layers = attn_layers
+        self.norm = nn.LayerNorm(dim)
+
+        self.init_()
+
+        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+
+        # memory tokens (like [cls]) from Memory Transformers paper
+        num_memory_tokens = default(num_memory_tokens, 0)
+        self.num_memory_tokens = num_memory_tokens
+        if num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+            # let funnel encoder know number of memory tokens, if specified
+            if hasattr(attn_layers, 'num_memory_tokens'):
+                attn_layers.num_memory_tokens = num_memory_tokens
+
+    def init_(self):
+        nn.init.normal_(self.token_emb.weight, std=0.02)
+
+    def forward(
+            self,
+            x,
+            return_embeddings=False,
+            mask=None,
+            return_mems=False,
+            return_attn=False,
+            mems=None,
+            **kwargs
+    ):
+        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
+        x = self.token_emb(x)
+        x += self.pos_emb(x)
+        x = self.emb_dropout(x)
+
+        x = self.project_emb(x)
+
+        if num_mem > 0:
+            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
+            x = torch.cat((mem, x), dim=1)
+
+            # auto-handle masking after appending memory tokens
+            if exists(mask):
+                mask = F.pad(mask, (num_mem, 0), value=True)
+
+        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+        x = self.norm(x)
+
+        mem, x = x[:, :num_mem], x[:, num_mem:]
+
+        out = self.to_logits(x) if not return_embeddings else x
+
+        if return_mems:
+            hiddens = intermediates.hiddens
+            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
+            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+            return out, new_mems
+
+        if return_attn:
+            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            return out, attn_maps
+
+        return out
+
diff --git a/ldm/test_sd_decoder.py b/ldm/test_sd_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ff4485b0bd1b0242ea3fc0952f09aeeb9b26eff
--- /dev/null
+++ b/ldm/test_sd_decoder.py
@@ -0,0 +1,29 @@
+import numpy as np
+import torch
+
+from modules.diffusionmodules.model import SimpleDecoder, Decoder
+
+# decoder = SimpleDecoder(4,32)
+# https://github.com/CompVis/stable-diffusion/blob/main/configs/autoencoder/autoencoder_kl_32x32x4.yaml
+decoder = Decoder(
+    ch=64,
+    out_ch=32,
+    ch_mult=(1, 2),
+    num_res_blocks=2,
+    # num_res_blocks=1,
+    dropout=0.0,
+    attn_resolutions=(),
+    z_channels=4,
+    resolution=64,
+    in_channels=3,
+).cuda()
+
+input_tensor = torch.randn(1,4,32,32,).cuda()
+# input_tensor = torch.randn(
+#     1,
+#     96,
+#     32,
+#     32,
+# )
+
+print(decoder(input_tensor).shape)
diff --git a/ldm/util.py b/ldm/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..66d9b2a69db2898323cbf2ad26a09ac8b2facd11
--- /dev/null
+++ b/ldm/util.py
@@ -0,0 +1,275 @@
+import functools
+import importlib
+import os
+from functools import partial
+from inspect import isfunction
+
+import fsspec
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from safetensors.torch import load_file as load_safetensors
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def get_string_from_tuple(s):
+    try:
+        # Check if the string starts and ends with parentheses
+        if s[0] == "(" and s[-1] == ")":
+            # Convert the string to a tuple
+            t = eval(s)
+            # Check if the type of t is tuple
+            if type(t) == tuple:
+                return t[0]
+            else:
+                pass
+    except:
+        pass
+    return s
+
+
+def is_power_of_two(n):
+    """
+    chat.openai.com/chat
+    Return True if n is a power of 2, otherwise return False.
+
+    The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
+    The function works by first checking if n is less than or equal to 0. If n is less than or equal to 0, it can't be a power of 2, so the function returns False.
+    If n is greater than 0, the function checks whether n is a power of 2 by using a bitwise AND operation between n and n-1. If n is a power of 2, then it will have only one bit set to 1 in its binary representation. When we subtract 1 from a power of 2, all the bits to the right of that bit become 1, and the bit itself becomes 0. So, when we perform a bitwise AND between n and n-1, we get 0 if n is a power of 2, and a non-zero value otherwise.
+    Thus, if the result of the bitwise AND operation is 0, then n is a power of 2 and the function returns True. Otherwise, the function returns False.
+
+    """
+    if n <= 0:
+        return False
+    return (n & (n - 1)) == 0
+
+
+def autocast(f, enabled=True):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(
+            enabled=enabled,
+            dtype=torch.get_autocast_gpu_dtype(),
+            cache_enabled=torch.is_autocast_cache_enabled(),
+        ):
+            return f(*args, **kwargs)
+
+    return do_autocast
+
+
+def load_partial_from_config(config):
+    return partial(get_obj_from_str(config["target"]), **config.get("params", dict()))
+
+
+def log_txt_as_img(wh, xc, size=10):
+    # wh a tuple of (width, height)
+    # xc a list of captions to plot
+    b = len(xc)
+    txts = list()
+    for bi in range(b):
+        txt = Image.new("RGB", wh, color="white")
+        draw = ImageDraw.Draw(txt)
+        font = ImageFont.truetype("data/DejaVuSans.ttf", size=size)
+        nc = int(40 * (wh[0] / 256))
+        if isinstance(xc[bi], list):
+            text_seq = xc[bi][0]
+        else:
+            text_seq = xc[bi]
+        lines = "\n".join(
+            text_seq[start : start + nc] for start in range(0, len(text_seq), nc)
+        )
+
+        try:
+            draw.text((0, 0), lines, fill="black", font=font)
+        except UnicodeEncodeError:
+            print("Cant encode string for logging. Skipping.")
+
+        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+        txts.append(txt)
+    txts = np.stack(txts)
+    txts = torch.tensor(txts)
+    return txts
+
+
+def partialclass(cls, *args, **kwargs):
+    class NewCls(cls):
+        __init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
+
+    return NewCls
+
+
+def make_path_absolute(path):
+    fs, p = fsspec.core.url_to_fs(path)
+    if fs.protocol == "file":
+        return os.path.abspath(p)
+    return path
+
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def isheatmap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+
+    return x.ndim == 2
+
+
+def isneighbors(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return x.ndim == 5 and (x.shape[2] == 3 or x.shape[2] == 1)
+
+
+def exists(x):
+    return x is not None
+
+
+def expand_dims_like(x, y):
+    while x.dim() != y.dim():
+        x = x.unsqueeze(-1)
+    return x
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+    """
+    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+    return total_params
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == "__is_first_stage__":
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False, invalidate_cache=True):
+    module, cls = string.rsplit(".", 1)
+    if invalidate_cache:
+        importlib.invalidate_caches()
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+        )
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def load_model_from_config(config, ckpt, verbose=True, freeze=True):
+    print(f"Loading model from {ckpt}")
+    if ckpt.endswith("ckpt"):
+        pl_sd = torch.load(ckpt, map_location="cpu")
+        if "global_step" in pl_sd:
+            print(f"Global Step: {pl_sd['global_step']}")
+        sd = pl_sd["state_dict"]
+    elif ckpt.endswith("safetensors"):
+        sd = load_safetensors(ckpt)
+    else:
+        raise NotImplementedError
+
+    model = instantiate_from_config(config.model)
+
+    m, u = model.load_state_dict(sd, strict=False)
+
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    if freeze:
+        for param in model.parameters():
+            param.requires_grad = False
+
+    model.eval()
+    return model
+
+
+def get_configs_path() -> str:
+    """
+    Get the `configs` directory.
+    For a working copy, this is the one in the root of the repository,
+    but for an installed copy, it's in the `sgm` package (see pyproject.toml).
+    """
+    this_dir = os.path.dirname(__file__)
+    candidates = (
+        os.path.join(this_dir, "configs"),
+        os.path.join(this_dir, "..", "configs"),
+    )
+    for candidate in candidates:
+        candidate = os.path.abspath(candidate)
+        if os.path.isdir(candidate):
+            return candidate
+    raise FileNotFoundError(f"Could not find SGM configs in {candidates}")
+
+
+def get_nested_attribute(obj, attribute_path, depth=None, return_key=False):
+    """
+    Will return the result of a recursive get attribute call.
+    E.g.:
+        a.b.c
+        = getattr(getattr(a, "b"), "c")
+        = get_nested_attribute(a, "b.c")
+    If any part of the attribute call is an integer x with current obj a, will
+    try to call a[x] instead of a.x first.
+    """
+    attributes = attribute_path.split(".")
+    if depth is not None and depth > 0:
+        attributes = attributes[:depth]
+    assert len(attributes) > 0, "At least one attribute should be selected"
+    current_attribute = obj
+    current_key = None
+    for level, attribute in enumerate(attributes):
+        current_key = ".".join(attributes[: level + 1])
+        try:
+            id_ = int(attribute)
+            current_attribute = current_attribute[id_]
+        except ValueError:
+            current_attribute = getattr(current_attribute, attribute)
+
+    return (current_attribute, current_key) if return_key else current_attribute
diff --git a/nsr/__init__.py b/nsr/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3f5ce82485e0aee06a09d3bbd1f40e5b28fa5af
--- /dev/null
+++ b/nsr/__init__.py
@@ -0,0 +1,16 @@
+# triplane, tensorF etc.
+from .train_util import TrainLoop3DRec, TrainLoop3DRecTrajVis
+# from .train_util_cvD import TrainLoop3DcvD
+
+# from .cvD.nvsD import TrainLoop3DcvD_nvsD
+# from .cvD.nvsD_nosr import TrainLoop3DcvD_nvsD_noSR
+# from .cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD, TrainLoop3DcvD_nvsD_canoD_eg3d
+# from .cvD.nvsD_canoD_mask import TrainLoop3DcvD_nvsD_canoD_canomask
+# from .cvD.canoD import TrainLoop3DcvD_canoD
+# from .cvD.nvsD_canoD_multiview import TrainLoop3DcvD_nvsD_canoD_multiview
+
+# * difffusion trainer
+from .train_util_diffusion import TrainLoop3DDiffusion
+from .train_util_diffusion_dit import TrainLoop3DDiffusionDiT
+
+from .lsgm import *
\ No newline at end of file
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diff --git a/nsr/augment.py b/nsr/augment.py
new file mode 100755
index 0000000000000000000000000000000000000000..3efbf1270a94f08413075c986deeb1570a80f543
--- /dev/null
+++ b/nsr/augment.py
@@ -0,0 +1,431 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import numpy as np
+import scipy.signal
+import torch
+from torch_utils import persistence
+from torch_utils import misc
+from torch_utils.ops import upfirdn2d
+from torch_utils.ops import grid_sample_gradfix
+from torch_utils.ops import conv2d_gradfix
+
+#----------------------------------------------------------------------------
+# Coefficients of various wavelet decomposition low-pass filters.
+
+wavelets = {
+    'haar': [0.7071067811865476, 0.7071067811865476],
+    'db1':  [0.7071067811865476, 0.7071067811865476],
+    'db2':  [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
+    'db3':  [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
+    'db4':  [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523],
+    'db5':  [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125],
+    'db6':  [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017],
+    'db7':  [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236],
+    'db8':  [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161],
+    'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
+    'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
+    'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427],
+    'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728],
+    'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148],
+    'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255],
+    'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609],
+}
+
+#----------------------------------------------------------------------------
+# Helpers for constructing transformation matrices.
+
+def matrix(*rows, device=None):
+    assert all(len(row) == len(rows[0]) for row in rows)
+    elems = [x for row in rows for x in row]
+    ref = [x for x in elems if isinstance(x, torch.Tensor)]
+    if len(ref) == 0:
+        return misc.constant(np.asarray(rows), device=device)
+    assert device is None or device == ref[0].device
+    elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems]
+    return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))
+
+def translate2d(tx, ty, **kwargs):
+    return matrix(
+        [1, 0, tx],
+        [0, 1, ty],
+        [0, 0, 1],
+        **kwargs)
+
+def translate3d(tx, ty, tz, **kwargs):
+    return matrix(
+        [1, 0, 0, tx],
+        [0, 1, 0, ty],
+        [0, 0, 1, tz],
+        [0, 0, 0, 1],
+        **kwargs)
+
+def scale2d(sx, sy, **kwargs):
+    return matrix(
+        [sx, 0,  0],
+        [0,  sy, 0],
+        [0,  0,  1],
+        **kwargs)
+
+def scale3d(sx, sy, sz, **kwargs):
+    return matrix(
+        [sx, 0,  0,  0],
+        [0,  sy, 0,  0],
+        [0,  0,  sz, 0],
+        [0,  0,  0,  1],
+        **kwargs)
+
+def rotate2d(theta, **kwargs):
+    return matrix(
+        [torch.cos(theta), torch.sin(-theta), 0],
+        [torch.sin(theta), torch.cos(theta),  0],
+        [0,                0,                 1],
+        **kwargs)
+
+def rotate3d(v, theta, **kwargs):
+    vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2]
+    s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c
+    return matrix(
+        [vx*vx*cc+c,    vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0],
+        [vy*vx*cc+vz*s, vy*vy*cc+c,    vy*vz*cc-vx*s, 0],
+        [vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c,    0],
+        [0,             0,             0,             1],
+        **kwargs)
+
+def translate2d_inv(tx, ty, **kwargs):
+    return translate2d(-tx, -ty, **kwargs)
+
+def scale2d_inv(sx, sy, **kwargs):
+    return scale2d(1 / sx, 1 / sy, **kwargs)
+
+def rotate2d_inv(theta, **kwargs):
+    return rotate2d(-theta, **kwargs)
+
+#----------------------------------------------------------------------------
+# Versatile image augmentation pipeline from the paper
+# "Training Generative Adversarial Networks with Limited Data".
+#
+# All augmentations are disabled by default; individual augmentations can
+# be enabled by setting their probability multipliers to 1.
+
+@persistence.persistent_class
+class AugmentPipe(torch.nn.Module):
+    def __init__(self,
+        xflip=0, rotate90=0, xint=0, xint_max=0.125,
+        scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125,
+        brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1,
+        imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1,
+        noise=0, cutout=0, noise_std=0.1, cutout_size=0.5,
+    ):
+        super().__init__()
+        self.register_buffer('p', torch.ones([]))       # Overall multiplier for augmentation probability.
+
+        # Pixel blitting.
+        self.xflip            = float(xflip)            # Probability multiplier for x-flip.
+        self.rotate90         = float(rotate90)         # Probability multiplier for 90 degree rotations.
+        self.xint             = float(xint)             # Probability multiplier for integer translation.
+        self.xint_max         = float(xint_max)         # Range of integer translation, relative to image dimensions.
+
+        # General geometric transformations.
+        self.scale            = float(scale)            # Probability multiplier for isotropic scaling.
+        self.rotate           = float(rotate)           # Probability multiplier for arbitrary rotation.
+        self.aniso            = float(aniso)            # Probability multiplier for anisotropic scaling.
+        self.xfrac            = float(xfrac)            # Probability multiplier for fractional translation.
+        self.scale_std        = float(scale_std)        # Log2 standard deviation of isotropic scaling.
+        self.rotate_max       = float(rotate_max)       # Range of arbitrary rotation, 1 = full circle.
+        self.aniso_std        = float(aniso_std)        # Log2 standard deviation of anisotropic scaling.
+        self.xfrac_std        = float(xfrac_std)        # Standard deviation of frational translation, relative to image dimensions.
+
+        # Color transformations.
+        self.brightness       = float(brightness)       # Probability multiplier for brightness.
+        self.contrast         = float(contrast)         # Probability multiplier for contrast.
+        self.lumaflip         = float(lumaflip)         # Probability multiplier for luma flip.
+        self.hue              = float(hue)              # Probability multiplier for hue rotation.
+        self.saturation       = float(saturation)       # Probability multiplier for saturation.
+        self.brightness_std   = float(brightness_std)   # Standard deviation of brightness.
+        self.contrast_std     = float(contrast_std)     # Log2 standard deviation of contrast.
+        self.hue_max          = float(hue_max)          # Range of hue rotation, 1 = full circle.
+        self.saturation_std   = float(saturation_std)   # Log2 standard deviation of saturation.
+
+        # Image-space filtering.
+        self.imgfilter        = float(imgfilter)        # Probability multiplier for image-space filtering.
+        self.imgfilter_bands  = list(imgfilter_bands)   # Probability multipliers for individual frequency bands.
+        self.imgfilter_std    = float(imgfilter_std)    # Log2 standard deviation of image-space filter amplification.
+
+        # Image-space corruptions.
+        self.noise            = float(noise)            # Probability multiplier for additive RGB noise.
+        self.cutout           = float(cutout)           # Probability multiplier for cutout.
+        self.noise_std        = float(noise_std)        # Standard deviation of additive RGB noise.
+        self.cutout_size      = float(cutout_size)      # Size of the cutout rectangle, relative to image dimensions.
+
+        # Setup orthogonal lowpass filter for geometric augmentations.
+        self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6']))
+
+        # Construct filter bank for image-space filtering.
+        Hz_lo = np.asarray(wavelets['sym2'])            # H(z)
+        Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z)
+        Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2    # H(z) * H(z^-1) / 2
+        Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2    # H(-z) * H(-z^-1) / 2
+        Hz_fbank = np.eye(4, 1)                         # Bandpass(H(z), b_i)
+        for i in range(1, Hz_fbank.shape[0]):
+            Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1]
+            Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])
+            Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2
+        self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32))
+
+    def forward(self, images, debug_percentile=None):
+        assert isinstance(images, torch.Tensor) and images.ndim == 4
+        batch_size, num_channels, height, width = images.shape
+        device = images.device
+        if debug_percentile is not None:
+            debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device)
+
+        # -------------------------------------
+        # Select parameters for pixel blitting.
+        # -------------------------------------
+
+        # Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in
+        I_3 = torch.eye(3, device=device)
+        G_inv = I_3
+
+        # Apply x-flip with probability (xflip * strength).
+        if self.xflip > 0:
+            i = torch.floor(torch.rand([batch_size], device=device) * 2)
+            i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i))
+            if debug_percentile is not None:
+                i = torch.full_like(i, torch.floor(debug_percentile * 2))
+            G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)
+
+        # Apply 90 degree rotations with probability (rotate90 * strength).
+        if self.rotate90 > 0:
+            i = torch.floor(torch.rand([batch_size], device=device) * 4)
+            i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i))
+            if debug_percentile is not None:
+                i = torch.full_like(i, torch.floor(debug_percentile * 4))
+            G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)
+
+        # Apply integer translation with probability (xint * strength).
+        if self.xint > 0:
+            t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max
+            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t))
+            if debug_percentile is not None:
+                t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max)
+            G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height))
+
+        # --------------------------------------------------------
+        # Select parameters for general geometric transformations.
+        # --------------------------------------------------------
+
+        # Apply isotropic scaling with probability (scale * strength).
+        if self.scale > 0:
+            s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
+            s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s))
+            if debug_percentile is not None:
+                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std))
+            G_inv = G_inv @ scale2d_inv(s, s)
+
+        # Apply pre-rotation with probability p_rot.
+        p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p
+        if self.rotate > 0:
+            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
+            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
+            if debug_percentile is not None:
+                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max)
+            G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.
+
+        # Apply anisotropic scaling with probability (aniso * strength).
+        if self.aniso > 0:
+            s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std)
+            s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s))
+            if debug_percentile is not None:
+                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std))
+            G_inv = G_inv @ scale2d_inv(s, 1 / s)
+
+        # Apply post-rotation with probability p_rot.
+        if self.rotate > 0:
+            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
+            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
+            if debug_percentile is not None:
+                theta = torch.zeros_like(theta)
+            G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.
+
+        # Apply fractional translation with probability (xfrac * strength).
+        if self.xfrac > 0:
+            t = torch.randn([batch_size, 2], device=device) * self.xfrac_std
+            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t))
+            if debug_percentile is not None:
+                t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)
+            G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height)
+
+        # ----------------------------------
+        # Execute geometric transformations.
+        # ----------------------------------
+
+        # Execute if the transform is not identity.
+        if G_inv is not I_3:
+
+            # Calculate padding.
+            cx = (width - 1) / 2
+            cy = (height - 1) / 2
+            cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz]
+            cp = G_inv @ cp.t() # [batch, xyz, idx]
+            Hz_pad = self.Hz_geom.shape[0] // 4
+            margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx]
+            margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1]
+            margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)
+            margin = margin.max(misc.constant([0, 0] * 2, device=device))
+            margin = margin.min(misc.constant([width-1, height-1] * 2, device=device))
+            mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)
+
+            # Pad image and adjust origin.
+            images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect')
+            G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv
+
+            # Upsample.
+            images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)
+            G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device)
+            G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device)
+
+            # Execute transformation.
+            shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2]
+            G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device)
+            grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False)
+            images = grid_sample_gradfix.grid_sample(images, grid)
+
+            # Downsample and crop.
+            images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True)
+
+        # --------------------------------------------
+        # Select parameters for color transformations.
+        # --------------------------------------------
+
+        # Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out
+        I_4 = torch.eye(4, device=device)
+        C = I_4
+
+        # Apply brightness with probability (brightness * strength).
+        if self.brightness > 0:
+            b = torch.randn([batch_size], device=device) * self.brightness_std
+            b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b))
+            if debug_percentile is not None:
+                b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std)
+            C = translate3d(b, b, b) @ C
+
+        # Apply contrast with probability (contrast * strength).
+        if self.contrast > 0:
+            c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std)
+            c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c))
+            if debug_percentile is not None:
+                c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std))
+            C = scale3d(c, c, c) @ C
+
+        # Apply luma flip with probability (lumaflip * strength).
+        v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.
+        if self.lumaflip > 0:
+            i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)
+            i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i))
+            if debug_percentile is not None:
+                i = torch.full_like(i, torch.floor(debug_percentile * 2))
+            C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.
+
+        # Apply hue rotation with probability (hue * strength).
+        if self.hue > 0 and num_channels > 1:
+            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max
+            theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta))
+            if debug_percentile is not None:
+                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max)
+            C = rotate3d(v, theta) @ C # Rotate around v.
+
+        # Apply saturation with probability (saturation * strength).
+        if self.saturation > 0 and num_channels > 1:
+            s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std)
+            s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s))
+            if debug_percentile is not None:
+                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std))
+            C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C
+
+        # ------------------------------
+        # Execute color transformations.
+        # ------------------------------
+
+        # Execute if the transform is not identity.
+        if C is not I_4:
+            images = images.reshape([batch_size, num_channels, height * width])
+            if num_channels == 3:
+                images = C[:, :3, :3] @ images + C[:, :3, 3:]
+            elif num_channels == 1:
+                C = C[:, :3, :].mean(dim=1, keepdims=True)
+                images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:]
+            else:
+                raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
+            images = images.reshape([batch_size, num_channels, height, width])
+
+        # ----------------------
+        # Image-space filtering.
+        # ----------------------
+
+        if self.imgfilter > 0:
+            num_bands = self.Hz_fbank.shape[0]
+            assert len(self.imgfilter_bands) == num_bands
+            expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f).
+
+            # Apply amplification for each band with probability (imgfilter * strength * band_strength).
+            g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity).
+            for i, band_strength in enumerate(self.imgfilter_bands):
+                t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std)
+                t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i))
+                if debug_percentile is not None:
+                    t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i)
+                t = torch.ones([batch_size, num_bands], device=device)                  # Temporary gain vector.
+                t[:, i] = t_i                                                           # Replace i'th element.
+                t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power.
+                g = g * t                                                               # Accumulate into global gain.
+
+            # Construct combined amplification filter.
+            Hz_prime = g @ self.Hz_fbank                                    # [batch, tap]
+            Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1])   # [batch, channels, tap]
+            Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap]
+
+            # Apply filter.
+            p = self.Hz_fbank.shape[1] // 2
+            images = images.reshape([1, batch_size * num_channels, height, width])
+            images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect')
+            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels)
+            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels)
+            images = images.reshape([batch_size, num_channels, height, width])
+
+        # ------------------------
+        # Image-space corruptions.
+        # ------------------------
+
+        # Apply additive RGB noise with probability (noise * strength).
+        if self.noise > 0:
+            sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std
+            sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma))
+            if debug_percentile is not None:
+                sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std)
+            images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma
+
+        # Apply cutout with probability (cutout * strength).
+        if self.cutout > 0:
+            size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device)
+            size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size))
+            center = torch.rand([batch_size, 2, 1, 1, 1], device=device)
+            if debug_percentile is not None:
+                size = torch.full_like(size, self.cutout_size)
+                center = torch.full_like(center, debug_percentile)
+            coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])
+            coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1])
+            mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2)
+            mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2)
+            mask = torch.logical_or(mask_x, mask_y).to(torch.float32)
+            images = images * mask
+
+        return images
+
+#----------------------------------------------------------------------------
diff --git a/nsr/camera_utils.py b/nsr/camera_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3c5fa32397e14090246aa518035f2128525d936
--- /dev/null
+++ b/nsr/camera_utils.py
@@ -0,0 +1,286 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""
+Helper functions for constructing camera parameter matrices. Primarily used in visualization and inference scripts.
+"""
+
+import math
+
+import torch as th
+import torch
+import torch.nn as nn
+import numpy as np
+
+from nsr.volumetric_rendering import math_utils
+
+
+class GaussianCameraPoseSampler:
+    """
+    Samples pitch and yaw from a Gaussian distribution and returns a camera pose.
+    Camera is specified as looking at the origin.
+    If horizontal and vertical stddev (specified in radians) are zero, gives a
+    deterministic camera pose with yaw=horizontal_mean, pitch=vertical_mean.
+    The coordinate system is specified with y-up, z-forward, x-left.
+    Horizontal mean is the azimuthal angle (rotation around y axis) in radians,
+    vertical mean is the polar angle (angle from the y axis) in radians.
+    A point along the z-axis has azimuthal_angle=0, polar_angle=pi/2.
+
+    Example:
+    For a camera pose looking at the origin with the camera at position [0, 0, 1]:
+    cam2world = GaussianCameraPoseSampler.sample(math.pi/2, math.pi/2, radius=1)
+    """
+
+    @staticmethod
+    def sample(horizontal_mean,
+               vertical_mean,
+               horizontal_stddev=0,
+               vertical_stddev=0,
+               radius=1,
+               batch_size=1,
+               device='cpu'):
+        h = torch.randn((batch_size, 1),
+                        device=device) * horizontal_stddev + horizontal_mean
+        v = torch.randn(
+            (batch_size, 1), device=device) * vertical_stddev + vertical_mean
+        v = torch.clamp(v, 1e-5, math.pi - 1e-5)
+
+        theta = h
+        v = v / math.pi
+        phi = torch.arccos(1 - 2 * v)
+
+        camera_origins = torch.zeros((batch_size, 3), device=device)
+
+        camera_origins[:, 0:1] = radius * torch.sin(phi) * torch.cos(math.pi -
+                                                                     theta)
+        camera_origins[:, 2:3] = radius * torch.sin(phi) * torch.sin(math.pi -
+                                                                     theta)
+        camera_origins[:, 1:2] = radius * torch.cos(phi)
+
+        forward_vectors = math_utils.normalize_vecs(-camera_origins)
+        return create_cam2world_matrix(forward_vectors, camera_origins)
+
+
+class LookAtPoseSampler:
+    """
+    Same as GaussianCameraPoseSampler, except the
+    camera is specified as looking at 'lookat_position', a 3-vector.
+
+    Example:
+    For a camera pose looking at the origin with the camera at position [0, 0, 1]:
+    cam2world = LookAtPoseSampler.sample(math.pi/2, math.pi/2, torch.tensor([0, 0, 0]), radius=1)
+    """
+
+    @staticmethod
+    def sample(horizontal_mean,
+               vertical_mean,
+               lookat_position,
+               horizontal_stddev=0.,
+               vertical_stddev=0.,
+               radius=1,
+               batch_size=1,
+               device='cpu'):
+        h = torch.randn((batch_size, 1),
+                        device=device) * horizontal_stddev + horizontal_mean
+        v = torch.randn(
+            (batch_size, 1), device=device) * vertical_stddev + vertical_mean
+        v = torch.clamp(v, 1e-5, math.pi - 1e-5)
+
+        theta = h
+        v = v / math.pi
+        phi = torch.arccos(1 - 2 * v)
+
+        camera_origins = torch.zeros((batch_size, 3), device=device)
+
+        camera_origins[:, 0:1] = radius * torch.sin(phi) * torch.cos(math.pi -
+                                                                     theta)
+        camera_origins[:, 2:3] = radius * torch.sin(phi) * torch.sin(math.pi -
+                                                                     theta)
+        camera_origins[:, 1:2] = radius * torch.cos(phi)
+
+        # forward_vectors = math_utils.normalize_vecs(-camera_origins)
+        forward_vectors = math_utils.normalize_vecs(lookat_position -
+                                                    camera_origins)
+        return create_cam2world_matrix(forward_vectors, camera_origins)
+
+
+class UniformCameraPoseSampler:
+    """
+    Same as GaussianCameraPoseSampler, except the
+    pose is sampled from a uniform distribution with range +-[horizontal/vertical]_stddev.
+
+    Example:
+    For a batch of random camera poses looking at the origin with yaw sampled from [-pi/2, +pi/2] radians:
+
+    cam2worlds = UniformCameraPoseSampler.sample(math.pi/2, math.pi/2, horizontal_stddev=math.pi/2, radius=1, batch_size=16)
+    """
+
+    @staticmethod
+    def sample(horizontal_mean,
+               vertical_mean,
+               horizontal_stddev=0,
+               vertical_stddev=0,
+               radius=1,
+               batch_size=1,
+               device='cpu'):
+        h = (torch.rand((batch_size, 1), device=device) * 2 -
+             1) * horizontal_stddev + horizontal_mean
+        v = (torch.rand((batch_size, 1), device=device) * 2 -
+             1) * vertical_stddev + vertical_mean
+        v = torch.clamp(v, 1e-5, math.pi - 1e-5)
+
+        theta = h
+        v = v / math.pi
+        phi = torch.arccos(1 - 2 * v)
+
+        camera_origins = torch.zeros((batch_size, 3), device=device)
+
+        camera_origins[:, 0:1] = radius * torch.sin(phi) * torch.cos(math.pi -
+                                                                     theta)
+        camera_origins[:, 2:3] = radius * torch.sin(phi) * torch.sin(math.pi -
+                                                                     theta)
+        camera_origins[:, 1:2] = radius * torch.cos(phi)
+
+        forward_vectors = math_utils.normalize_vecs(-camera_origins)
+        return create_cam2world_matrix(forward_vectors, camera_origins)
+
+
+def create_cam2world_matrix(forward_vector, origin):
+    """
+    Takes in the direction the camera is pointing and the camera origin and returns a cam2world matrix.
+    Works on batches of forward_vectors, origins. Assumes y-axis is up and that there is no camera roll.
+    """
+
+    forward_vector = math_utils.normalize_vecs(forward_vector)
+    up_vector = torch.tensor([0, 1, 0],
+                             dtype=torch.float,
+                             device=origin.device).expand_as(forward_vector)
+
+    right_vector = -math_utils.normalize_vecs(
+        torch.cross(up_vector, forward_vector, dim=-1))
+    up_vector = math_utils.normalize_vecs(
+        torch.cross(forward_vector, right_vector, dim=-1))
+
+    rotation_matrix = torch.eye(4, device=origin.device).unsqueeze(0).repeat(
+        forward_vector.shape[0], 1, 1)
+    rotation_matrix[:, :3, :3] = torch.stack(
+        (right_vector, up_vector, forward_vector), axis=-1)
+
+    translation_matrix = torch.eye(4,
+                                   device=origin.device).unsqueeze(0).repeat(
+                                       forward_vector.shape[0], 1, 1)
+    translation_matrix[:, :3, 3] = origin
+    cam2world = (translation_matrix @ rotation_matrix)[:, :, :]
+    assert (cam2world.shape[1:] == (4, 4))
+    return cam2world
+
+
+def FOV_to_intrinsics(fov_degrees, device='cpu'):
+    """
+    Creates a 3x3 camera intrinsics matrix from the camera field of view, specified in degrees.
+    Note the intrinsics are returned as normalized by image size, rather than in pixel units.
+    Assumes principal point is at image center.
+    """
+
+    focal_length = float(1 / (math.tan(fov_degrees * 3.14159 / 360) * 1.414))
+    intrinsics = torch.tensor(
+        [[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]],
+        device=device)
+    return intrinsics
+
+def generate_input_camera(r, poses, device='cpu', fov=30):
+    def normalize_vecs(vectors): return vectors / (torch.norm(vectors, dim=-1, keepdim=True))
+    poses = np.deg2rad(poses)
+    poses = torch.tensor(poses).float()
+    pitch = poses[:, 0]
+    yaw = poses[:, 1]
+
+    z = r*torch.sin(pitch)
+    x = r*torch.cos(pitch)*torch.cos(yaw)
+    y = r*torch.cos(pitch)*torch.sin(yaw)
+    cam_pos = torch.stack([x, y, z], dim=-1).reshape(z.shape[0], -1).to(device)
+
+    forward_vector = normalize_vecs(-cam_pos)
+    up_vector = torch.tensor([0, 0, -1], dtype=torch.float,
+                                        device=device).reshape(-1).expand_as(forward_vector)
+    left_vector = normalize_vecs(torch.cross(up_vector, forward_vector,
+                                                        dim=-1))
+
+    up_vector = normalize_vecs(torch.cross(forward_vector, left_vector,
+                                                        dim=-1))
+    rotate = torch.stack(
+                    (left_vector, up_vector, forward_vector), dim=-1)
+
+    rotation_matrix = torch.eye(4, device=device).unsqueeze(0).repeat(forward_vector.shape[0], 1, 1)
+    rotation_matrix[:, :3, :3] = rotate
+
+    translation_matrix = torch.eye(4, device=device).unsqueeze(0).repeat(forward_vector.shape[0], 1, 1)
+    translation_matrix[:, :3, 3] = cam_pos
+    cam2world = translation_matrix @ rotation_matrix
+
+    fx = 0.5/np.tan(np.deg2rad(fov/2))
+    fxfycxcy = torch.tensor([fx, fx, 0.5, 0.5], dtype=rotate.dtype, device=device)
+
+    return cam2world, fxfycxcy
+
+def uni_mesh_path(frame_number=16, radius=1.8):
+    azimuths = []
+    elevations = []
+
+    # for elevation in [0,-30,30]:
+    # for elevation in [0,-30,30, -65, 65]:
+    # for elevation in [0,-30,30, -60, 60]:
+    for elevation in [60,30, 0, -30, -60]:
+
+        for i in range(frame_number): # 1030 * 5 * 10, for FID 50K
+
+            # azi, elevation = sample_uniform_cameras_on_sphere()
+            # azi, elevation = azi[0] / np.pi * 180, elevation[0] / np.pi * 180
+            # azi, elevation = azi[0] / np.pi * 180, 0
+            azi = i / frame_number * 360 # [0, 2pi]
+            azimuths.append(azi)
+            elevations.append(elevation)
+
+    azimuths = np.array(azimuths)
+    elevations = np.array(elevations)
+
+    all_frame_number = azimuths.shape[0]
+
+    # azimuths = np.array(list(range(0,360,30))).astype(float)
+    # frame_number = azimuths.shape[0]
+    # elevations = np.array([10]*azimuths.shape[0]).astype(float)
+
+    zero123pp_pose, _ = generate_input_camera(radius, [[elevations[i], azimuths[i]] for i in range(all_frame_number)], fov=30)
+    K = th.Tensor([1.3889, 0.0000, 0.5000, 0.0000, 1.3889, 0.5000, 0.0000, 0.0000, 0.0039]).to(zero123pp_pose) # keeps the same
+    mesh_pathes = th.cat([zero123pp_pose.reshape(all_frame_number,-1), K.unsqueeze(0).repeat(all_frame_number,1)], dim=-1).cpu().numpy()
+
+    return mesh_pathes
+
+
+
+def sample_uniform_cameras_on_sphere(num_samples=1):
+    # Step 1: Sample azimuth angles uniformly from [0, 2*pi)
+    theta = np.random.uniform(0, 2 * np.pi, num_samples)
+    
+    # Step 2: Sample cos(phi) uniformly from [-1, 1]
+    cos_phi = np.random.uniform(-1, 1, num_samples)
+    
+    # Step 3: Calculate the elevation angle (phi) from cos(phi)
+    phi = np.arccos(cos_phi)  # phi will be in [0, pi]
+    
+    # Step 4: Convert spherical coordinates to Cartesian coordinates (x, y, z)
+    # x = np.sin(phi) * np.cos(theta)
+    # y = np.sin(phi) * np.sin(theta)
+    # z = np.cos(phi)
+    
+    # Combine the x, y, z coordinates into a single array
+    # cameras = np.vstack((x, y, z)).T  # Shape: (num_samples, 3)
+    
+    # return cameras
+    return theta, phi
diff --git a/nsr/common_blks.py b/nsr/common_blks.py
new file mode 100644
index 0000000000000000000000000000000000000000..2789aea5a060c5a94d21e8a69cd566e6211a76e5
--- /dev/null
+++ b/nsr/common_blks.py
@@ -0,0 +1,216 @@
+
+
+# https://github.com/sxyu/pixel-nerf/blob/master/src/model/resnetfc.py
+from torch import nn
+import torch
+
+from vit.vision_transformer import Mlp, DropPath
+
+
+# Resnet Blocks
+class ResnetBlockFC(nn.Module):
+    """
+    Fully connected ResNet Block class.
+    Taken from DVR code.
+    :param size_in (int): input dimension
+    :param size_out (int): output dimension
+    :param size_h (int): hidden dimension
+    """
+    def __init__(self, size_in, size_out=None, size_h=None, beta=0.0, init_as_zero=False):
+        super().__init__()
+        # Attributes
+        if size_out is None:
+            size_out = size_in
+
+        if size_h is None:
+            size_h = min(size_in, size_out)
+
+        self.size_in = size_in
+        self.size_h = size_h
+        self.size_out = size_out
+        # Submodules
+        self.fc_0 = nn.Linear(size_in, size_h)
+        self.fc_1 = nn.Linear(size_h, size_out)
+
+        # Init
+        nn.init.constant_(self.fc_0.bias, 0.0)
+        if init_as_zero:
+            nn.init.zeros_(self.fc_0.weight)
+        else:
+            nn.init.kaiming_normal_(self.fc_0.weight, a=0, mode="fan_in")
+        nn.init.constant_(self.fc_1.bias, 0.0)
+        nn.init.zeros_(self.fc_1.weight)
+
+        if beta > 0:
+            self.activation = nn.Softplus(beta=beta)
+        else:
+            self.activation = nn.ReLU()
+
+        if size_in == size_out:
+            self.shortcut = None
+        else:
+            self.shortcut = nn.Linear(size_in, size_out, bias=False)
+            # nn.init.constant_(self.shortcut.bias, 0.0)
+            nn.init.kaiming_normal_(self.shortcut.weight, a=0, mode="fan_in")
+
+    def forward(self, x):
+        # with profiler.record_function("resblock"):
+        net = self.fc_0(self.activation(x))
+        dx = self.fc_1(self.activation(net))
+
+        if self.shortcut is not None:
+            x_s = self.shortcut(x)
+        else:
+            x_s = x
+        return x_s + dx
+
+
+
+
+# Resnet Blocks
+class ResnetBlockFCViT(nn.Module):
+    """
+    Fully connected ResNet Block class.
+    Taken from DVR code.
+    :param size_in (int): input dimension
+    :param size_out (int): output dimension
+    :param size_h (int): hidden dimension
+    """
+    def __init__(self, size_in, size_out=None, size_h=None, beta=0.0, init_as_zero=False):
+        super().__init__()
+        # Attributes
+        if size_out is None:
+            size_out = size_in
+
+        if size_h is None:
+            size_h = min(size_in, size_out)
+
+        self.size_in = size_in
+        self.size_h = size_h
+        self.size_out = size_out
+        # Submodules
+        self.fc_0 = nn.Linear(size_in, size_h)
+        self.fc_1 = nn.Linear(size_h, size_out)
+
+        # Init
+        nn.init.constant_(self.fc_0.bias, 0.0)
+        if init_as_zero:
+            nn.init.zeros_(self.fc_0.weight)
+        else:
+            nn.init.kaiming_normal_(self.fc_0.weight, a=0, mode="fan_in")
+        nn.init.constant_(self.fc_1.bias, 0.0)
+        nn.init.zeros_(self.fc_1.weight)
+
+        if beta > 0:
+            self.activation = nn.Softplus(beta=beta)
+        else:
+            self.activation = nn.ReLU()
+
+        if size_in == size_out:
+            self.shortcut = None
+        else:
+            self.shortcut = nn.Linear(size_in, size_out, bias=False)
+            # nn.init.constant_(self.shortcut.bias, 0.0)
+            nn.init.kaiming_normal_(self.shortcut.weight, a=0, mode="fan_in")
+
+    def forward(self, x):
+        # with profiler.record_function("resblock"):
+        net = self.fc_0(self.activation(x))
+        dx = self.fc_1(self.activation(net))
+
+        if self.shortcut is not None:
+            x_s = self.shortcut(x)
+        else:
+            x_s = x
+        return x_s + dx
+
+
+# class Block(nn.Module):
+#     def __init__(self,
+#                  dim,
+#                  num_heads,
+#                  mlp_ratio=4.,
+#                  qkv_bias=False,
+#                  qk_scale=None,
+#                  drop=0.,
+#                  attn_drop=0.,
+#                  drop_path=0.,
+#                  act_layer=nn.GELU,
+#                  norm_layer=nn.LayerNorm):
+#         super().__init__()
+#         self.norm1 = norm_layer(dim)
+#         self.attn = Attention(dim,
+#                               num_heads=num_heads,
+#                               qkv_bias=qkv_bias,
+#                               qk_scale=qk_scale,
+#                               attn_drop=attn_drop,
+#                               proj_drop=drop)
+#         self.drop_path = DropPath(
+#             drop_path) if drop_path > 0. else nn.Identity()
+#         self.norm2 = norm_layer(dim)
+#         mlp_hidden_dim = int(dim * mlp_ratio)
+#         self.mlp = Mlp(in_features=dim,
+#                        hidden_features=mlp_hidden_dim,
+#                        act_layer=act_layer,
+#                        drop=drop)
+
+#     def forward(self, x, return_attention=False):
+#         y, attn = self.attn(self.norm1(x))
+#         if return_attention:
+#             return attn
+#         x = x + self.drop_path(y)
+#         x = x + self.drop_path(self.mlp(self.norm2(x)))
+#         return x
+
+
+
+
+class ResMlp(nn.Module):
+    def __init__(self,
+                 
+                size_in,
+                size_out=None,
+                size_h=None,
+                drop=0.,
+                drop_path=0.,
+                act_layer=nn.GELU,
+                norm_layer=nn.LayerNorm,
+                 ):
+        super().__init__()
+
+        # Attributes
+        if size_out is None:
+            size_out = size_in
+        if size_h is None:
+            size_h = min(size_in, size_out)
+        self.size_in = size_in
+        self.size_h = size_h
+        self.size_out = size_out
+
+        # Submodules
+        self.norm1 = norm_layer(size_in) # ? how to use
+
+        self.mlp = Mlp(in_features=size_in, 
+                       out_features=size_out,
+                       act_layer=act_layer,
+                       drop=drop)
+
+        # Residual shortcuts
+        if size_in == size_out:
+            self.shortcut = None
+        else:
+            self.shortcut = nn.Linear(size_in, size_out, bias=False)
+            self.norm2 = norm_layer(size_in)
+
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        dx = self.mlp(self.norm1(x))
+
+        if self.shortcut is not None:
+            x_s = self.shortcut(self.norm2(x))
+        else:
+            x_s = x
+
+        return x_s + self.drop_path(dx)
\ No newline at end of file
diff --git a/nsr/confnet.py b/nsr/confnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..65d908ffaf5c5131aaef75bac769c23b8a760b2f
--- /dev/null
+++ b/nsr/confnet.py
@@ -0,0 +1,63 @@
+import torch
+import torch.nn as nn
+import torchvision
+
+
+EPS = 1e-7
+
+class ConfNet(nn.Module):
+    def __init__(self, cin=3, cout=1, zdim=128, nf=64):
+        super(ConfNet, self).__init__()
+        ## downsampling
+        network = [
+            nn.Conv2d(cin, nf, kernel_size=4, stride=2, padding=1, bias=False),  # 64x64 -> 32x32
+            nn.GroupNorm(16, nf),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(nf, nf*2, kernel_size=4, stride=2, padding=1, bias=False),  # 32x32 -> 16x16
+            nn.GroupNorm(16*2, nf*2),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(nf*2, nf*4, kernel_size=4, stride=2, padding=1, bias=False),  # 16x16 -> 8x8
+            nn.GroupNorm(16*4, nf*4),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(nf*4, nf*8, kernel_size=4, stride=2, padding=1, bias=False),  # 8x8 -> 4x4
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(nf*8, zdim, kernel_size=4, stride=1, padding=0, bias=False),  # 4x4 -> 1x1
+            nn.ReLU(inplace=True)]
+        ## upsampling
+        network += [
+            nn.ConvTranspose2d(zdim, nf*8, kernel_size=4, padding=0, bias=False),  # 1x1 -> 4x4
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(nf*8, nf*4, kernel_size=4, stride=2, padding=1, bias=False),  # 4x4 -> 8x8
+            nn.GroupNorm(16*4, nf*4),
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(nf*4, nf*2, kernel_size=4, stride=2, padding=1, bias=False),  # 8x8 -> 16x16
+            nn.GroupNorm(16*2, nf*2),
+            nn.ReLU(inplace=True)]
+        self.network = nn.Sequential(*network)
+
+        # ! only the symmetric confidence is required
+        # out_net1 = [
+        #     nn.ConvTranspose2d(nf*2, nf, kernel_size=4, stride=2, padding=1, bias=False),  # 16x16 -> 32x32
+        #     nn.GroupNorm(16, nf),
+        #     nn.ReLU(inplace=True),
+        #     nn.ConvTranspose2d(nf, nf, kernel_size=4, stride=2, padding=1, bias=False),  # 32x32 -> 64x64
+        #     nn.GroupNorm(16, nf),
+        #     nn.ReLU(inplace=True),
+        #     nn.Conv2d(nf, 2, kernel_size=5, stride=1, padding=2, bias=False),  # 64x64
+        #     # nn.Conv2d(nf, 1, kernel_size=5, stride=1, padding=2, bias=False),  # 64x64
+        #     nn.Softplus()
+        # ]
+        # self.out_net1 = nn.Sequential(*out_net1)
+
+        # ! for perceptual loss
+        out_net2 = [nn.Conv2d(nf*2, 2, kernel_size=3, stride=1, padding=1, bias=False),  # 16x16
+                    nn.Softplus()
+                    # nn.Sigmoid()
+                    ]
+        self.out_net2 = nn.Sequential(*out_net2)
+
+    def forward(self, input):
+        out = self.network(input)
+        # return self.out_net1(out)
+        return self.out_net2(out)
+        # return self.out_net1(out), self.out_net2(out)
\ No newline at end of file
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diff --git a/nsr/cvD/canoD.py b/nsr/cvD/canoD.py
new file mode 100644
index 0000000000000000000000000000000000000000..67aee15e9429743a64cc117b4641d70d2fc1df35
--- /dev/null
+++ b/nsr/cvD/canoD.py
@@ -0,0 +1,480 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from ..train_util import TrainLoopBasic, TrainLoop3DRec
+import vision_aided_loss
+from dnnlib.util import calculate_adaptive_weight
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+from ..train_util_cvD import TrainLoop3DcvD
+# from .nvD import
+
+
+class TrainLoop3DcvD_canoD(TrainLoop3DcvD):
+
+    def __init__(self,
+                 *,
+                 model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(model=model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         use_amp=use_amp, cvD_name='cano_cvD',
+                         **kwargs)
+
+        device = dist_util.dev()
+
+        # self.canonical_cvD = vision_aided_loss.Discriminator(
+        #     cv_type='clip', loss_type='multilevel_sigmoid_s',
+        #     device=device).to(device)
+        # self.canonical_cvD.cv_ensemble.requires_grad_(
+        #     False)  # Freeze feature extractor
+
+        # self._load_and_sync_parameters(model=self.canonical_cvD,
+        #                                model_name='cvD')
+
+        # self.mp_trainer_canonical_cvD = MixedPrecisionTrainer(
+        #     model=self.canonical_cvD,
+        #     use_fp16=self.use_fp16,
+        #     fp16_scale_growth=fp16_scale_growth,
+        #     model_name='canonical_cvD',
+        #     use_amp=use_amp)
+
+        # self.opt_cano_cvD = AdamW(
+        #     self.mp_trainer_canonical_cvD.master_params,
+        #     lr=1e-5,  # same as the G
+        #     betas=(0, 0.99),
+        #     eps=1e-8)  # dlr in biggan cfg
+
+        # if self.use_ddp:
+        #     self.ddp_canonical_cvD = DDP(
+        #         self.canonical_cvD,
+        #         device_ids=[dist_util.dev()],
+        #         output_device=dist_util.dev(),
+        #         broadcast_buffers=False,
+        #         bucket_cap_mb=128,
+        #         find_unused_parameters=False,
+        #     )
+        # else:
+        #     self.ddp_canonical_cvD = self.canonical_cvD
+
+        th.cuda.empty_cache()
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step_rec':
+            self.forward_G_rec(batch)
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        # elif step == 'g_step_nvs':
+        #     self.forward_G_nvs(batch)
+        #     took_step_g_nvs = self.mp_trainer.optimize(self.opt)
+
+        #     if took_step_g_nvs:
+        #         self._update_ema()  # g_ema
+
+        elif step == 'd_step':
+            self.forward_D(batch)
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            # _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        else:
+            return
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_rec')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_cvD, 'cano_cvD')
+                # self.save(self.mp_trainer_canonical_cvD, 'cano_cvD')
+
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save()
+                    self.save(self.mp_trainer_cvD, 'cano_cvD')
+                    # self.save(self.mp_trainer_canonical_cvD, 'cano_cvD')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def forward_D(self, batch):  # update D
+        # self.mp_trainer_canonical_cvD.zero_grad()
+        self.mp_trainer_cvD.zero_grad()
+
+        self.rec_model.requires_grad_(False)
+
+        # update two D
+        self.ddp_nvs_cvD.requires_grad_(True)
+        # self.ddp_canonical_cvD.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_cvD.use_amp):
+
+                novel_view_c = th.cat([
+                    micro['c'][batch_size // 2:], micro['c'][batch_size // 2:]
+                ])
+
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                # TODO, optimize with one encoder, and two triplane decoder
+                cano_pred = self.rec_model(latent=latent,
+                                           c=micro['c'],
+                                           behaviour='triplane_dec')
+
+                # nvs_pred = self.rec_model(latent=latent,
+                #                           c=novel_view_c,
+                #                           behaviour='triplane_dec')
+
+                # d_loss_nvs = self.run_D_Diter(
+                #     real=cano_pred['image_raw'],
+                #     fake=nvs_pred['image_raw'],
+                #     D=self.ddp_cvD)  # TODO, add SR for FFHQ
+
+                d_loss_cano = self.run_D_Diter(
+                    real=micro['img_to_encoder'],
+                    fake=cano_pred['image_raw'],
+                    D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+            # log_rec3d_loss_dict({'vision_aided_loss/D_nvs': d_loss_nvs})
+            log_rec3d_loss_dict({'vision_aided_loss/D_cano': d_loss_cano})
+
+            self.mp_trainer_cvD.backward(d_loss_cano)
+            # self.mp_trainer_cvD.backward(d_loss_nvs)
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        # self.ddp_canonical_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                pred_for_rec = pred
+
+                if last_batch or not self.use_ddp:
+                    loss, loss_dict = self.loss_class(pred_for_rec,
+                                                      target_for_rec,
+                                                      test_mode=False)
+                else:
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss, loss_dict = self.loss_class(pred_for_rec,
+                                                          target_for_rec,
+                                                          test_mode=False)
+
+                # add cvD supervision
+                vision_aided_loss = self.ddp_nvs_cvD(
+                    pred_for_rec['image_raw'],
+                    for_G=True).mean()  # [B, 1] shape
+
+                last_layer = self.rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                    -1].weight  # type: ignore
+
+                d_weight = calculate_adaptive_weight(
+                    loss, vision_aided_loss, last_layer,
+                    # disc_weight_max=1) * 1
+                    disc_weight_max=0.1) * 0.1
+                loss += vision_aided_loss * d_weight
+
+                loss_dict.update({
+                    'vision_aided_loss/G_rec': vision_aided_loss,
+                    'd_weight': d_weight
+                })
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(loss)  # no nvs cvD loss, following VQ3D
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        pred_img = th.cat(
+                            [self.pool_512(pred_img), pred['image_sr']],
+                            dim=-1)
+                        gt_img = th.cat(
+                            [self.pool_512(micro['img']), micro['img_sr']],
+                            dim=-1)
+                        pred_depth = self.pool_512(pred_depth)
+                        gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        # self.ddp_canonical_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)  # only use novel view D
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_cvD.use_amp):
+
+                pred_nv = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][batch_size // 2:],
+                        micro['c'][:batch_size // 2],
+                    ]))  # ! render novel views only for D loss
+
+                # add cvD supervision
+                vision_aided_loss = self.ddp_nvs_cvD(
+                    pred_nv['image_raw'], for_G=True).mean()  # [B, 1] shape
+
+                loss = vision_aided_loss * 0.1
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs':
+                    vision_aided_loss,
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred_nv['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred_nv['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred_nv:
+                        pred_img = th.cat(
+                            [self.pool_512(pred_img), pred_nv['image_sr']],
+                            dim=-1)
+                        gt_img = th.cat(
+                            [self.pool_512(micro['img']), micro['img_sr']],
+                            dim=-1)
+                        pred_depth = self.pool_512(pred_depth)
+                        gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # print(vis.shape)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
diff --git a/nsr/cvD/nvsD.py b/nsr/cvD/nvsD.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e2923d0376263f4c9c95259a71479ff488508e7
--- /dev/null
+++ b/nsr/cvD/nvsD.py
@@ -0,0 +1,538 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from ..train_util import TrainLoopBasic, TrainLoop3DRec
+import vision_aided_loss
+from dnnlib.util import calculate_adaptive_weight
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+from ..train_util_cvD import TrainLoop3DcvD
+
+
+class TrainLoop3DcvD_nvsD(TrainLoop3DcvD):
+
+    def __init__(self,
+                 *,
+                #  model,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         use_amp=use_amp,
+                         **kwargs)
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step_rec':
+            self.forward_G_rec(batch)
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'g_step_nvs':
+            self.forward_G_nvs(batch)
+            took_step_g_nvs = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_nvs:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step':
+            self.forward_D(batch)
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_rec') # pure VAE reconstruction
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_cvD, 'cvD')
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+                    self.save()
+                    self.save(self.mp_trainer_cvD, 'cvD')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_cvD, 'cvD')
+
+    def forward_D(self, batch):  # update D
+        self.rec_model.requires_grad_(False)
+
+        self.mp_trainer_cvD.zero_grad()
+        self.ddp_nvs_cvD.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_cvD.use_amp):
+
+                novel_view_c = th.cat([
+                    micro['c'][1:], micro['c'][:1]
+                ])
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                cano_pred = self.rec_model(latent=latent,
+                                           c=micro['c'],
+                                           behaviour='triplane_dec')
+
+                nvs_pred = self.rec_model(latent=latent,
+                                            c=novel_view_c,
+                                            behaviour='triplane_dec')
+
+                if 'image_sr' in nvs_pred:
+                    d_loss_nvs = self.run_D_Diter(
+                        real=th.cat([
+                            th.nn.functional.interpolate(
+                                cano_pred['image_raw'],
+                                size=cano_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            cano_pred['image_sr'],
+                        ], dim=1),
+                        fake=th.cat([
+                            th.nn.functional.interpolate(
+                                nvs_pred['image_raw'],
+                                size=nvs_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            nvs_pred['image_sr'],
+                        ], dim=1),
+                        D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                else:
+                    d_loss_nvs = self.run_D_Diter(
+                        real=cano_pred['image_raw'],
+                        fake=nvs_pred['image_raw'],
+                        D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                log_rec3d_loss_dict(
+                    {'vision_aided_loss/D_nvs': d_loss_nvs})
+                self.mp_trainer_cvD.backward(d_loss_nvs)
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                pred_for_rec = pred
+
+                if last_batch or not self.use_ddp:
+                    loss, loss_dict = self.loss_class(pred_for_rec,
+                                                      target_for_rec,
+                                                      test_mode=False)
+                else:
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss, loss_dict = self.loss_class(pred_for_rec,
+                                                          target_for_rec,
+                                                          test_mode=False)
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+        self.ddp_nvs_cvD.requires_grad_(False)  # only use novel view D
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                nvs_pred = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][1:],
+                        micro['c'][:1],
+                    ]))  # ! render novel views only for D loss
+
+                # add cvD supervision
+
+                if 'image_sr' in nvs_pred:
+                    # concat sr and raw results
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        th.cat([
+                            th.nn.functional.interpolate(
+                                nvs_pred['image_raw'],
+                                size=nvs_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            nvs_pred['image_sr'],
+                        ], dim=1),
+                        for_G=True).mean()  
+                else:
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        nvs_pred['image_raw'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                loss = vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs': loss
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = nvs_pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = nvs_pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in nvs_pred:
+
+                        if nvs_pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat([
+                                self.pool_512(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif nvs_pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat([
+                                self.pool_256(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat([
+                                self.pool_128(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+
+    def save(self, mp_trainer=None, model_name='rec'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        dist.barrier()
+
+    def _load_and_sync_parameters(self, model=None, model_name='rec'):
+        resume_checkpoint, self.resume_step = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.rec_model  # default model in the parent class
+
+        print(resume_checkpoint)
+
+        if resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                print(f'mark {model_name} loading ', flush=True)
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                print(f'mark {model_name} loading finished', flush=True)
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+                    elif 'IN' in k:
+                        print('ignore ', k)
+                    else:
+                        print('!!!! ignore key: ', k, ": ", v.size(),
+                              'shape in model: ', model_state_dict[k].size())
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            print(f'synced {model_name} params')
diff --git a/nsr/cvD/nvsD_canoD.py b/nsr/cvD/nvsD_canoD.py
new file mode 100644
index 0000000000000000000000000000000000000000..d59297b766dadbb72e9b9a1f23370a71fe1a4bbd
--- /dev/null
+++ b/nsr/cvD/nvsD_canoD.py
@@ -0,0 +1,1043 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from ..train_util import TrainLoopBasic, TrainLoop3DRec
+import vision_aided_loss
+from dnnlib.util import calculate_adaptive_weight
+
+def flip_yaw(pose_matrix):
+    flipped = pose_matrix.clone()
+    flipped[:, 0, 1] *= -1
+    flipped[:, 0, 2] *= -1
+    flipped[:, 1, 0] *= -1
+    flipped[:, 2, 0] *= -1
+    flipped[:, 0, 3] *= -1
+    # st()
+    return flipped
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+from ..train_util_cvD import TrainLoop3DcvD
+# from .nvD import
+
+
+class TrainLoop3DcvD_nvsD_canoD(TrainLoop3DcvD):
+    # class TrainLoop3DcvD_nvsD_canoD():
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         use_amp=use_amp,
+                         **kwargs)
+
+        device = dist_util.dev()
+
+        self.cano_cvD = vision_aided_loss.Discriminator(
+            cv_type='clip', loss_type='multilevel_sigmoid_s',
+            device=device).to(device)
+        self.cano_cvD.cv_ensemble.requires_grad_(
+            False)  # Freeze feature extractor
+        # self.cano_cvD.train()
+
+        cvD_model_params = list(self.cano_cvD.parameters())
+        SR_TRAINING = False
+        if SR_TRAINING:  # replace the conv1 with 6 channel input
+            # width, patch_size = self.nvs_cvD.cv_ensemble
+            vision_width, vision_patch_size = [
+                self.cano_cvD.cv_ensemble.models[0].model.conv1.weight.shape[k]
+                for k in [0, -1]
+            ]
+            self.cano_cvD.cv_ensemble.models[0].model.conv1 = th.nn.Conv2d(
+                in_channels=6,
+                out_channels=vision_width,
+                kernel_size=vision_patch_size,
+                stride=vision_patch_size,
+                bias=False).to(dist_util.dev())
+            cvD_model_params += list(
+                self.cano_cvD.cv_ensemble.models[0].model.conv1.parameters())
+
+            self.cano_cvD.cv_ensemble.models[
+                0].image_mean = self.cano_cvD.cv_ensemble.models[
+                    0].image_mean.repeat(2)
+            self.cano_cvD.cv_ensemble.models[
+                0].image_std = self.cano_cvD.cv_ensemble.models[
+                    0].image_std.repeat(2)
+
+        # logger.log(f'cano_cvD_model_params: {cvD_model_params}')
+
+        self._load_and_sync_parameters(model=self.cano_cvD,
+                                       model_name='cano_cvD')
+
+        self.mp_trainer_canonical_cvD = MixedPrecisionTrainer(
+            model=self.cano_cvD,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='canonical_cvD',
+            use_amp=use_amp,
+            model_params=cvD_model_params)
+
+        # cano_lr = 2e-5 * (lr / 1e-5) # D_lr=2e-4 in cvD by default
+        # cano_lr = 5e-5 * (lr / 1e-5) # D_lr=2e-4 in cvD by default
+        cano_lr = 2e-4 * (
+            lr / 1e-5)  # D_lr=2e-4 in cvD by default. 1e-4 still overfitting
+        self.opt_cano_cvD = AdamW(
+            self.mp_trainer_canonical_cvD.master_params,
+            lr=cano_lr,  # same as the G
+            betas=(0, 0.999),
+            eps=1e-8)  # dlr in biggan cfg
+
+        logger.log(f'cpt_cano_cvD lr: {cano_lr}')
+
+        if self.use_ddp:
+            self.ddp_cano_cvD = DDP(
+                self.cano_cvD,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            self.ddp_cano_cvD = self.cano_cvD
+
+        th.cuda.empty_cache()
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step_rec':
+            self.forward_G_rec(batch)
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step_rec':
+            self.forward_D(batch, behaviour='rec')
+            # _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        elif step == 'g_step_nvs':
+            self.forward_G_nvs(batch)
+            took_step_g_nvs = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_nvs:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step_nvs':
+            self.forward_D(batch, behaviour='nvs')
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            # _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            batch = next(self.data)
+
+            if self.novel_view_poses is None:
+                self.novel_view_poses = th.roll(batch['c'], 1, 0).to(
+                    dist_util.dev())  # save for eval visualization use
+
+            self.run_step(batch, 'g_step_rec')
+
+            # if self.step % 2 == 0:
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_rec')
+
+            # if self.step % 2 == 1:
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_nvs')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_cvD, self.mp_trainer_cvD.model_name)
+                self.save(self.mp_trainer_canonical_cvD,
+                          self.mp_trainer_canonical_cvD.model_name)
+
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save()
+                    self.save(self.mp_trainer_cvD,
+                              self.mp_trainer_cvD.model_name)
+                    self.save(self.mp_trainer_canonical_cvD,
+                              self.mp_trainer_canonical_cvD.model_name)
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def forward_D(self, batch, behaviour):  # update D
+        self.mp_trainer_canonical_cvD.zero_grad()
+        self.mp_trainer_cvD.zero_grad()
+
+        self.rec_model.requires_grad_(False)
+        # self.ddp_model.requires_grad_(False)
+
+        # update two D
+        if behaviour == 'nvs':
+            self.ddp_nvs_cvD.requires_grad_(True)
+            self.ddp_cano_cvD.requires_grad_(False)
+        else:  # update rec canonical D
+            self.ddp_nvs_cvD.requires_grad_(False)
+            self.ddp_cano_cvD.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_canonical_cvD.use_amp):
+
+                novel_view_c = th.cat([micro['c'][1:], micro['c'][:1]])
+
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                cano_pred = self.rec_model(latent=latent,
+                                           c=micro['c'],
+                                           behaviour='triplane_dec')
+
+                # TODO, optimize with one encoder, and two triplane decoder
+                # FIXME: quit autocast to runbackward
+                if behaviour == 'rec':
+
+                    if 'image_sr' in cano_pred:
+                        # try concat them in batch
+                        # d_loss = self.run_D_Diter(
+                        #     real=th.cat([
+                        #         th.nn.functional.interpolate(
+                        #             micro['img'],
+                        #             size=micro['img_sr'].shape[2:],
+                        #             mode='bilinear',
+                        #             align_corners=False,
+                        #             antialias=True),
+                        #         micro['img_sr'],
+                        #     ],
+                        #                 dim=1),
+                        #     fake=th.cat([
+                        #         th.nn.functional.interpolate(
+                        #             cano_pred['image_raw'],
+                        #             size=cano_pred['image_sr'].shape[2:],
+                        #             mode='bilinear',
+                        #             align_corners=False,
+                        #             antialias=True),
+                        #         cano_pred['image_sr'],
+                        #     ],
+                        #                 dim=1),
+                        #     D=self.ddp_cano_cvD)  # TODO, add SR for FFHQ
+
+                        d_loss = self.run_D_Diter(
+                            real= micro['img_sr'],
+                            fake=cano_pred['image_sr'],
+                            D=self.ddp_cano_cvD)  # TODO, add SR for FFHQ
+
+                    else:
+                        d_loss = self.run_D_Diter(
+                            real=micro['img'],
+                            fake=cano_pred['image_raw'],
+                            D=self.ddp_cano_cvD)  # TODO, add SR for FFHQ
+
+                    log_rec3d_loss_dict(
+                        {'vision_aided_loss/D_cano': d_loss})
+                    # self.mp_trainer_canonical_cvD.backward(d_loss)
+                else:
+                    assert behaviour == 'nvs'
+
+                    nvs_pred = self.rec_model(latent=latent,
+                                                c=novel_view_c,
+                                                behaviour='triplane_dec')
+
+                    if 'image_sr' in nvs_pred:
+
+                        d_loss = self.run_D_Diter(
+                            real=cano_pred['image_sr'],
+                            # th.cat([
+                                # th.nn.functional.interpolate(
+                                #     cano_pred['image_raw'],
+                                #     size=cano_pred['image_sr'].shape[2:],
+                                #     mode='bilinear',
+                                #     align_corners=False,
+                                #     antialias=True),
+                            # ],
+                                        # dim=1),
+                            fake= nvs_pred['image_sr'],
+                            # th.cat([
+                                # th.nn.functional.interpolate(
+                                #     nvs_pred['image_raw'],
+                                #     size=nvs_pred['image_sr'].shape[2:],
+                                #     mode='bilinear',
+                                #     align_corners=False,
+                                #     antialias=True),
+                            # ],
+                                        # dim=1),
+                            D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                    else:
+                        d_loss = self.run_D_Diter(
+                            real=cano_pred['image_raw'],
+                            fake=nvs_pred['image_raw'],
+                            D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                    log_rec3d_loss_dict(
+                        {'vision_aided_loss/D_nvs': d_loss})
+                    # self.mp_trainer_cvD.backward(d_loss)
+
+            if behaviour == 'rec':
+                self.mp_trainer_canonical_cvD.backward(d_loss)
+            else:
+                assert behaviour == 'nvs'
+                self.mp_trainer_cvD.backward(d_loss)
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                cano_pred = pred
+
+                # if last_batch or not self.use_ddp:
+                #     loss, loss_dict = self.loss_class(cano_pred,
+                #                                       target_for_rec,
+                #                                       test_mode=False,
+                #                                       step=self.step +
+                #                                       self.resume_step)
+                # else:
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, fg_mask = self.loss_class(cano_pred,
+                                                        target_for_rec,
+                                                        test_mode=False,
+                                                        step=self.step +
+                                                        self.resume_step, 
+                                                        return_fg_mask=True)
+
+                # cano_pred_img = cano_pred['image_raw']
+                
+                if self.loss_class.opt.symmetry_loss:
+                    pose, intrinsics = micro['c'][:, :16].reshape(
+                        -1, 4, 4), micro['c'][:, 16:]
+                    flipped_pose = flip_yaw(pose)
+                    mirror_c = th.cat(
+                        [flipped_pose.reshape(-1, 16), intrinsics], -1)
+
+                    nvs_pred = self.rec_model(latent={
+                        k: v
+                        for k, v in pred.items() if 'latent' in k
+                    },
+                                              c=mirror_c,
+                                              behaviour='triplane_dec',
+                                              return_raw_only=True)
+                    # cano_pred_img = th.cat([cano_pred_img, nvs_pred['image_raw']], 0)
+
+                    # concat data for supervision
+                    nvs_gt = {
+                        k: th.flip(target_for_rec[k], [-1])
+                        for k in
+                        ['img']  # fliplr leads to wrong color; B 3 H W shape
+                    }
+                    flipped_fg_mask = th.flip(fg_mask, [-1])
+                    if 'conf_sigma' in pred:
+                        conf_sigma = th.flip(pred['conf_sigma'], [-1])
+                        conf_sigma = th.nn.AdaptiveAvgPool2d(fg_mask.shape[-2:])(conf_sigma) # dynamically resize to target img size
+                    else:
+                        conf_sigma=None
+                    
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss_symm, loss_dict_symm = self.loss_class.calc_2d_rec_loss(
+                            nvs_pred['image_raw'],
+                            nvs_gt['img'],
+                            flipped_fg_mask,
+                            # test_mode=True,
+                            test_mode=False,
+                            step=self.step + self.resume_step,
+                            conf_sigma=conf_sigma,
+                        )
+                        loss += (loss_symm * 1.0) # as in unsup3d
+                        # if conf_sigma is not None:
+                        #     conf_loss = th.nn.functional.mse_loss(conf_sigma, flipped_fg_mask) * 0.2
+                        #     loss += conf_loss # a log that regularizes all confidence to 1
+                        #     loss_dict[f'conf_loss'] = conf_loss
+                        for k, v in loss_dict_symm.items():
+                            loss_dict[f'{k}_symm'] = v
+
+
+                # add cvD supervision
+                # ! TODO
+
+                if 'image_sr' in cano_pred:
+                    # concat both resolution
+                    # vision_aided_loss = self.ddp_cano_cvD(
+                    #     th.cat([
+                    #         th.nn.functional.interpolate(
+                    #             cano_pred['image_raw'],
+                    #             size=cano_pred['image_sr'].shape[2:],
+                    #             mode='bilinear',
+                    #             align_corners=False,
+                    #             antialias=True),
+                    #         cano_pred['image_sr'],
+                    #     ],
+                    #            dim=1),  # 6 channel input
+                    #     for_G=True).mean()  # [B, 1] shape
+
+                    vision_aided_loss = self.ddp_cano_cvD(
+                            cano_pred['image_sr'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                else:
+                    vision_aided_loss = self.ddp_cano_cvD(
+                        cano_pred['image_raw'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                # last_layer = self.rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                #     -1].weight  # type: ignore
+
+                d_weight = th.tensor(self.loss_class.opt.rec_cvD_lambda).to(
+                    dist_util.dev())
+                # d_weight = calculate_adaptive_weight(
+                #     loss,
+                #     vision_aided_loss,
+                #     last_layer,
+                #     disc_weight_max=0.1) * self.loss_class.opt.rec_cvD_lambda
+                loss += vision_aided_loss * d_weight
+
+                loss_dict.update({
+                    'vision_aided_loss/G_rec':
+                    (vision_aided_loss * d_weight).detach(),
+                    'd_weight':
+                    d_weight
+                })
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(
+                loss)  # no nvs cvD loss, following VQ3D
+
+            # DDP some parameters no grad:
+            # for name, p in self.ddp_model.named_parameters():
+            #     if p.grad is None:
+            #         print(f"(in rec)found rec unused param: {name}")
+
+            # ! move to other places, add tensorboard
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            #     with th.no_grad():
+            #         # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+            #         gt_depth = micro['depth']
+            #         if gt_depth.ndim == 3:
+            #             gt_depth = gt_depth.unsqueeze(1)
+            #         gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+            #                                                   gt_depth.min())
+            #         # if True:
+            #         pred_depth = pred['image_depth']
+            #         pred_depth = (pred_depth - pred_depth.min()) / (
+            #             pred_depth.max() - pred_depth.min())
+            #         pred_img = pred['image_raw']
+            #         gt_img = micro['img']
+
+            #         if 'image_sr' in pred:
+            #             if pred['image_sr'].shape[-1] == 512:
+            #                 pred_img = th.cat(
+            #                     [self.pool_512(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_512(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 pred_depth = self.pool_512(pred_depth)
+            #                 gt_depth = self.pool_512(gt_depth)
+
+            #             elif pred['image_sr'].shape[-1] == 256:
+            #                 pred_img = th.cat(
+            #                     [self.pool_256(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_256(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 pred_depth = self.pool_256(pred_depth)
+            #                 gt_depth = self.pool_256(gt_depth)
+
+            #             else:
+            #                 pred_img = th.cat(
+            #                     [self.pool_128(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_128(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 gt_depth = self.pool_128(gt_depth)
+            #                 pred_depth = self.pool_128(pred_depth)
+            #         else:
+            #             gt_img = self.pool_64(gt_img)
+            #             gt_depth = self.pool_64(gt_depth)
+
+            #         gt_vis = th.cat(
+            #             [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+            #             dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+            #         pred_vis = th.cat(
+            #             [pred_img,
+            #              pred_depth.repeat_interleave(3, dim=1)],
+            #             dim=-1)  # B, 3, H, W
+
+            #         vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+            #             1, 2, 0).cpu()  # ! pred in range[-1, 1]
+            #         # vis_grid = torchvision.utils.make_grid(vis) # HWC
+            #         vis = vis.numpy() * 127.5 + 127.5
+            #         vis = vis.clip(0, 255).astype(np.uint8)
+            #         Image.fromarray(vis).save(
+            #             f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+            #         )
+            #         print(
+            #             'log vis to: ',
+            #             f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+            #         )
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    def norm_depth(pred_depth): # to [-1,1]
+                        # pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                        return -(pred_depth * 2 - 1)
+
+                    pred_img = pred['image_raw'].clip(-1,1)
+                    gt_img = micro['img']
+
+                    # infer novel view also
+                    pred_nv_img = self.rec_model(
+                        img=micro['img_to_encoder'],
+                        c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+                    # if 'depth' in micro:
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = norm_depth(gt_depth)
+                    # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                    #                                           gt_depth.min())
+                    # if True:
+                    if 'image_depth' in pred:
+                        # pred_depth = pred['image_depth']
+                        # pred_depth = (pred_depth - pred_depth.min()) / (
+                        #     pred_depth.max() - pred_depth.min())
+                        pred_depth = norm_depth(pred['image_depth'])
+                        pred_nv_depth = norm_depth(
+                            pred_nv_img['image_depth'])
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+                        pred_nv_depth = th.zeros_like(gt_depth)
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            pred_nv_depth = self.pool_512(pred_nv_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                            pred_vis_nv = th.cat(
+                                [self.pool_512(pred_nv_img['image_raw']), pred_nv_img['image_sr']],
+                                dim=-1)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    if gt_img.shape[-1] == 64:
+                        gt_depth = self.pool_64(gt_depth)
+                    elif gt_img.shape[-1] == 128:
+                        gt_depth = self.pool_128(gt_depth)
+                    # else:
+                        # gt_depth = self.pool_64(gt_depth)
+
+                    # st()
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    pred_vis_nv = th.cat([
+                        # pred_nv_img['image_raw'].clip(-1,1),
+                        pred_vis_nv,
+                        pred_nv_depth.repeat_interleave(3, dim=1)
+                    ],
+                                         dim=-1)  # B, 3, H, W
+                    pred_vis = th.cat([pred_vis, pred_vis_nv],
+                                      dim=-2)  # cat in H dim
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+                    # .permute(
+                    #     0, 2, 3, 1).cpu()
+                    vis_tensor = torchvision.utils.make_grid(
+                        vis, nrow=vis.shape[-1] // 64)  # HWC
+                    torchvision.utils.save_image(
+                        vis_tensor,
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg', normalize=True, value_range=(-1,1))
+                    # vis = vis.numpy() * 127.5 + 127.5
+                    # vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # Image.fromarray(vis).save(
+                    #     f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)  # only use novel view D
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                nvs_pred = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][1:],
+                        micro['c'][:1],
+                    ]))  # ! render novel views only for D loss
+
+                # add cvD supervision
+
+                if 'image_sr' in nvs_pred:
+                    # concat sr and raw results
+                    # vision_aided_loss = self.ddp_nvs_cvD(
+                    #     # pred_nv['image_sr'],
+                    #     # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                    #     th.cat([
+                    #         th.nn.functional.interpolate(
+                    #             nvs_pred['image_raw'],
+                    #             size=nvs_pred['image_sr'].shape[2:],
+                    #             mode='bilinear',
+                    #             align_corners=False,
+                    #             antialias=True),
+                    #         nvs_pred['image_sr'],
+                    #     ],
+                    #            dim=1),
+                    #     for_G=True).mean()  # ! for debugging
+
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        # pred_nv['image_sr'],
+                        # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                        nvs_pred['image_sr'],
+                        for_G=True).mean()  # ! for debugging
+
+
+                    # supervise sr only
+                    # vision_aided_loss = self.ddp_nvs_cvD(
+                    #     # pred_nv['image_sr'],
+                    #     # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                    #     th.cat([nvs_pred['image_sr'],
+                    #     th.nn.functional.interpolate(nvs_pred['image_raw'], size=nvs_pred['image_sr'].shape[2:], mode='bilinear',
+                    #                         align_corners=False,
+                    #                         antialias=True),]),
+                    #     for_G=True).mean()  # ! for debugging
+
+                    # pred_nv['image_raw'], for_G=True).mean()  # [B, 1] shape
+                else:
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        nvs_pred['image_raw'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                loss = vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs': loss
+                    # vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda,
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            if dist_util.get_rank() == 0 and self.step % 500 == 1:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    def norm_depth(pred_depth): # to [-1,1]
+                        # pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                        return -(pred_depth * 2 - 1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = norm_depth(gt_depth)
+
+                    # if True:
+                    # pred_depth = nvs_pred['image_depth']
+                    # pred_depth = (pred_depth - pred_depth.min()) / (
+                    #     pred_depth.max() - pred_depth.min())
+                    pred_depth = norm_depth(nvs_pred['image_depth'])
+                    pred_img = nvs_pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in nvs_pred:
+
+                        if nvs_pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat([
+                                self.pool_512(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif nvs_pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat([
+                                self.pool_256(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat([
+                                self.pool_128(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+
+                    if gt_img.shape[-1] == 64:
+                        gt_depth = self.pool_64(gt_depth)
+                    elif gt_img.shape[-1] == 128:
+                        gt_depth = self.pool_128(gt_depth)
+
+                    # else:
+                    #     gt_img = self.pool_64(gt_img)
+                    #     gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # print(vis.shape)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+
+class TrainLoop3DcvD_nvsD_canoD_eg3d(TrainLoop3DcvD_nvsD_canoD):
+    def __init__(self, *, rec_model, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, load_submodule_name='', ignore_resume_opt=False, use_amp=False, **kwargs):
+        super().__init__(rec_model=rec_model, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, load_submodule_name=load_submodule_name, ignore_resume_opt=ignore_resume_opt, use_amp=use_amp, **kwargs)
+        self.rendering_kwargs = self.rec_model.module.decoder.triplane_decoder.rendering_kwargs # type: ignore
+        self._prepare_nvs_pose() # for eval novelview visualization
+
+    @th.inference_mode()
+    def eval_novelview_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            video_out = imageio.get_writer(
+                f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}_batch_{i}.mp4',
+                mode='I',
+                fps=60,
+                codec='libx264')
+
+            for idx, c in enumerate(self.all_nvs_params):
+                pred = self.rec_model(img=micro['img_to_encoder'],
+                                    c=c.unsqueeze(0).repeat_interleave(micro['img'].shape[0], 0))  # pred: (B, 3, 64, 64)
+                                    #   c=micro['c'])  # pred: (B, 3, 64, 64)
+
+                # normalize depth
+                # if True:
+                pred_depth = pred['image_depth']
+                pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                                pred_depth.min())
+                if 'image_sr' in pred:
+
+                    if pred['image_sr'].shape[-1] == 512:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_512(pred['image_raw']), pred['image_sr'],
+                            self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                        ],
+                                        dim=-1)
+
+                    elif pred['image_sr'].shape[-1] == 256:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_256(pred['image_raw']), pred['image_sr'],
+                            self.pool_256(pred_depth).repeat_interleave(3, dim=1)
+                        ],
+                                        dim=-1)
+
+                    else:
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_128(pred['image_raw']),
+                            self.pool_128(pred['image_sr']),
+                            self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                        ],
+                                        dim=-1)
+
+                else:
+
+                    # st()
+                    pred_vis = th.cat([
+                        self.pool_128(micro['img']), 
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                    dim=-1)  # B, 3, H, W
+
+                    # ! cooncat h dim
+                    pred_vis = pred_vis.permute(0,2,3,1).flatten(0,1) # H W 3
+
+                # vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+                # vis = pred_vis.permute(1,2,0).cpu().numpy()
+                vis = pred_vis.cpu().numpy()
+                vis = vis * 127.5 + 127.5
+                vis = vis.clip(0, 255).astype(np.uint8)
+
+                # for j in range(vis.shape[0]):
+                    # video_out.append_data(vis[j])
+                video_out.append_data(vis)
+
+            video_out.close()
+
+        th.cuda.empty_cache()
+
+    
+    def _prepare_nvs_pose(self):
+        from nsr.camera_utils import LookAtPoseSampler, FOV_to_intrinsics
+
+        device = dist_util.dev()
+        
+        fov_deg = 18.837 # for ffhq/afhq
+        intrinsics = FOV_to_intrinsics(fov_deg, device=device)
+
+        all_nvs_params = []
+
+        pitch_range = 0.25
+        yaw_range = 0.35
+        num_keyframes = 10 # how many nv poses to sample from
+        w_frames = 1
+
+        cam_pivot = th.Tensor(self.rendering_kwargs.get('avg_camera_pivot')).to(device)
+        cam_radius = self.rendering_kwargs.get('avg_camera_radius')
+
+        for frame_idx in range(num_keyframes):
+
+            cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
+                                                    3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
+                                                    cam_pivot, radius=cam_radius, device=device)
+
+            camera_params = th.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
+
+            all_nvs_params.append(camera_params)
+        
+        self.all_nvs_params = th.cat(all_nvs_params, 0)
\ No newline at end of file
diff --git a/nsr/cvD/nvsD_canoD_mask.py b/nsr/cvD/nvsD_canoD_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b1cb4ee9bbf9b7ade208c53ba0b5d628cdd1a4d
--- /dev/null
+++ b/nsr/cvD/nvsD_canoD_mask.py
@@ -0,0 +1,835 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from ..train_util import TrainLoopBasic, TrainLoop3DRec
+import vision_aided_loss
+from dnnlib.util import calculate_adaptive_weight
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+from ..train_util_cvD import TrainLoop3DcvD
+# from .nvD import
+
+
+class TrainLoop3DcvD_nvsD_canoD_canomask(TrainLoop3DcvD):
+    # class TrainLoop3DcvD_nvsD_canoD():
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         use_amp=use_amp,
+                         **kwargs)
+
+        device = dist_util.dev()
+
+        self.cano_cvD = vision_aided_loss.Discriminator(
+            cv_type='clip', loss_type='multilevel_sigmoid_s',
+            device=device).to(device)
+        self.cano_cvD.cv_ensemble.requires_grad_(
+            False)  # Freeze feature extractor
+        # self.cano_cvD.train()
+
+        cvD_model_params = list(self.cano_cvD.parameters())
+        SR_TRAINING = False
+        if SR_TRAINING:  # replace the conv1 with 6 channel input
+            # width, patch_size = self.nvs_cvD.cv_ensemble
+            vision_width, vision_patch_size = [
+                self.cano_cvD.cv_ensemble.models[0].model.conv1.weight.shape[k]
+                for k in [0, -1]
+            ]
+            self.cano_cvD.cv_ensemble.models[0].model.conv1 = th.nn.Conv2d(
+                in_channels=6,
+                out_channels=vision_width,
+                kernel_size=vision_patch_size,
+                stride=vision_patch_size,
+                bias=False).to(dist_util.dev())
+            cvD_model_params += list(
+                self.cano_cvD.cv_ensemble.models[0].model.conv1.parameters())
+
+            self.cano_cvD.cv_ensemble.models[
+                0].image_mean = self.cano_cvD.cv_ensemble.models[
+                    0].image_mean.repeat(2)
+            self.cano_cvD.cv_ensemble.models[
+                0].image_std = self.cano_cvD.cv_ensemble.models[
+                    0].image_std.repeat(2)
+
+        # logger.log(f'cano_cvD_model_params: {cvD_model_params}')
+
+        self._load_and_sync_parameters(model=self.cano_cvD,
+                                       model_name='cano_cvD')
+
+        self.mp_trainer_canonical_cvD = MixedPrecisionTrainer(
+            model=self.cano_cvD,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='canonical_cvD',
+            use_amp=use_amp,
+            model_params=cvD_model_params)
+
+        # cano_lr = 2e-5 * (lr / 1e-5) # D_lr=2e-4 in cvD by default
+        # cano_lr = 5e-5 * (lr / 1e-5) # D_lr=2e-4 in cvD by default
+        cano_lr = 2e-4 * (
+            lr / 1e-5)  # D_lr=2e-4 in cvD by default. 1e-4 still overfitting
+        self.opt_cano_cvD = AdamW(
+            self.mp_trainer_canonical_cvD.master_params,
+            lr=cano_lr,  # same as the G
+            betas=(0, 0.999),
+            eps=1e-8)  # dlr in biggan cfg
+
+        logger.log(f'cpt_cano_cvD lr: {cano_lr}')
+
+        if self.use_ddp:
+            self.ddp_cano_cvD = DDP(
+                self.cano_cvD,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            self.ddp_cano_cvD = self.cano_cvD
+
+        th.cuda.empty_cache()
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step_rec':
+            self.forward_G_rec(batch)
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step_rec':
+            self.forward_D(batch, behaviour='rec')
+            # _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        elif step == 'g_step_nvs':
+            self.forward_G_nvs(batch)
+            took_step_g_nvs = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_nvs:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step_nvs':
+            self.forward_D(batch, behaviour='nvs')
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            # _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            batch = next(self.data)
+
+            if self.novel_view_poses is None:
+                self.novel_view_poses = th.roll(batch['c'], 1, 0).to(
+                    dist_util.dev())  # save for eval visualization use
+
+            self.run_step(batch, 'g_step_rec')
+
+            # if self.step % 2 == 0:
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_rec')
+
+            # if self.step % 2 == 1:
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_nvs')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_cvD, self.mp_trainer_cvD.model_name)
+                self.save(self.mp_trainer_canonical_cvD,
+                          self.mp_trainer_canonical_cvD.model_name)
+
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save()
+                    self.save(self.mp_trainer_cvD,
+                              self.mp_trainer_cvD.model_name)
+                    self.save(self.mp_trainer_canonical_cvD,
+                              self.mp_trainer_canonical_cvD.model_name)
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def forward_D(self, batch, behaviour):  # update D
+        self.mp_trainer_canonical_cvD.zero_grad()
+        self.mp_trainer_cvD.zero_grad()
+
+        self.rec_model.requires_grad_(False)
+        # self.ddp_model.requires_grad_(False)
+
+        # update two D
+        if behaviour == 'nvs':
+            self.ddp_nvs_cvD.requires_grad_(True)
+            self.ddp_cano_cvD.requires_grad_(False)
+        else:  # update rec canonical D
+            self.ddp_nvs_cvD.requires_grad_(False)
+            self.ddp_cano_cvD.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_canonical_cvD.use_amp):
+
+                novel_view_c = th.cat([micro['c'][1:], micro['c'][:1]])
+
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                cano_pred = self.rec_model(latent=latent,
+                                           c=micro['c'],
+                                           behaviour='triplane_dec')
+
+                # TODO, optimize with one encoder, and two triplane decoder
+                if behaviour == 'rec':
+
+                    if 'image_sr' in cano_pred:
+                        # d_loss_cano = self.run_D_Diter(
+                        #     # real=micro['img_sr'],
+                        #     # fake=cano_pred['image_sr'],
+                        #     real=0.5 * micro['img_sr'] + 0.5 * th.nn.functional.interpolate(micro['img'], size=micro['img_sr'].shape[2:], mode='bilinear'),
+                        #     fake=0.5 * cano_pred['image_sr'] + 0.5 * th.nn.functional.interpolate(cano_pred['image_raw'], size=cano_pred['image_sr'].shape[2:], mode='bilinear'),
+                        #     D=self.ddp_canonical_cvD)  # ! failed, color bias
+
+                        # try concat them in batch
+                        d_loss_cano = self.run_D_Diter(
+                            real=th.cat([
+                                th.nn.functional.interpolate(
+                                    micro['img'],
+                                    size=micro['img_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                micro['img_sr'],
+                            ],
+                                        dim=1),
+                            fake=th.cat([
+                                th.nn.functional.interpolate(
+                                    cano_pred['image_raw'],
+                                    size=cano_pred['image_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                cano_pred['image_sr'],
+                            ],
+                                        dim=1),
+                            D=self.ddp_cano_cvD)  # TODO, add SR for FFHQ
+
+                    else:
+                        d_loss_cano = self.run_D_Diter(
+                            real=micro['img'],
+                            fake=cano_pred['image_raw'],
+                            D=self.ddp_cano_cvD)  # TODO, add SR for FFHQ
+
+                    log_rec3d_loss_dict(
+                        {'vision_aided_loss/D_cano': d_loss_cano})
+                    self.mp_trainer_canonical_cvD.backward(d_loss_cano)
+                else:
+                    assert behaviour == 'nvs'
+
+                    nvs_pred = self.rec_model(latent=latent,
+                                              c=novel_view_c,
+                                              behaviour='triplane_dec')
+
+                    if 'image_sr' in nvs_pred:
+                        # d_loss_nvs = self.run_D_Diter(
+                        #     # real=cano_pred['image_sr'],
+                        #     # fake=nvs_pred['image_sr'],
+                        #     real=0.5 * cano_pred['image_sr'] + 0.5 * th.nn.functional.interpolate(cano_pred['image_raw'], size=cano_pred['image_sr'].shape[2:], mode='bilinear'),
+                        #     fake=0.5 * nvs_pred['image_sr'] + 0.5 * th.nn.functional.interpolate(nvs_pred['image_raw'], size=nvs_pred['image_sr'].shape[2:], mode='bilinear'),
+                        #     D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                        d_loss_nvs = self.run_D_Diter(
+                            real=th.cat([
+                                th.nn.functional.interpolate(
+                                    cano_pred['image_raw'],
+                                    size=cano_pred['image_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                cano_pred['image_sr'],
+                            ],
+                                        dim=1),
+                            fake=th.cat([
+                                th.nn.functional.interpolate(
+                                    nvs_pred['image_raw'],
+                                    size=nvs_pred['image_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                nvs_pred['image_sr'],
+                            ],
+                                        dim=1),
+                            D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                    else:
+                        d_loss_nvs = self.run_D_Diter(
+                            real=cano_pred['silhouette_normalized_3channel'],
+                            fake=nvs_pred['silhouette_normalized_3channel'],
+                            D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                    log_rec3d_loss_dict(
+                        {'vision_aided_loss/D_nvs_silhouette': d_loss_nvs})
+                    self.mp_trainer_cvD.backward(d_loss_nvs)
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                cano_pred = pred
+
+                if last_batch or not self.use_ddp:
+                    loss, loss_dict = self.loss_class(cano_pred,
+                                                      target_for_rec,
+                                                      test_mode=False,
+                                                      step=self.step +
+                                                      self.resume_step)
+                else:
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss, loss_dict = self.loss_class(cano_pred,
+                                                          target_for_rec,
+                                                          test_mode=False,
+                                                          step=self.step +
+                                                          self.resume_step)
+
+                # add cvD supervision
+                # ! TODO
+
+                if 'image_sr' in cano_pred:
+                    # concat both resolution
+                    vision_aided_loss = self.ddp_cano_cvD(
+                        th.cat([
+                            th.nn.functional.interpolate(
+                                cano_pred['image_raw'],
+                                size=cano_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            cano_pred['image_sr'],
+                        ],
+                               dim=1),  # 6 channel input
+                        for_G=True).mean()  # [B, 1] shape
+
+                else:
+                    vision_aided_loss = self.ddp_cano_cvD(
+                        cano_pred['image_raw'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                # last_layer = self.rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                #     -1].weight  # type: ignore
+
+                d_weight = th.tensor(self.loss_class.opt.rec_cvD_lambda).to(
+                    dist_util.dev())
+                # d_weight = calculate_adaptive_weight(
+                #     loss,
+                #     vision_aided_loss,
+                #     last_layer,
+                #     disc_weight_max=0.1) * self.loss_class.opt.rec_cvD_lambda
+                loss += vision_aided_loss * d_weight
+
+                loss_dict.update({
+                    'vision_aided_loss/G_rec':
+                    (vision_aided_loss * d_weight).detach(),
+                    'd_weight':
+                    d_weight
+                })
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(
+                loss)  # no nvs cvD loss, following VQ3D
+
+            # DDP some parameters no grad:
+            # for name, p in self.ddp_model.named_parameters():
+            #     if p.grad is None:
+            #         print(f"(in rec)found rec unused param: {name}")
+
+            # ! move to other places, add tensorboard
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            #     with th.no_grad():
+            #         # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+            #         gt_depth = micro['depth']
+            #         if gt_depth.ndim == 3:
+            #             gt_depth = gt_depth.unsqueeze(1)
+            #         gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+            #                                                   gt_depth.min())
+            #         # if True:
+            #         pred_depth = pred['image_depth']
+            #         pred_depth = (pred_depth - pred_depth.min()) / (
+            #             pred_depth.max() - pred_depth.min())
+            #         pred_img = pred['image_raw']
+            #         gt_img = micro['img']
+
+            #         if 'image_sr' in pred:
+            #             if pred['image_sr'].shape[-1] == 512:
+            #                 pred_img = th.cat(
+            #                     [self.pool_512(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_512(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 pred_depth = self.pool_512(pred_depth)
+            #                 gt_depth = self.pool_512(gt_depth)
+
+            #             elif pred['image_sr'].shape[-1] == 256:
+            #                 pred_img = th.cat(
+            #                     [self.pool_256(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_256(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 pred_depth = self.pool_256(pred_depth)
+            #                 gt_depth = self.pool_256(gt_depth)
+
+            #             else:
+            #                 pred_img = th.cat(
+            #                     [self.pool_128(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_128(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 gt_depth = self.pool_128(gt_depth)
+            #                 pred_depth = self.pool_128(pred_depth)
+            #         else:
+            #             gt_img = self.pool_64(gt_img)
+            #             gt_depth = self.pool_64(gt_depth)
+
+            #         gt_vis = th.cat(
+            #             [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+            #             dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+            #         pred_vis = th.cat(
+            #             [pred_img,
+            #              pred_depth.repeat_interleave(3, dim=1)],
+            #             dim=-1)  # B, 3, H, W
+
+            #         vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+            #             1, 2, 0).cpu()  # ! pred in range[-1, 1]
+            #         # vis_grid = torchvision.utils.make_grid(vis) # HWC
+            #         vis = vis.numpy() * 127.5 + 127.5
+            #         vis = vis.clip(0, 255).astype(np.uint8)
+            #         Image.fromarray(vis).save(
+            #             f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+            #         )
+            #         print(
+            #             'log vis to: ',
+            #             f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+            #         )
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    def norm_depth(pred_depth):
+                        # pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                        return pred_depth
+
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    # infer novel view also
+                    pred_nv_img = self.rec_model(
+                        img=micro['img_to_encoder'],
+                        c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+                    # if 'depth' in micro:
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = norm_depth(gt_depth)
+                    # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                    #                                           gt_depth.min())
+                    # if True:
+                    if 'image_depth' in pred:
+                        # pred_depth = pred['image_depth']
+                        # pred_depth = (pred_depth - pred_depth.min()) / (
+                        #     pred_depth.max() - pred_depth.min())
+                        pred_depth = norm_depth(pred['image_depth'])
+                        pred_nv_depth = norm_depth(
+                            pred_nv_img['image_depth'])
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+                        pred_nv_depth = th.zeros_like(gt_depth)
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_img = self.pool_64(gt_img)
+                        gt_depth = self.pool_64(gt_depth)
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    pred_vis_nv = th.cat([
+                        pred_nv_img['image_raw'],
+                        pred_nv_depth.repeat_interleave(3, dim=1)
+                    ],
+                                         dim=-1)  # B, 3, H, W
+                    pred_vis = th.cat([pred_vis, pred_vis_nv],
+                                      dim=-2)  # cat in H dim
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+                    # .permute(
+                    #     0, 2, 3, 1).cpu()
+                    vis_tensor = torchvision.utils.make_grid(
+                        vis, nrow=vis.shape[-1] // 64)  # HWC
+                    torchvision.utils.save_image(
+                        vis_tensor,
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+                    # vis = vis.numpy() * 127.5 + 127.5
+                    # vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # Image.fromarray(vis).save(
+                    #     f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)  # only use novel view D
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                nvs_pred = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][1:],
+                        micro['c'][:1],
+                    ]))  # ! render novel views only for D loss
+
+                # add cvD supervision
+
+                if 'image_sr' in nvs_pred:
+                    # concat sr and raw results
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        # pred_nv['image_sr'],
+                        # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                        th.cat([
+                            th.nn.functional.interpolate(
+                                nvs_pred['image_raw'],
+                                size=nvs_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            nvs_pred['image_sr'],
+                        ],
+                               dim=1),
+                        for_G=True).mean()  # ! for debugging
+
+                    # supervise sr only
+                    # vision_aided_loss = self.ddp_nvs_cvD(
+                    #     # pred_nv['image_sr'],
+                    #     # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                    #     th.cat([nvs_pred['image_sr'],
+                    #     th.nn.functional.interpolate(nvs_pred['image_raw'], size=nvs_pred['image_sr'].shape[2:], mode='bilinear',
+                    #                         align_corners=False,
+                    #                         antialias=True),]),
+                    #     for_G=True).mean()  # ! for debugging
+
+                    # pred_nv['image_raw'], for_G=True).mean()  # [B, 1] shape
+                else:
+                    # vision_aided_loss = self.ddp_nvs_cvD(
+                    #     nvs_pred['image_raw'],
+                    #     for_G=True).mean()  # [B, 1] shape
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        nvs_pred['silhouette_normalized_3channel'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                loss = vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs_silhouette': loss
+                    # vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda,
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            if dist_util.get_rank() == 0 and self.step % 500 == 1:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = nvs_pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = nvs_pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in nvs_pred:
+
+                        if nvs_pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat([
+                                self.pool_512(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif nvs_pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat([
+                                self.pool_256(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat([
+                                self.pool_128(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    else:
+                        gt_img = self.pool_64(gt_img)
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # print(vis.shape)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
diff --git a/nsr/cvD/nvsD_canoD_multiview.py b/nsr/cvD/nvsD_canoD_multiview.py
new file mode 100644
index 0000000000000000000000000000000000000000..16b6e56185127679907652b32b008355c35b6f78
--- /dev/null
+++ b/nsr/cvD/nvsD_canoD_multiview.py
@@ -0,0 +1,551 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from .nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+
+class TrainLoop3DcvD_nvsD_canoD_multiview(TrainLoop3DcvD_nvsD_canoD):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         use_amp=use_amp,
+                         **kwargs)
+        assert not self.mp_trainer_rec.use_amp, 'amp may lead to grad nan?'
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        target_cano = {}
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            for k, v in micro.items():
+                if k[:2] == 'nv':
+                    orig_key = k.replace('nv_', '')
+                    # target_nvs[orig_key] = v
+                    target_cano[orig_key] = micro[orig_key]
+
+            # last_batch = (i + self.microbatch) >= batch_size
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                cano_pred = pred
+
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, fg_mask = self.loss_class(
+                        cano_pred,
+                        target_for_rec,
+                        test_mode=False,
+                        step=self.step + self.resume_step,
+                        return_fg_mask=True)
+
+                if 'image_sr' in cano_pred:
+                    raise NotImplementedError()
+                    # concat both resolution
+                    vision_aided_loss = self.ddp_cano_cvD(
+                        th.cat([
+                            th.nn.functional.interpolate(
+                                cano_pred['image_raw'],
+                                size=cano_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            cano_pred['image_sr'],
+                        ],
+                               dim=1),  # 6 channel input
+                        for_G=True).mean()  # [B, 1] shape
+
+                else:
+                    vision_aided_loss = self.ddp_cano_cvD(
+                        cano_pred['image_raw'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                # last_layer = self.rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                #     -1].weight  # type: ignore
+
+                d_weight = th.tensor(self.loss_class.opt.rec_cvD_lambda).to(
+                    dist_util.dev())
+                # d_weight = calculate_adaptive_weight(
+                #     loss,
+                #     vision_aided_loss,
+                #     last_layer,
+                #     disc_weight_max=0.1) * self.loss_class.opt.rec_cvD_lambda
+                loss += vision_aided_loss * d_weight
+
+                loss_dict.update({
+                    'vision_aided_loss/G_rec':
+                    (vision_aided_loss * d_weight).detach(),
+                    'd_weight':
+                    d_weight
+                })
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(
+                loss)  # no nvs cvD loss, following VQ3D
+
+            # DDP some parameters no grad:
+            # for name, p in self.ddp_model.named_parameters():
+            #     if p.grad is None:
+            #         print(f"(in rec)found rec unused param: {name}")
+
+            # ! move to other places, add tensorboard
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            #     with th.no_grad():
+            #         # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+            #         gt_depth = micro['depth']
+            #         if gt_depth.ndim == 3:
+            #             gt_depth = gt_depth.unsqueeze(1)
+            #         gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+            #                                                   gt_depth.min())
+            #         # if True:
+            #         pred_depth = pred['image_depth']
+            #         pred_depth = (pred_depth - pred_depth.min()) / (
+            #             pred_depth.max() - pred_depth.min())
+            #         pred_img = pred['image_raw']
+            #         gt_img = micro['img']
+
+            #         if 'image_sr' in pred:
+            #             if pred['image_sr'].shape[-1] == 512:
+            #                 pred_img = th.cat(
+            #                     [self.pool_512(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_512(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 pred_depth = self.pool_512(pred_depth)
+            #                 gt_depth = self.pool_512(gt_depth)
+
+            #             elif pred['image_sr'].shape[-1] == 256:
+            #                 pred_img = th.cat(
+            #                     [self.pool_256(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_256(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 pred_depth = self.pool_256(pred_depth)
+            #                 gt_depth = self.pool_256(gt_depth)
+
+            #             else:
+            #                 pred_img = th.cat(
+            #                     [self.pool_128(pred_img), pred['image_sr']],
+            #                     dim=-1)
+            #                 gt_img = th.cat(
+            #                     [self.pool_128(micro['img']), micro['img_sr']],
+            #                     dim=-1)
+            #                 gt_depth = self.pool_128(gt_depth)
+            #                 pred_depth = self.pool_128(pred_depth)
+            #         else:
+            #             gt_img = self.pool_64(gt_img)
+            #             gt_depth = self.pool_64(gt_depth)
+
+            #         gt_vis = th.cat(
+            #             [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+            #             dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+            #         pred_vis = th.cat(
+            #             [pred_img,
+            #              pred_depth.repeat_interleave(3, dim=1)],
+            #             dim=-1)  # B, 3, H, W
+
+            #         vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+            #             1, 2, 0).cpu()  # ! pred in range[-1, 1]
+            #         # vis_grid = torchvision.utils.make_grid(vis) # HWC
+            #         vis = vis.numpy() * 127.5 + 127.5
+            #         vis = vis.clip(0, 255).astype(np.uint8)
+            #         Image.fromarray(vis).save(
+            #             f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+            #         )
+            #         print(
+            #             'log vis to: ',
+            #             f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+            #         )
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    def norm_depth(pred_depth):  # to [-1,1]
+                        # pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                        return -(pred_depth * 2 - 1)
+
+                    pred_img = pred['image_raw'].clip(-1, 1)
+                    gt_img = micro['img']
+
+                    # infer novel view also
+                    pred_nv_img = self.rec_model(
+                        img=micro['img_to_encoder'],
+                        c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+                    # if 'depth' in micro:
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = norm_depth(gt_depth)
+                    # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                    #                                           gt_depth.min())
+                    # if True:
+                    if 'image_depth' in pred:
+                        # pred_depth = pred['image_depth']
+                        # pred_depth = (pred_depth - pred_depth.min()) / (
+                        #     pred_depth.max() - pred_depth.min())
+                        pred_depth = norm_depth(pred['image_depth'])
+                        pred_nv_depth = norm_depth(pred_nv_img['image_depth'])
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+                        pred_nv_depth = th.zeros_like(gt_depth)
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    if gt_img.shape[-1] == 64:
+                        gt_depth = self.pool_64(gt_depth)
+                    elif gt_img.shape[-1] == 128:
+                        gt_depth = self.pool_128(gt_depth)
+                    # else:
+                    # gt_depth = self.pool_64(gt_depth)
+
+                    # st()
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    pred_vis_nv = th.cat([
+                        pred_nv_img['image_raw'].clip(-1, 1),
+                        pred_nv_depth.repeat_interleave(3, dim=1)
+                    ],
+                                         dim=-1)  # B, 3, H, W
+                    pred_vis = th.cat([pred_vis, pred_vis_nv],
+                                      dim=-2)  # cat in H dim
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+                    # .permute(
+                    #     0, 2, 3, 1).cpu()
+                    vis_tensor = torchvision.utils.make_grid(
+                        vis, nrow=vis.shape[-1] // 64)  # HWC
+                    torchvision.utils.save_image(
+                        vis_tensor,
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+                        normalize=True,
+                        value_range=(-1, 1))
+                    # vis = vis.numpy() * 127.5 + 127.5
+                    # vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # Image.fromarray(vis).save(
+                    #     f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)  # only use novel view D
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+            target_nvs = {}
+
+            for k, v in micro.items():
+                if k[:2] == 'nv':
+                    orig_key = k.replace('nv_', '')
+                    target_nvs[orig_key] = v
+                    # target_cano[orig_key] = micro[orig_key]
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                nvs_pred = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['nv_c'],
+                )  # predict novel view here
+                # c=th.cat([
+                #     micro['c'][1:],
+                #     micro['c'][:1],
+                # ]))  # ! render novel views only for D loss
+
+                # add cvD supervision
+
+                if 'image_sr' in nvs_pred:
+                    raise NotImplementedError()
+                    # concat sr and raw results
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        # pred_nv['image_sr'],
+                        # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                        th.cat([
+                            th.nn.functional.interpolate(
+                                nvs_pred['image_raw'],
+                                size=nvs_pred['image_sr'].shape[2:],
+                                mode='bilinear',
+                                align_corners=False,
+                                antialias=True),
+                            nvs_pred['image_sr'],
+                        ],
+                               dim=1),
+                        for_G=True).mean()  # ! for debugging
+
+                    # supervise sr only
+                    # vision_aided_loss = self.ddp_nvs_cvD(
+                    #     # pred_nv['image_sr'],
+                    #     # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'),
+                    #     th.cat([nvs_pred['image_sr'],
+                    #     th.nn.functional.interpolate(nvs_pred['image_raw'], size=nvs_pred['image_sr'].shape[2:], mode='bilinear',
+                    #                         align_corners=False,
+                    #                         antialias=True),]),
+                    #     for_G=True).mean()  # ! for debugging
+
+                    # pred_nv['image_raw'], for_G=True).mean()  # [B, 1] shape
+                else:
+                    vision_aided_loss = self.ddp_nvs_cvD(
+                        nvs_pred['image_raw'],
+                        for_G=True).mean()  # [B, 1] shape
+
+                # ! add nv reconstruction loss
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, fg_mask = self.loss_class(
+                        nvs_pred,
+                        target_nvs,
+                        step=self.step + self.resume_step,
+                        test_mode=False,
+                        return_fg_mask=True,
+                        conf_sigma_l1=None,
+                        conf_sigma_percl=None)
+                    log_rec3d_loss_dict(loss_dict)
+
+                loss += vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs':
+                    vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda,
+                    **{f'{k}_nv': v for k, v in loss_dict.items()}
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            if dist_util.get_rank() == 0 and self.step % 500 == 1:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    def norm_depth(pred_depth):  # to [-1,1]
+                        # pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                        return -(pred_depth * 2 - 1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = norm_depth(gt_depth)
+
+                    # if True:
+                    # pred_depth = nvs_pred['image_depth']
+                    # pred_depth = (pred_depth - pred_depth.min()) / (
+                    #     pred_depth.max() - pred_depth.min())
+                    pred_depth = norm_depth(nvs_pred['image_depth'])
+                    pred_img = nvs_pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in nvs_pred:
+
+                        if nvs_pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat([
+                                self.pool_512(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif nvs_pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat([
+                                self.pool_256(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat([
+                                self.pool_128(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    if gt_img.shape[-1] == 64:
+                        gt_depth = self.pool_64(gt_depth)
+                    elif gt_img.shape[-1] == 128:
+                        gt_depth = self.pool_128(gt_depth)
+
+                    # else:
+                    #     gt_img = self.pool_64(gt_img)
+                    #     gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # print(vis.shape)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
diff --git a/nsr/cvD/nvsD_nosr.py b/nsr/cvD/nvsD_nosr.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bb1a7c599966a7168d39dca28370e1d222f9887
--- /dev/null
+++ b/nsr/cvD/nvsD_nosr.py
@@ -0,0 +1,545 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from ..train_util import TrainLoopBasic, TrainLoop3DRec
+import vision_aided_loss
+from dnnlib.util import calculate_adaptive_weight
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+from ..train_util_cvD import TrainLoop3DcvD
+
+
+class TrainLoop3DcvD_nvsD_noSR(TrainLoop3DcvD):
+
+    def __init__(self,
+                 *,
+                #  model,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         use_amp=use_amp,
+                         SR_TRAINING=False,
+                         **kwargs)
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step_rec':
+            self.forward_G_rec(batch)
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'g_step_nvs':
+            self.forward_G_nvs(batch)
+            took_step_g_nvs = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_nvs:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step':
+            self.forward_D(batch)
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_rec') # pure VAE reconstruction
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_cvD, 'cvD')
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+                    self.save()
+                    self.save(self.mp_trainer_cvD, 'cvD')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_cvD, 'cvD')
+
+    def forward_D(self, batch):  # update D
+        self.rec_model.requires_grad_(False)
+
+        self.mp_trainer_cvD.zero_grad()
+        self.ddp_nvs_cvD.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_cvD.use_amp):
+
+                novel_view_c = th.cat([
+                    micro['c'][1:], micro['c'][:1]
+                ])
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                cano_pred = self.rec_model(latent=latent,
+                                           c=micro['c'],
+                                           behaviour='triplane_dec', 
+                                           return_raw_only=True)
+
+                nvs_pred = self.rec_model(latent=latent,
+                                            c=novel_view_c,
+                                            behaviour='triplane_dec',
+                                            return_raw_only=True)
+
+                # if 'image_sr' in nvs_pred:
+                #     d_loss_nvs = self.run_D_Diter(
+                #         real=th.cat([
+                #             th.nn.functional.interpolate(
+                #                 cano_pred['image_raw'],
+                #                 size=cano_pred['image_sr'].shape[2:],
+                #                 mode='bilinear',
+                #                 align_corners=False,
+                #                 antialias=True),
+                #             cano_pred['image_sr'],
+                #         ], dim=1),
+                #         fake=th.cat([
+                #             th.nn.functional.interpolate(
+                #                 nvs_pred['image_raw'],
+                #                 size=nvs_pred['image_sr'].shape[2:],
+                #                 mode='bilinear',
+                #                 align_corners=False,
+                #                 antialias=True),
+                #             nvs_pred['image_sr'],
+                #         ], dim=1),
+                #         D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                # else:
+                d_loss_nvs = self.run_D_Diter(
+                    real=cano_pred['image_raw'],
+                    fake=nvs_pred['image_raw'],
+                    D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                log_rec3d_loss_dict(
+                    {'vision_aided_loss/D_nvs': d_loss_nvs})
+                self.mp_trainer_cvD.backward(d_loss_nvs)
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                pred_for_rec = pred
+
+                if last_batch or not self.use_ddp:
+                    loss, loss_dict = self.loss_class(pred_for_rec,
+                                                      target_for_rec,
+                                                      test_mode=False)
+                else:
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss, loss_dict = self.loss_class(pred_for_rec,
+                                                          target_for_rec,
+                                                          test_mode=False)
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+        self.ddp_nvs_cvD.requires_grad_(False)  # only use novel view D
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                nvs_pred = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][1:],
+                        micro['c'][:1],
+                    ]))  # ! render novel views only for D loss
+
+                # add cvD supervision
+
+                # if 'image_sr' in nvs_pred:
+                #     # concat sr and raw results
+                #     vision_aided_loss = self.ddp_nvs_cvD(
+                #         nvs_pred['image_raw'],
+                #         # th.cat([
+                #         #     th.nn.functional.interpolate(
+                #         #         size=nvs_pred['image_sr'].shape[2:],
+                #         #         mode='bilinear',
+                #         #         align_corners=False,
+                #         #         antialias=True),
+                #         #     # nvs_pred['image_sr'],
+                #         # ], dim=1),
+                #         for_G=True).mean()  
+                # else:
+                vision_aided_loss = self.ddp_nvs_cvD(
+                    nvs_pred['image_raw'],
+                    for_G=True).mean()  # [B, 1] shape
+
+                loss = vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs': loss
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = nvs_pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = nvs_pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in nvs_pred:
+
+                        if nvs_pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat([
+                                self.pool_512(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif nvs_pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat([
+                                self.pool_256(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat([
+                                self.pool_128(pred_img), nvs_pred['image_sr']
+                            ],
+                                              dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    print(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+
+    def save(self, mp_trainer=None, model_name='rec'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        dist.barrier()
+
+    def _load_and_sync_parameters(self, model=None, model_name='rec'):
+        resume_checkpoint, self.resume_step = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.rec_model  # default model in the parent class
+
+        print(resume_checkpoint)
+
+        if resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                print(f'mark {model_name} loading ', flush=True)
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                print(f'mark {model_name} loading finished', flush=True)
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+                    elif 'IN' in k:
+                        print('ignore ', k)
+                    else:
+                        print('!!!! ignore key: ', k, ": ", v.size(),)
+                            #   'shape in model: ', model_state_dict[k].size())
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            print(f'synced {model_name} params')
diff --git a/nsr/dual_discriminator.py b/nsr/dual_discriminator.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9156230b656f7ecc9dc2c0792cc5bb54ad5a0a1
--- /dev/null
+++ b/nsr/dual_discriminator.py
@@ -0,0 +1,480 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Discriminator architectures from the paper
+"Efficient Geometry-aware 3D Generative Adversarial Networks"."""
+
+import numpy as np
+import torch
+from torch_utils import persistence
+from torch_utils.ops import upfirdn2d
+from .networks_stylegan2 import DiscriminatorBlock, MappingNetwork, DiscriminatorEpilogue
+from pdb import set_trace as st
+
+
+@persistence.persistent_class
+class SingleDiscriminator(torch.nn.Module):
+    def __init__(
+            self,
+            c_dim,  # Conditioning label (C) dimensionality.
+            img_resolution,  # Input resolution.
+            img_channels,  # Number of input color channels.
+            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
+            channel_base=32768,  # Overall multiplier for the number of channels.
+            channel_max=512,  # Maximum number of channels in any layer.
+            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
+            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+            sr_upsample_factor=1,  # Ignored for SingleDiscriminator
+            block_kwargs={},  # Arguments for DiscriminatorBlock.
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [
+            2**i for i in range(self.img_resolution_log2, 2, -1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max)
+            for res in self.block_resolutions + [4]
+        }
+        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
+                              8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(img_channels=img_channels,
+                             architecture=architecture,
+                             conv_clamp=conv_clamp)
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = (res >= fp16_resolution)
+            block = DiscriminatorBlock(in_channels,
+                                       tmp_channels,
+                                       out_channels,
+                                       resolution=res,
+                                       first_layer_idx=cur_layer_idx,
+                                       use_fp16=use_fp16,
+                                       **block_kwargs,
+                                       **common_kwargs)
+            setattr(self, f'b{res}', block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(z_dim=0,
+                                          c_dim=c_dim,
+                                          w_dim=cmap_dim,
+                                          num_ws=None,
+                                          w_avg_beta=None,
+                                          **mapping_kwargs)
+        self.b4 = DiscriminatorEpilogue(channels_dict[4],
+                                        cmap_dim=cmap_dim,
+                                        resolution=4,
+                                        **epilogue_kwargs,
+                                        **common_kwargs)
+
+    def forward(self, img, c, update_emas=False, **block_kwargs):
+        img = img['image']
+
+        _ = update_emas  # unused
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+    def extra_repr(self):
+        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
+
+
+#----------------------------------------------------------------------------
+
+
+def filtered_resizing(image_orig_tensor, size, f, filter_mode='antialiased'):
+    if filter_mode == 'antialiased':
+        ada_filtered_64 = torch.nn.functional.interpolate(image_orig_tensor,
+                                                          size=(size, size),
+                                                          mode='bilinear',
+                                                          align_corners=False,
+                                                          antialias=True)
+    elif filter_mode == 'classic':
+        ada_filtered_64 = upfirdn2d.upsample2d(image_orig_tensor, f, up=2)
+        ada_filtered_64 = torch.nn.functional.interpolate(ada_filtered_64,
+                                                          size=(size * 2 + 2,
+                                                                size * 2 + 2),
+                                                          mode='bilinear',
+                                                          align_corners=False)
+        ada_filtered_64 = upfirdn2d.downsample2d(ada_filtered_64,
+                                                 f,
+                                                 down=2,
+                                                 flip_filter=True,
+                                                 padding=-1)
+    elif filter_mode == 'none':
+        ada_filtered_64 = torch.nn.functional.interpolate(image_orig_tensor,
+                                                          size=(size, size),
+                                                          mode='bilinear',
+                                                          align_corners=False)
+    elif type(filter_mode) == float:
+        assert 0 < filter_mode < 1
+
+        filtered = torch.nn.functional.interpolate(image_orig_tensor,
+                                                   size=(size, size),
+                                                   mode='bilinear',
+                                                   align_corners=False,
+                                                   antialias=True)
+        aliased = torch.nn.functional.interpolate(image_orig_tensor,
+                                                  size=(size, size),
+                                                  mode='bilinear',
+                                                  align_corners=False,
+                                                  antialias=False)
+        ada_filtered_64 = (1 -
+                           filter_mode) * aliased + (filter_mode) * filtered
+
+    return ada_filtered_64
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class DualDiscriminator(torch.nn.Module):
+    def __init__(
+            self,
+            c_dim,  # Conditioning label (C) dimensionality.
+            img_resolution,  # Input resolution.
+            img_channels,  # Number of input color channels.
+            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
+            channel_base=32768,  # Overall multiplier for the number of channels.
+            channel_max=512,  # Maximum number of channels in any layer.
+            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
+            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+            disc_c_noise=0,  # Corrupt camera parameters with X std dev of noise before disc. pose conditioning.
+            block_kwargs={},  # Arguments for DiscriminatorBlock.
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
+    ):
+        super().__init__()
+        # img_channels *= 2
+        if img_channels == 3:
+            img_channels *= 2
+
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [
+            2**i for i in range(self.img_resolution_log2, 2, -1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max)
+            for res in self.block_resolutions + [4]
+        }
+        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
+                              8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(img_channels=img_channels,
+                             architecture=architecture,
+                             conv_clamp=conv_clamp)
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = (res >= fp16_resolution)
+            block = DiscriminatorBlock(in_channels,
+                                       tmp_channels,
+                                       out_channels,
+                                       resolution=res,
+                                       first_layer_idx=cur_layer_idx,
+                                       use_fp16=use_fp16,
+                                       **block_kwargs,
+                                       **common_kwargs)
+            setattr(self, f'b{res}', block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(z_dim=0,
+                                          c_dim=c_dim,
+                                          w_dim=cmap_dim,
+                                          num_ws=None,
+                                          w_avg_beta=None,
+                                          **mapping_kwargs)
+        self.b4 = DiscriminatorEpilogue(channels_dict[4],
+                                        cmap_dim=cmap_dim,
+                                        resolution=4,
+                                        **epilogue_kwargs,
+                                        **common_kwargs)
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter([1, 3, 3, 1]))
+        self.disc_c_noise = disc_c_noise
+
+    def forward(self, img, c, update_emas=False, **block_kwargs):
+        image_raw = filtered_resizing(img['image_raw'],
+                                    #   size=img['image'].shape[-1],
+                                      size=img['image_sr'].shape[-1],
+                                      f=self.resample_filter)
+        # img = torch.cat([img['image'], image_raw], 1)
+        img = torch.cat([img['image_sr'], image_raw], 1)
+
+        _ = update_emas  # unused
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            if self.disc_c_noise > 0:
+                c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+    def extra_repr(self):
+        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
+
+
+@persistence.persistent_class
+class GeoDualDiscriminator(DualDiscriminator):
+    def __init__(self, c_dim, img_resolution, img_channels, architecture='resnet', channel_base=32768, channel_max=512, num_fp16_res=4, conv_clamp=256, cmap_dim=None, disc_c_noise=0, block_kwargs={}, mapping_kwargs={}, epilogue_kwargs={}, normal_condition=False):
+        super().__init__(c_dim, img_resolution, img_channels, architecture, channel_base, channel_max, num_fp16_res, conv_clamp, cmap_dim, disc_c_noise, block_kwargs, mapping_kwargs, epilogue_kwargs)
+        self.normal_condition = normal_condition
+
+    def forward(self, img, c, update_emas=False, **block_kwargs):
+        image= img['image']
+        image_raw = filtered_resizing(img['image_raw'],
+                                      size=img['image'].shape[-1],
+                                      f=self.resample_filter)
+        D_input_img = torch.cat([image, image_raw], 1)
+
+        image_depth = filtered_resizing(img['image_depth'], size=img['image'].shape[-1], f=self.resample_filter)
+        if self.normal_condition and 'normal' in img:
+            image_normal = filtered_resizing(img['normal'], size=img['image'].shape[-1], f=self.resample_filter)
+            D_input_img = torch.cat([D_input_img, image_depth, image_normal], 1)
+        else:
+            D_input_img = torch.cat([D_input_img, image_depth], 1)
+
+        img = D_input_img
+
+        _ = update_emas  # unused
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            if self.disc_c_noise > 0:
+                c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class DummyDualDiscriminator(torch.nn.Module):
+    def __init__(
+            self,
+            c_dim,  # Conditioning label (C) dimensionality.
+            img_resolution,  # Input resolution.
+            img_channels,  # Number of input color channels.
+            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
+            channel_base=32768,  # Overall multiplier for the number of channels.
+            channel_max=512,  # Maximum number of channels in any layer.
+            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
+            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+            block_kwargs={},  # Arguments for DiscriminatorBlock.
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
+    ):
+        super().__init__()
+        img_channels *= 2
+
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [
+            2**i for i in range(self.img_resolution_log2, 2, -1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max)
+            for res in self.block_resolutions + [4]
+        }
+        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
+                              8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(img_channels=img_channels,
+                             architecture=architecture,
+                             conv_clamp=conv_clamp)
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = (res >= fp16_resolution)
+            block = DiscriminatorBlock(in_channels,
+                                       tmp_channels,
+                                       out_channels,
+                                       resolution=res,
+                                       first_layer_idx=cur_layer_idx,
+                                       use_fp16=use_fp16,
+                                       **block_kwargs,
+                                       **common_kwargs)
+            setattr(self, f'b{res}', block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(z_dim=0,
+                                          c_dim=c_dim,
+                                          w_dim=cmap_dim,
+                                          num_ws=None,
+                                          w_avg_beta=None,
+                                          **mapping_kwargs)
+        self.b4 = DiscriminatorEpilogue(channels_dict[4],
+                                        cmap_dim=cmap_dim,
+                                        resolution=4,
+                                        **epilogue_kwargs,
+                                        **common_kwargs)
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter([1, 3, 3, 1]))
+
+        self.raw_fade = 1
+
+    def forward(self, img, c, update_emas=False, **block_kwargs):
+        self.raw_fade = max(0, self.raw_fade - 1 / (500000 / 32))
+
+        image_raw = filtered_resizing(img['image_raw'],
+                                      size=img['image'].shape[-1],
+                                      f=self.resample_filter) * self.raw_fade
+        img = torch.cat([img['image'], image_raw], 1)
+
+        _ = update_emas  # unused
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+    def extra_repr(self):
+        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
+
+
+#----------------------------------------------------------------------------
+
+# panohead
+# Tri-discriminator: upsampled image, super-resolved image, and segmentation mask
+# V2: first concatenate imgs and seg mask, using only one conv block
+@persistence.persistent_class
+class MaskDualDiscriminatorV2(torch.nn.Module):
+    def __init__(self,
+        c_dim,                          # Conditioning label (C) dimensionality.
+        img_resolution,                 # Input resolution.
+        img_channels,                   # Number of input color channels.
+        seg_resolution,                 # Input resolution.
+        seg_channels,                   # Number of input color channels.
+        architecture        = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
+        channel_base        = 32768,    # Overall multiplier for the number of channels.
+        channel_max         = 512,      # Maximum number of channels in any layer.
+        num_fp16_res        = 4,        # Use FP16 for the N highest resolutions.
+        conv_clamp          = 256,      # Clamp the output of convolution layers to +-X, None = disable clamping.
+        cmap_dim            = None,     # Dimensionality of mapped conditioning label, None = default.
+        disc_c_noise        = 0,        # Corrupt camera parameters with X std dev of noise before disc. pose conditioning.
+        block_kwargs        = {},       # Arguments for DiscriminatorBlock.
+        mapping_kwargs      = {},       # Arguments for MappingNetwork.
+        epilogue_kwargs     = {},       # Arguments for DiscriminatorEpilogue.
+    ):
+        super().__init__()
+        img_channels = img_channels * 2 + seg_channels
+
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
+        channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
+        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = (res >= fp16_resolution)
+            block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
+                first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
+            setattr(self, f'b{res}', block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
+        self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
+        self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))
+        self.disc_c_noise = disc_c_noise
+
+    def forward(self, img, c, update_emas=False, **block_kwargs):
+        image_raw = filtered_resizing(img['image_raw'], size=img['image'].shape[-1], f=self.resample_filter)
+        seg = filtered_resizing(img['image_mask'], size=img['image'].shape[-1], f=self.resample_filter)
+        seg = 2 * seg - 1 # normalize to [-1,1]
+        img = torch.cat([img['image'], image_raw, seg], 1)
+
+        _ = update_emas # unused
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            if self.disc_c_noise > 0: c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+    def extra_repr(self):
+        return ' '.join([
+            f'c_dim={self.c_dim:d},',
+            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
+            f'seg_resolution={self.seg_resolution:d}, seg_channels={self.seg_channels:d}'])
\ No newline at end of file
diff --git a/nsr/gaussian_renderer/__init__.py b/nsr/gaussian_renderer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f74e336af41e042dfb9f1c308e40caf17d0b3211
--- /dev/null
+++ b/nsr/gaussian_renderer/__init__.py
@@ -0,0 +1,100 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import math
+from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianRasterizer
+from scene.gaussian_model import GaussianModel
+from utils.sh_utils import eval_sh
+
+def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None):
+    """
+    Render the scene. 
+    
+    Background tensor (bg_color) must be on GPU!
+    """
+ 
+    # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
+    screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device="cuda") + 0
+    try:
+        screenspace_points.retain_grad()
+    except:
+        pass
+
+    # Set up rasterization configuration
+    tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
+    tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
+
+    raster_settings = GaussianRasterizationSettings(
+        image_height=int(viewpoint_camera.image_height),
+        image_width=int(viewpoint_camera.image_width),
+        tanfovx=tanfovx,
+        tanfovy=tanfovy,
+        bg=bg_color,
+        scale_modifier=scaling_modifier,
+        viewmatrix=viewpoint_camera.world_view_transform,
+        projmatrix=viewpoint_camera.full_proj_transform,
+        sh_degree=pc.active_sh_degree,
+        campos=viewpoint_camera.camera_center,
+        prefiltered=False,
+        debug=pipe.debug
+    )
+
+    rasterizer = GaussianRasterizer(raster_settings=raster_settings)
+
+    means3D = pc.get_xyz
+    means2D = screenspace_points
+    opacity = pc.get_opacity
+
+    # If precomputed 3d covariance is provided, use it. If not, then it will be computed from
+    # scaling / rotation by the rasterizer.
+    scales = None
+    rotations = None
+    cov3D_precomp = None
+    if pipe.compute_cov3D_python:
+        cov3D_precomp = pc.get_covariance(scaling_modifier)
+    else:
+        scales = pc.get_scaling
+        rotations = pc.get_rotation
+
+    # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors
+    # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.
+    shs = None
+    colors_precomp = None
+    if override_color is None:
+        if pipe.convert_SHs_python:
+            shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2)
+            dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1))
+            dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True)
+            sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized)
+            colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
+        else:
+            shs = pc.get_features
+    else:
+        colors_precomp = override_color
+
+    # Rasterize visible Gaussians to image, obtain their radii (on screen). 
+    rendered_image, radii = rasterizer(
+        means3D = means3D,
+        means2D = means2D,
+        shs = shs,
+        colors_precomp = colors_precomp,
+        opacities = opacity,
+        scales = scales,
+        rotations = rotations,
+        cov3D_precomp = cov3D_precomp)
+
+    # Those Gaussians that were frustum culled or had a radius of 0 were not visible.
+    # They will be excluded from value updates used in the splitting criteria.
+    return {"render": rendered_image,
+            "viewspace_points": screenspace_points,
+            "visibility_filter" : radii > 0,
+            "radii": radii}
diff --git a/nsr/gaussian_renderer/network_gui.py b/nsr/gaussian_renderer/network_gui.py
new file mode 100644
index 0000000000000000000000000000000000000000..df2f9dae782b24527ae5b09f91ca4009361de53f
--- /dev/null
+++ b/nsr/gaussian_renderer/network_gui.py
@@ -0,0 +1,86 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import traceback
+import socket
+import json
+from scene.cameras import MiniCam
+
+host = "127.0.0.1"
+port = 6009
+
+conn = None
+addr = None
+
+listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+
+def init(wish_host, wish_port):
+    global host, port, listener
+    host = wish_host
+    port = wish_port
+    listener.bind((host, port))
+    listener.listen()
+    listener.settimeout(0)
+
+def try_connect():
+    global conn, addr, listener
+    try:
+        conn, addr = listener.accept()
+        print(f"\nConnected by {addr}")
+        conn.settimeout(None)
+    except Exception as inst:
+        pass
+            
+def read():
+    global conn
+    messageLength = conn.recv(4)
+    messageLength = int.from_bytes(messageLength, 'little')
+    message = conn.recv(messageLength)
+    return json.loads(message.decode("utf-8"))
+
+def send(message_bytes, verify):
+    global conn
+    if message_bytes != None:
+        conn.sendall(message_bytes)
+    conn.sendall(len(verify).to_bytes(4, 'little'))
+    conn.sendall(bytes(verify, 'ascii'))
+
+def receive():
+    message = read()
+
+    width = message["resolution_x"]
+    height = message["resolution_y"]
+
+    if width != 0 and height != 0:
+        try:
+            do_training = bool(message["train"])
+            fovy = message["fov_y"]
+            fovx = message["fov_x"]
+            znear = message["z_near"]
+            zfar = message["z_far"]
+            do_shs_python = bool(message["shs_python"])
+            do_rot_scale_python = bool(message["rot_scale_python"])
+            keep_alive = bool(message["keep_alive"])
+            scaling_modifier = message["scaling_modifier"]
+            world_view_transform = torch.reshape(torch.tensor(message["view_matrix"]), (4, 4)).cuda()
+            world_view_transform[:,1] = -world_view_transform[:,1]
+            world_view_transform[:,2] = -world_view_transform[:,2]
+            full_proj_transform = torch.reshape(torch.tensor(message["view_projection_matrix"]), (4, 4)).cuda()
+            full_proj_transform[:,1] = -full_proj_transform[:,1]
+            custom_cam = MiniCam(width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform)
+        except Exception as e:
+            print("")
+            traceback.print_exc()
+            raise e
+        return custom_cam, do_training, do_shs_python, do_rot_scale_python, keep_alive, scaling_modifier
+    else:
+        return None, None, None, None, None, None
\ No newline at end of file
diff --git a/nsr/geometry/__init__.py b/nsr/geometry/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..89e9a6c2fffe82a55693885dae78c1a630924389
--- /dev/null
+++ b/nsr/geometry/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
diff --git a/nsr/geometry/camera/__init__.py b/nsr/geometry/camera/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5c7082e47c65a08e25489b3c3fd010d07ad9758
--- /dev/null
+++ b/nsr/geometry/camera/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+from torch import nn
+
+
+class Camera(nn.Module):
+    def __init__(self):
+        super(Camera, self).__init__()
+        pass
diff --git a/nsr/geometry/camera/perspective_camera.py b/nsr/geometry/camera/perspective_camera.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f1e1906096476bcb411033601251115c64c97f9
--- /dev/null
+++ b/nsr/geometry/camera/perspective_camera.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+from . import Camera
+import numpy as np
+
+
+def projection(x=0.1, n=1.0, f=50.0, near_plane=None):
+    if near_plane is None:
+        near_plane = n
+    return np.array(
+        [[n / x, 0, 0, 0],
+         [0, n / -x, 0, 0],
+         [0, 0, -(f + near_plane) / (f - near_plane), -(2 * f * near_plane) / (f - near_plane)],
+         [0, 0, -1, 0]]).astype(np.float32)
+
+
+class PerspectiveCamera(Camera):
+    def __init__(self, fovy=49.0, focal=None, device='cuda'):
+        super(PerspectiveCamera, self).__init__()
+        self.device = device
+        if focal is None:
+            focal = np.tan(fovy / 180.0 * np.pi * 0.5)
+        self.proj_mtx = torch.from_numpy(projection(x=focal, f=1000.0, n=1.0, near_plane=0.1)).to(self.device).unsqueeze(dim=0)
+
+    def project(self, points_bxnx4):
+        out = torch.matmul(
+            points_bxnx4,
+            torch.transpose(self.proj_mtx, 1, 2))
+        return out
diff --git a/nsr/geometry/render/__init__.py b/nsr/geometry/render/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..483cfabbf395853f1ca3e67b856d5f17b9889d1b
--- /dev/null
+++ b/nsr/geometry/render/__init__.py
@@ -0,0 +1,8 @@
+import torch
+
+class Renderer():
+    def __init__(self):
+        pass
+
+    def forward(self):
+        pass
\ No newline at end of file
diff --git a/nsr/geometry/render/neural_render.py b/nsr/geometry/render/neural_render.py
new file mode 100644
index 0000000000000000000000000000000000000000..473464480125c050ee6dba973450678a197145fb
--- /dev/null
+++ b/nsr/geometry/render/neural_render.py
@@ -0,0 +1,121 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+import torch.nn.functional as F
+import nvdiffrast.torch as dr
+from . import Renderer
+
+_FG_LUT = None
+
+
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    return dr.interpolate(
+        attr.contiguous(), rast, attr_idx, rast_db=rast_db,
+        diff_attrs=None if rast_db is None else 'all')
+
+
+def xfm_points(points, matrix, use_python=True):
+    '''Transform points.
+    Args:
+        points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3]
+        matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4]
+        use_python: Use PyTorch's torch.matmul (for validation)
+    Returns:
+        Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4].
+    '''
+    out = torch.matmul(torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2))
+    if torch.is_anomaly_enabled():
+        assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN"
+    return out
+
+
+def dot(x, y):
+    return torch.sum(x * y, -1, keepdim=True)
+
+
+def compute_vertex_normal(v_pos, t_pos_idx):
+    i0 = t_pos_idx[:, 0]
+    i1 = t_pos_idx[:, 1]
+    i2 = t_pos_idx[:, 2]
+
+    v0 = v_pos[i0, :]
+    v1 = v_pos[i1, :]
+    v2 = v_pos[i2, :]
+
+    face_normals = torch.cross(v1 - v0, v2 - v0)
+
+    # Splat face normals to vertices
+    v_nrm = torch.zeros_like(v_pos)
+    v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
+    v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
+    v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
+
+    # Normalize, replace zero (degenerated) normals with some default value
+    v_nrm = torch.where(
+        dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
+    )
+    v_nrm = F.normalize(v_nrm, dim=1)
+    assert torch.all(torch.isfinite(v_nrm))
+
+    return v_nrm
+
+
+class NeuralRender(Renderer):
+    def __init__(self, device='cuda', camera_model=None):
+        super(NeuralRender, self).__init__()
+        self.device = device
+        self.ctx = dr.RasterizeCudaContext(device=device)
+        self.projection_mtx = None
+        self.camera = camera_model
+
+    def render_mesh(
+            self,
+            mesh_v_pos_bxnx3,
+            mesh_t_pos_idx_fx3,
+            camera_mv_bx4x4,
+            mesh_v_feat_bxnxd,
+            resolution=256,
+            spp=1,
+            device='cuda',
+            hierarchical_mask=False
+    ):
+        assert not hierarchical_mask
+        
+        mtx_in = torch.tensor(camera_mv_bx4x4, dtype=torch.float32, device=device) if not torch.is_tensor(camera_mv_bx4x4) else camera_mv_bx4x4
+        v_pos = xfm_points(mesh_v_pos_bxnx3, mtx_in)  # Rotate it to camera coordinates
+        v_pos_clip = self.camera.project(v_pos)  # Projection in the camera
+
+        v_nrm = compute_vertex_normal(mesh_v_pos_bxnx3[0], mesh_t_pos_idx_fx3.long())  # vertex normals in world coordinates
+
+        # Render the image,
+        # Here we only return the feature (3D location) at each pixel, which will be used as the input for neural render
+        num_layers = 1
+        mask_pyramid = None
+        assert mesh_t_pos_idx_fx3.shape[0] > 0  # Make sure we have shapes
+        mesh_v_feat_bxnxd = torch.cat([mesh_v_feat_bxnxd.repeat(v_pos.shape[0], 1, 1), v_pos], dim=-1)  # Concatenate the pos
+
+        with dr.DepthPeeler(self.ctx, v_pos_clip, mesh_t_pos_idx_fx3, [resolution * spp, resolution * spp]) as peeler:
+            for _ in range(num_layers):
+                rast, db = peeler.rasterize_next_layer()
+                gb_feat, _ = interpolate(mesh_v_feat_bxnxd, rast, mesh_t_pos_idx_fx3)
+
+        hard_mask = torch.clamp(rast[..., -1:], 0, 1)
+        antialias_mask = dr.antialias(
+            hard_mask.clone().contiguous(), rast, v_pos_clip,
+            mesh_t_pos_idx_fx3)
+
+        depth = gb_feat[..., -2:-1]
+        ori_mesh_feature = gb_feat[..., :-4]
+
+        normal, _ = interpolate(v_nrm[None, ...], rast, mesh_t_pos_idx_fx3)
+        normal = dr.antialias(normal.clone().contiguous(), rast, v_pos_clip, mesh_t_pos_idx_fx3)
+        normal = F.normalize(normal, dim=-1)
+        normal = torch.lerp(torch.zeros_like(normal), (normal + 1.0) / 2.0, hard_mask.float())      # black background
+
+        return ori_mesh_feature, antialias_mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal
diff --git a/nsr/geometry/rep_3d/__init__.py b/nsr/geometry/rep_3d/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3d5628a8433298477d1963f92578d47106b4a0f
--- /dev/null
+++ b/nsr/geometry/rep_3d/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+import numpy as np
+
+
+class Geometry():
+    def __init__(self):
+        pass
+
+    def forward(self):
+        pass
diff --git a/nsr/geometry/rep_3d/dmtet.py b/nsr/geometry/rep_3d/dmtet.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6a709380abac0bbf66fd1c8582485f3982223e4
--- /dev/null
+++ b/nsr/geometry/rep_3d/dmtet.py
@@ -0,0 +1,504 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+import numpy as np
+import os
+from . import Geometry
+from .dmtet_utils import get_center_boundary_index
+import torch.nn.functional as F
+
+
+###############################################################################
+# DMTet utility functions
+###############################################################################
+def create_mt_variable(device):
+    triangle_table = torch.tensor(
+        [
+            [-1, -1, -1, -1, -1, -1],
+            [1, 0, 2, -1, -1, -1],
+            [4, 0, 3, -1, -1, -1],
+            [1, 4, 2, 1, 3, 4],
+            [3, 1, 5, -1, -1, -1],
+            [2, 3, 0, 2, 5, 3],
+            [1, 4, 0, 1, 5, 4],
+            [4, 2, 5, -1, -1, -1],
+            [4, 5, 2, -1, -1, -1],
+            [4, 1, 0, 4, 5, 1],
+            [3, 2, 0, 3, 5, 2],
+            [1, 3, 5, -1, -1, -1],
+            [4, 1, 2, 4, 3, 1],
+            [3, 0, 4, -1, -1, -1],
+            [2, 0, 1, -1, -1, -1],
+            [-1, -1, -1, -1, -1, -1]
+        ], dtype=torch.long, device=device)
+
+    num_triangles_table = torch.tensor([0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long, device=device)
+    base_tet_edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=device)
+    v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=device))
+    return triangle_table, num_triangles_table, base_tet_edges, v_id
+
+
+def sort_edges(edges_ex2):
+    with torch.no_grad():
+        order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()
+        order = order.unsqueeze(dim=1)
+        a = torch.gather(input=edges_ex2, index=order, dim=1)
+        b = torch.gather(input=edges_ex2, index=1 - order, dim=1)
+    return torch.stack([a, b], -1)
+
+
+###############################################################################
+# marching tetrahedrons (differentiable)
+###############################################################################
+
+def marching_tets(pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id):
+    with torch.no_grad():
+        occ_n = sdf_n > 0
+        occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
+        occ_sum = torch.sum(occ_fx4, -1)
+        valid_tets = (occ_sum > 0) & (occ_sum < 4)
+        occ_sum = occ_sum[valid_tets]
+
+        # find all vertices
+        all_edges = tet_fx4[valid_tets][:, base_tet_edges].reshape(-1, 2)
+        all_edges = sort_edges(all_edges)
+        unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
+
+        unique_edges = unique_edges.long()
+        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
+        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=sdf_n.device) * -1
+        mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=sdf_n.device)
+        idx_map = mapping[idx_map]  # map edges to verts
+
+        interp_v = unique_edges[mask_edges]  # .long()
+    edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
+    edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
+    edges_to_interp_sdf[:, -1] *= -1
+
+    denominator = edges_to_interp_sdf.sum(1, keepdim=True)
+
+    edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
+    verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
+
+    idx_map = idx_map.reshape(-1, 6)
+
+    tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
+    num_triangles = num_triangles_table[tetindex]
+
+    # Generate triangle indices
+    faces = torch.cat(
+        (
+            torch.gather(
+                input=idx_map[num_triangles == 1], dim=1,
+                index=triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1, 3),
+            torch.gather(
+                input=idx_map[num_triangles == 2], dim=1,
+                index=triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1, 3),
+        ), dim=0)
+    return verts, faces
+
+
+def create_tetmesh_variables(device='cuda'):
+    tet_table = torch.tensor(
+        [[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+         [0, 4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1],
+         [1, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1],
+         [1, 0, 8, 7, 0, 5, 8, 7, 0, 5, 6, 8],
+         [2, 5, 7, 9, -1, -1, -1, -1, -1, -1, -1, -1],
+         [2, 0, 9, 7, 0, 4, 9, 7, 0, 4, 6, 9],
+         [2, 1, 9, 5, 1, 4, 9, 5, 1, 4, 8, 9],
+         [6, 0, 1, 2, 6, 1, 2, 8, 6, 8, 2, 9],
+         [3, 6, 8, 9, -1, -1, -1, -1, -1, -1, -1, -1],
+         [3, 0, 9, 8, 0, 4, 9, 8, 0, 4, 5, 9],
+         [3, 1, 9, 6, 1, 4, 9, 6, 1, 4, 7, 9],
+         [5, 0, 1, 3, 5, 1, 3, 7, 5, 7, 3, 9],
+         [3, 2, 8, 6, 2, 5, 8, 6, 2, 5, 7, 8],
+         [4, 0, 2, 3, 4, 2, 3, 7, 4, 7, 3, 8],
+         [4, 1, 2, 3, 4, 2, 3, 5, 4, 5, 3, 6],
+         [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]], dtype=torch.long, device=device)
+    num_tets_table = torch.tensor([0, 1, 1, 3, 1, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 0], dtype=torch.long, device=device)
+    return tet_table, num_tets_table
+
+
+def marching_tets_tetmesh(
+        pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id,
+        return_tet_mesh=False, ori_v=None, num_tets_table=None, tet_table=None):
+    with torch.no_grad():
+        occ_n = sdf_n > 0
+        occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
+        occ_sum = torch.sum(occ_fx4, -1)
+        valid_tets = (occ_sum > 0) & (occ_sum < 4)
+        occ_sum = occ_sum[valid_tets]
+
+        # find all vertices
+        all_edges = tet_fx4[valid_tets][:, base_tet_edges].reshape(-1, 2)
+        all_edges = sort_edges(all_edges)
+        unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
+
+        unique_edges = unique_edges.long()
+        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
+        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=sdf_n.device) * -1
+        mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=sdf_n.device)
+        idx_map = mapping[idx_map]  # map edges to verts
+
+        interp_v = unique_edges[mask_edges]  # .long()
+    edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
+    edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
+    edges_to_interp_sdf[:, -1] *= -1
+
+    denominator = edges_to_interp_sdf.sum(1, keepdim=True)
+
+    edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
+    verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
+
+    idx_map = idx_map.reshape(-1, 6)
+
+    tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
+    num_triangles = num_triangles_table[tetindex]
+
+    # Generate triangle indices
+    faces = torch.cat(
+        (
+            torch.gather(
+                input=idx_map[num_triangles == 1], dim=1,
+                index=triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1, 3),
+            torch.gather(
+                input=idx_map[num_triangles == 2], dim=1,
+                index=triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1, 3),
+        ), dim=0)
+    if not return_tet_mesh:
+        return verts, faces
+    occupied_verts = ori_v[occ_n]
+    mapping = torch.ones((pos_nx3.shape[0]), dtype=torch.long, device="cuda") * -1
+    mapping[occ_n] = torch.arange(occupied_verts.shape[0], device="cuda")
+    tet_fx4 = mapping[tet_fx4.reshape(-1)].reshape((-1, 4))
+
+    idx_map = torch.cat([tet_fx4[valid_tets] + verts.shape[0], idx_map], -1)  # t x 10
+    tet_verts = torch.cat([verts, occupied_verts], 0)
+    num_tets = num_tets_table[tetindex]
+
+    tets = torch.cat(
+        (
+            torch.gather(input=idx_map[num_tets == 1], dim=1, index=tet_table[tetindex[num_tets == 1]][:, :4]).reshape(
+                -1,
+                4),
+            torch.gather(input=idx_map[num_tets == 3], dim=1, index=tet_table[tetindex[num_tets == 3]][:, :12]).reshape(
+                -1,
+                4),
+        ), dim=0)
+    # add fully occupied tets
+    fully_occupied = occ_fx4.sum(-1) == 4
+    tet_fully_occupied = tet_fx4[fully_occupied] + verts.shape[0]
+    tets = torch.cat([tets, tet_fully_occupied])
+
+    return verts, faces, tet_verts, tets
+
+
+###############################################################################
+# Compact tet grid
+###############################################################################
+
+def compact_tets(pos_nx3, sdf_n, tet_fx4):
+    with torch.no_grad():
+        # Find surface tets
+        occ_n = sdf_n > 0
+        occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
+        occ_sum = torch.sum(occ_fx4, -1)
+        valid_tets = (occ_sum > 0) & (occ_sum < 4)  # one value per tet, these are the surface tets
+
+        valid_vtx = tet_fx4[valid_tets].reshape(-1)
+        unique_vtx, idx_map = torch.unique(valid_vtx, dim=0, return_inverse=True)
+        new_pos = pos_nx3[unique_vtx]
+        new_sdf = sdf_n[unique_vtx]
+        new_tets = idx_map.reshape(-1, 4)
+        return new_pos, new_sdf, new_tets
+
+
+###############################################################################
+# Subdivide volume
+###############################################################################
+
+def batch_subdivide_volume(tet_pos_bxnx3, tet_bxfx4, grid_sdf):
+    device = tet_pos_bxnx3.device
+    # get new verts
+    tet_fx4 = tet_bxfx4[0]
+    edges = [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3]
+    all_edges = tet_fx4[:, edges].reshape(-1, 2)
+    all_edges = sort_edges(all_edges)
+    unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
+    idx_map = idx_map + tet_pos_bxnx3.shape[1]
+    all_values = torch.cat([tet_pos_bxnx3, grid_sdf], -1)
+    mid_points_pos = all_values[:, unique_edges.reshape(-1)].reshape(
+        all_values.shape[0], -1, 2,
+        all_values.shape[-1]).mean(2)
+    new_v = torch.cat([all_values, mid_points_pos], 1)
+    new_v, new_sdf = new_v[..., :3], new_v[..., 3]
+
+    # get new tets
+
+    idx_a, idx_b, idx_c, idx_d = tet_fx4[:, 0], tet_fx4[:, 1], tet_fx4[:, 2], tet_fx4[:, 3]
+    idx_ab = idx_map[0::6]
+    idx_ac = idx_map[1::6]
+    idx_ad = idx_map[2::6]
+    idx_bc = idx_map[3::6]
+    idx_bd = idx_map[4::6]
+    idx_cd = idx_map[5::6]
+
+    tet_1 = torch.stack([idx_a, idx_ab, idx_ac, idx_ad], dim=1)
+    tet_2 = torch.stack([idx_b, idx_bc, idx_ab, idx_bd], dim=1)
+    tet_3 = torch.stack([idx_c, idx_ac, idx_bc, idx_cd], dim=1)
+    tet_4 = torch.stack([idx_d, idx_ad, idx_cd, idx_bd], dim=1)
+    tet_5 = torch.stack([idx_ab, idx_ac, idx_ad, idx_bd], dim=1)
+    tet_6 = torch.stack([idx_ab, idx_ac, idx_bd, idx_bc], dim=1)
+    tet_7 = torch.stack([idx_cd, idx_ac, idx_bd, idx_ad], dim=1)
+    tet_8 = torch.stack([idx_cd, idx_ac, idx_bc, idx_bd], dim=1)
+
+    tet_np = torch.cat([tet_1, tet_2, tet_3, tet_4, tet_5, tet_6, tet_7, tet_8], dim=0)
+    tet_np = tet_np.reshape(1, -1, 4).expand(tet_pos_bxnx3.shape[0], -1, -1)
+    tet = tet_np.long().to(device)
+
+    return new_v, tet, new_sdf
+
+
+###############################################################################
+# Adjacency
+###############################################################################
+def tet_to_tet_adj_sparse(tet_tx4):
+    # include self connection!!!!!!!!!!!!!!!!!!!
+    with torch.no_grad():
+        t = tet_tx4.shape[0]
+        device = tet_tx4.device
+        idx_array = torch.LongTensor(
+            [0, 1, 2,
+             1, 0, 3,
+             2, 3, 0,
+             3, 2, 1]).to(device).reshape(4, 3).unsqueeze(0).expand(t, -1, -1)  # (t, 4, 3)
+
+        # get all faces
+        all_faces = torch.gather(input=tet_tx4.unsqueeze(1).expand(-1, 4, -1), index=idx_array, dim=-1).reshape(
+            -1,
+            3)  # (tx4, 3)
+        all_faces_tet_idx = torch.arange(t, device=device).unsqueeze(-1).expand(-1, 4).reshape(-1)
+        # sort and group
+        all_faces_sorted, _ = torch.sort(all_faces, dim=1)
+
+        all_faces_unique, inverse_indices, counts = torch.unique(
+            all_faces_sorted, dim=0, return_counts=True,
+            return_inverse=True)
+        tet_face_fx3 = all_faces_unique[counts == 2]
+        counts = counts[inverse_indices]  # tx4
+        valid = (counts == 2)
+
+        group = inverse_indices[valid]
+        # print (inverse_indices.shape, group.shape, all_faces_tet_idx.shape)
+        _, indices = torch.sort(group)
+        all_faces_tet_idx_grouped = all_faces_tet_idx[valid][indices]
+        tet_face_tetidx_fx2 = torch.stack([all_faces_tet_idx_grouped[::2], all_faces_tet_idx_grouped[1::2]], dim=-1)
+
+        tet_adj_idx = torch.cat([tet_face_tetidx_fx2, torch.flip(tet_face_tetidx_fx2, [1])])
+        adj_self = torch.arange(t, device=tet_tx4.device)
+        adj_self = torch.stack([adj_self, adj_self], -1)
+        tet_adj_idx = torch.cat([tet_adj_idx, adj_self])
+
+        tet_adj_idx = torch.unique(tet_adj_idx, dim=0)
+        values = torch.ones(
+            tet_adj_idx.shape[0], device=tet_tx4.device).float()
+        adj_sparse = torch.sparse.FloatTensor(
+            tet_adj_idx.t(), values, torch.Size([t, t]))
+
+        # normalization
+        neighbor_num = 1.0 / torch.sparse.sum(
+            adj_sparse, dim=1).to_dense()
+        values = torch.index_select(neighbor_num, 0, tet_adj_idx[:, 0])
+        adj_sparse = torch.sparse.FloatTensor(
+            tet_adj_idx.t(), values, torch.Size([t, t]))
+    return adj_sparse
+
+
+###############################################################################
+# Compact grid
+###############################################################################
+
+def get_tet_bxfx4x3(bxnxz, bxfx4):
+    n_batch, z = bxnxz.shape[0], bxnxz.shape[2]
+    gather_input = bxnxz.unsqueeze(2).expand(
+        n_batch, bxnxz.shape[1], 4, z)
+    gather_index = bxfx4.unsqueeze(-1).expand(
+        n_batch, bxfx4.shape[1], 4, z).long()
+    tet_bxfx4xz = torch.gather(
+        input=gather_input, dim=1, index=gather_index)
+
+    return tet_bxfx4xz
+
+
+def shrink_grid(tet_pos_bxnx3, tet_bxfx4, grid_sdf):
+    with torch.no_grad():
+        assert tet_pos_bxnx3.shape[0] == 1
+
+        occ = grid_sdf[0] > 0
+        occ_sum = get_tet_bxfx4x3(occ.unsqueeze(0).unsqueeze(-1), tet_bxfx4).reshape(-1, 4).sum(-1)
+        mask = (occ_sum > 0) & (occ_sum < 4)
+
+        # build connectivity graph
+        adj_matrix = tet_to_tet_adj_sparse(tet_bxfx4[0])
+        mask = mask.float().unsqueeze(-1)
+
+        # Include a one ring of neighbors
+        for i in range(1):
+            mask = torch.sparse.mm(adj_matrix, mask)
+        mask = mask.squeeze(-1) > 0
+
+        mapping = torch.zeros((tet_pos_bxnx3.shape[1]), device=tet_pos_bxnx3.device, dtype=torch.long)
+        new_tet_bxfx4 = tet_bxfx4[:, mask].long()
+        selected_verts_idx = torch.unique(new_tet_bxfx4)
+        new_tet_pos_bxnx3 = tet_pos_bxnx3[:, selected_verts_idx]
+        mapping[selected_verts_idx] = torch.arange(selected_verts_idx.shape[0], device=tet_pos_bxnx3.device)
+        new_tet_bxfx4 = mapping[new_tet_bxfx4.reshape(-1)].reshape(new_tet_bxfx4.shape)
+        new_grid_sdf = grid_sdf[:, selected_verts_idx]
+        return new_tet_pos_bxnx3, new_tet_bxfx4, new_grid_sdf
+
+
+###############################################################################
+# Regularizer
+###############################################################################
+
+def sdf_reg_loss(sdf, all_edges):
+    sdf_f1x6x2 = sdf[all_edges.reshape(-1)].reshape(-1, 2)
+    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
+    sdf_f1x6x2 = sdf_f1x6x2[mask]
+    sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(
+        sdf_f1x6x2[..., 0],
+        (sdf_f1x6x2[..., 1] > 0).float()) + \
+               torch.nn.functional.binary_cross_entropy_with_logits(
+                   sdf_f1x6x2[..., 1],
+                   (sdf_f1x6x2[..., 0] > 0).float())
+    return sdf_diff
+
+
+def sdf_reg_loss_batch(sdf, all_edges):
+    sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2)
+    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
+    sdf_f1x6x2 = sdf_f1x6x2[mask]
+    sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \
+               torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float())
+    return sdf_diff
+
+
+###############################################################################
+#  Geometry interface
+###############################################################################
+class DMTetGeometry(Geometry):
+    def __init__(
+            self, grid_res=64, scale=2.0, device='cuda', renderer=None,
+            render_type='neural_render', args=None):
+        super(DMTetGeometry, self).__init__()
+        self.grid_res = grid_res
+        self.device = device
+        self.args = args
+        tets = np.load('data/tets/%d_compress.npz' % (grid_res))
+        self.verts = torch.from_numpy(tets['vertices']).float().to(self.device)
+        # Make sure the tet is zero-centered and length is equal to 1
+        length = self.verts.max(dim=0)[0] - self.verts.min(dim=0)[0]
+        length = length.max()
+        mid = (self.verts.max(dim=0)[0] + self.verts.min(dim=0)[0]) / 2.0
+        self.verts = (self.verts - mid.unsqueeze(dim=0)) / length
+        if isinstance(scale, list):
+            self.verts[:, 0] = self.verts[:, 0] * scale[0]
+            self.verts[:, 1] = self.verts[:, 1] * scale[1]
+            self.verts[:, 2] = self.verts[:, 2] * scale[1]
+        else:
+            self.verts = self.verts * scale
+        self.indices = torch.from_numpy(tets['tets']).long().to(self.device)
+        self.triangle_table, self.num_triangles_table, self.base_tet_edges, self.v_id = create_mt_variable(self.device)
+        self.tet_table, self.num_tets_table = create_tetmesh_variables(self.device)
+        # Parameters for regularization computation
+        edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=self.device)
+        all_edges = self.indices[:, edges].reshape(-1, 2)
+        all_edges_sorted = torch.sort(all_edges, dim=1)[0]
+        self.all_edges = torch.unique(all_edges_sorted, dim=0)
+
+        # Parameters used for fix boundary sdf
+        self.center_indices, self.boundary_indices = get_center_boundary_index(self.verts)
+        self.renderer = renderer
+        self.render_type = render_type
+
+    def getAABB(self):
+        return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values
+
+    def get_mesh(self, v_deformed_nx3, sdf_n, with_uv=False, indices=None):
+        if indices is None:
+            indices = self.indices
+        verts, faces = marching_tets(
+            v_deformed_nx3, sdf_n, indices, self.triangle_table,
+            self.num_triangles_table, self.base_tet_edges, self.v_id)
+        faces = torch.cat(
+            [faces[:, 0:1],
+             faces[:, 2:3],
+             faces[:, 1:2], ], dim=-1)
+        return verts, faces
+
+    def get_tet_mesh(self, v_deformed_nx3, sdf_n, with_uv=False, indices=None):
+        if indices is None:
+            indices = self.indices
+        verts, faces, tet_verts, tets = marching_tets_tetmesh(
+            v_deformed_nx3, sdf_n, indices, self.triangle_table,
+            self.num_triangles_table, self.base_tet_edges, self.v_id, return_tet_mesh=True,
+            num_tets_table=self.num_tets_table, tet_table=self.tet_table, ori_v=v_deformed_nx3)
+        faces = torch.cat(
+            [faces[:, 0:1],
+             faces[:, 2:3],
+             faces[:, 1:2], ], dim=-1)
+        return verts, faces, tet_verts, tets
+
+    def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False):
+        return_value = dict()
+        if self.render_type == 'neural_render':
+            tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth = self.renderer.render_mesh(
+                mesh_v_nx3.unsqueeze(dim=0),
+                mesh_f_fx3.int(),
+                camera_mv_bx4x4,
+                mesh_v_nx3.unsqueeze(dim=0),
+                resolution=resolution,
+                device=self.device,
+                hierarchical_mask=hierarchical_mask
+            )
+
+            return_value['tex_pos'] = tex_pos
+            return_value['mask'] = mask
+            return_value['hard_mask'] = hard_mask
+            return_value['rast'] = rast
+            return_value['v_pos_clip'] = v_pos_clip
+            return_value['mask_pyramid'] = mask_pyramid
+            return_value['depth'] = depth
+        else:
+            raise NotImplementedError
+
+        return return_value
+
+    def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256):
+        # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1
+        v_list = []
+        f_list = []
+        n_batch = v_deformed_bxnx3.shape[0]
+        all_render_output = []
+        for i_batch in range(n_batch):
+            verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch])
+            v_list.append(verts_nx3)
+            f_list.append(faces_fx3)
+            render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution)
+            all_render_output.append(render_output)
+
+        # Concatenate all render output
+        return_keys = all_render_output[0].keys()
+        return_value = dict()
+        for k in return_keys:
+            value = [v[k] for v in all_render_output]
+            return_value[k] = value
+            # We can do concatenation outside of the render
+        return return_value
diff --git a/nsr/geometry/rep_3d/dmtet_utils.py b/nsr/geometry/rep_3d/dmtet_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d466a9e78c49d947c115707693aa18d759885ad
--- /dev/null
+++ b/nsr/geometry/rep_3d/dmtet_utils.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+
+
+def get_center_boundary_index(verts):
+    length_ = torch.sum(verts ** 2, dim=-1)
+    center_idx = torch.argmin(length_)
+    boundary_neg = verts == verts.max()
+    boundary_pos = verts == verts.min()
+    boundary = torch.bitwise_or(boundary_pos, boundary_neg)
+    boundary = torch.sum(boundary.float(), dim=-1)
+    boundary_idx = torch.nonzero(boundary)
+    return center_idx, boundary_idx.squeeze(dim=-1)
diff --git a/nsr/geometry/rep_3d/extract_texture_map.py b/nsr/geometry/rep_3d/extract_texture_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d62bb5a6c5cdf632fb504db3d2dfa99a3abbd3
--- /dev/null
+++ b/nsr/geometry/rep_3d/extract_texture_map.py
@@ -0,0 +1,40 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+import xatlas
+import numpy as np
+import nvdiffrast.torch as dr
+
+
+# ==============================================================================================
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
+
+
+def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution):
+    vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy())
+
+    # Convert to tensors
+    indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
+
+    uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
+    mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
+    # mesh_v_tex. ture
+    uv_clip = uvs[None, ...] * 2.0 - 1.0
+
+    # pad to four component coordinate
+    uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
+
+    # rasterize
+    rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution))
+
+    # Interpolate world space position
+    gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
+    mask = rast[..., 3:4] > 0
+    return uvs, mesh_tex_idx, gb_pos, mask
diff --git a/nsr/geometry/rep_3d/flexicubes.py b/nsr/geometry/rep_3d/flexicubes.py
new file mode 100644
index 0000000000000000000000000000000000000000..26d7b91b6266d802baaf55b64238629cd0f740d0
--- /dev/null
+++ b/nsr/geometry/rep_3d/flexicubes.py
@@ -0,0 +1,579 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+import torch
+from .tables import *
+
+__all__ = [
+    'FlexiCubes'
+]
+
+
+class FlexiCubes:
+    """
+    This class implements the FlexiCubes method for extracting meshes from scalar fields. 
+    It maintains a series of lookup tables and indices to support the mesh extraction process. 
+    FlexiCubes, a differentiable variant of the Dual Marching Cubes (DMC) scheme, enhances 
+    the geometric fidelity and mesh quality of reconstructed meshes by dynamically adjusting 
+    the surface representation through gradient-based optimization.
+
+    During instantiation, the class loads DMC tables from a file and transforms them into 
+    PyTorch tensors on the specified device.
+
+    Attributes:
+        device (str): Specifies the computational device (default is "cuda").
+        dmc_table (torch.Tensor): Dual Marching Cubes (DMC) table that encodes the edges 
+            associated with each dual vertex in 256 Marching Cubes (MC) configurations.
+        num_vd_table (torch.Tensor): Table holding the number of dual vertices in each of 
+            the 256 MC configurations.
+        check_table (torch.Tensor): Table resolving ambiguity in cases C16 and C19 
+            of the DMC configurations.
+        tet_table (torch.Tensor): Lookup table used in tetrahedralizing the isosurface.
+        quad_split_1 (torch.Tensor): Indices for splitting a quad into two triangles 
+            along one diagonal.
+        quad_split_2 (torch.Tensor): Alternative indices for splitting a quad into 
+            two triangles along the other diagonal.
+        quad_split_train (torch.Tensor): Indices for splitting a quad into four triangles 
+            during training by connecting all edges to their midpoints.
+        cube_corners (torch.Tensor): Defines the positions of a standard unit cube's 
+            eight corners in 3D space, ordered starting from the origin (0,0,0), 
+            moving along the x-axis, then y-axis, and finally z-axis. 
+            Used as a blueprint for generating a voxel grid.
+        cube_corners_idx (torch.Tensor): Cube corners indexed as powers of 2, used 
+            to retrieve the case id.
+        cube_edges (torch.Tensor): Edge connections in a cube, listed in pairs. 
+            Used to retrieve edge vertices in DMC.
+        edge_dir_table (torch.Tensor): A mapping tensor that associates edge indices with 
+            their corresponding axis. For instance, edge_dir_table[0] = 0 indicates that the 
+            first edge is oriented along the x-axis. 
+        dir_faces_table (torch.Tensor): A tensor that maps the corresponding axis of shared edges 
+            across four adjacent cubes to the shared faces of these cubes. For instance, 
+            dir_faces_table[0] = [5, 4] implies that for four cubes sharing an edge along 
+            the x-axis, the first and second cubes share faces indexed as 5 and 4, respectively. 
+            This tensor is only utilized during isosurface tetrahedralization.
+        adj_pairs (torch.Tensor): 
+            A tensor containing index pairs that correspond to neighboring cubes that share the same edge.
+        qef_reg_scale (float):
+            The scaling factor applied to the regularization loss to prevent issues with singularity 
+            when solving the QEF. This parameter is only used when a 'grad_func' is specified.
+        weight_scale (float):
+            The scale of weights in FlexiCubes. Should be between 0 and 1.
+    """
+
+    def __init__(self, device="cuda", qef_reg_scale=1e-3, weight_scale=0.99):
+
+        self.device = device
+        self.dmc_table = torch.tensor(dmc_table, dtype=torch.long, device=device, requires_grad=False)
+        self.num_vd_table = torch.tensor(num_vd_table,
+                                         dtype=torch.long, device=device, requires_grad=False)
+        self.check_table = torch.tensor(
+            check_table,
+            dtype=torch.long, device=device, requires_grad=False)
+
+        self.tet_table = torch.tensor(tet_table, dtype=torch.long, device=device, requires_grad=False)
+        self.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=device, requires_grad=False)
+        self.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=device, requires_grad=False)
+        self.quad_split_train = torch.tensor(
+            [0, 1, 1, 2, 2, 3, 3, 0], dtype=torch.long, device=device, requires_grad=False)
+
+        self.cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [
+                                         1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.float, device=device)
+        self.cube_corners_idx = torch.pow(2, torch.arange(8, requires_grad=False))
+        self.cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6,
+                                       2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, device=device, requires_grad=False)
+
+        self.edge_dir_table = torch.tensor([0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1],
+                                           dtype=torch.long, device=device)
+        self.dir_faces_table = torch.tensor([
+            [[5, 4], [3, 2], [4, 5], [2, 3]],
+            [[5, 4], [1, 0], [4, 5], [0, 1]],
+            [[3, 2], [1, 0], [2, 3], [0, 1]]
+        ], dtype=torch.long, device=device)
+        self.adj_pairs = torch.tensor([0, 1, 1, 3, 3, 2, 2, 0], dtype=torch.long, device=device)
+        self.qef_reg_scale = qef_reg_scale
+        self.weight_scale = weight_scale
+
+    def construct_voxel_grid(self, res):
+        """
+        Generates a voxel grid based on the specified resolution.
+
+        Args:
+            res (int or list[int]): The resolution of the voxel grid. If an integer
+                is provided, it is used for all three dimensions. If a list or tuple 
+                of 3 integers is provided, they define the resolution for the x, 
+                y, and z dimensions respectively.
+
+        Returns:
+            (torch.Tensor, torch.Tensor): Returns the vertices and the indices of the 
+                cube corners (index into vertices) of the constructed voxel grid. 
+                The vertices are centered at the origin, with the length of each 
+                dimension in the grid being one.
+        """
+        base_cube_f = torch.arange(8).to(self.device)
+        if isinstance(res, int):
+            res = (res, res, res)
+        voxel_grid_template = torch.ones(res, device=self.device)
+
+        res = torch.tensor([res], dtype=torch.float, device=self.device)
+        coords = torch.nonzero(voxel_grid_template).float() / res  # N, 3
+        verts = (self.cube_corners.unsqueeze(0) / res + coords.unsqueeze(1)).reshape(-1, 3)
+        cubes = (base_cube_f.unsqueeze(0) +
+                 torch.arange(coords.shape[0], device=self.device).unsqueeze(1) * 8).reshape(-1)
+
+        verts_rounded = torch.round(verts * 10**5) / (10**5)
+        verts_unique, inverse_indices = torch.unique(verts_rounded, dim=0, return_inverse=True)
+        cubes = inverse_indices[cubes.reshape(-1)].reshape(-1, 8)
+
+        return verts_unique - 0.5, cubes
+
+    def __call__(self, x_nx3, s_n, cube_fx8, res, beta_fx12=None, alpha_fx8=None,
+                 gamma_f=None, training=False, output_tetmesh=False, grad_func=None):
+        r"""
+        Main function for mesh extraction from scalar field using FlexiCubes. This function converts 
+        discrete signed distance fields, encoded on voxel grids and additional per-cube parameters, 
+        to triangle or tetrahedral meshes using a differentiable operation as described in 
+        `Flexible Isosurface Extraction for Gradient-Based Mesh Optimization`_. FlexiCubes enhances 
+        mesh quality and geometric fidelity by adjusting the surface representation based on gradient 
+        optimization. The output surface is differentiable with respect to the input vertex positions, 
+        scalar field values, and weight parameters.
+
+        If you intend to extract a surface mesh from a fixed Signed Distance Field without the 
+        optimization of parameters, it is suggested to provide the "grad_func" which should 
+        return the surface gradient at any given 3D position. When grad_func is provided, the process 
+        to determine the dual vertex position adapts to solve a Quadratic Error Function (QEF), as 
+        described in the `Manifold Dual Contouring`_ paper, and employs an smart splitting strategy. 
+        Please note, this approach is non-differentiable.
+
+        For more details and example usage in optimization, refer to the 
+        `Flexible Isosurface Extraction for Gradient-Based Mesh Optimization`_ SIGGRAPH 2023 paper.
+
+        Args:
+            x_nx3 (torch.Tensor): Coordinates of the voxel grid vertices, can be deformed.
+            s_n (torch.Tensor): Scalar field values at each vertex of the voxel grid. Negative values 
+                denote that the corresponding vertex resides inside the isosurface. This affects 
+                the directions of the extracted triangle faces and volume to be tetrahedralized.
+            cube_fx8 (torch.Tensor): Indices of 8 vertices for each cube in the voxel grid.
+            res (int or list[int]): The resolution of the voxel grid. If an integer is provided, it 
+                is used for all three dimensions. If a list or tuple of 3 integers is provided, they 
+                specify the resolution for the x, y, and z dimensions respectively.
+            beta_fx12 (torch.Tensor, optional): Weight parameters for the cube edges to adjust dual 
+                vertices positioning. Defaults to uniform value for all edges.
+            alpha_fx8 (torch.Tensor, optional): Weight parameters for the cube corners to adjust dual 
+                vertices positioning. Defaults to uniform value for all vertices.
+            gamma_f (torch.Tensor, optional): Weight parameters to control the splitting of 
+                quadrilaterals into triangles. Defaults to uniform value for all cubes.
+            training (bool, optional): If set to True, applies differentiable quad splitting for 
+                training. Defaults to False.
+            output_tetmesh (bool, optional): If set to True, outputs a tetrahedral mesh, otherwise, 
+                outputs a triangular mesh. Defaults to False.
+            grad_func (callable, optional): A function to compute the surface gradient at specified 
+                3D positions (input: Nx3 positions). The function should return gradients as an Nx3 
+                tensor. If None, the original FlexiCubes algorithm is utilized. Defaults to None.
+
+        Returns:
+            (torch.Tensor, torch.LongTensor, torch.Tensor): Tuple containing:
+                - Vertices for the extracted triangular/tetrahedral mesh.
+                - Faces for the extracted triangular/tetrahedral mesh.
+                - Regularizer L_dev, computed per dual vertex.
+
+        .. _Flexible Isosurface Extraction for Gradient-Based Mesh Optimization:
+            https://research.nvidia.com/labs/toronto-ai/flexicubes/
+        .. _Manifold Dual Contouring:
+            https://people.engr.tamu.edu/schaefer/research/dualsimp_tvcg.pdf
+        """
+
+        surf_cubes, occ_fx8 = self._identify_surf_cubes(s_n, cube_fx8)
+        if surf_cubes.sum() == 0:
+            return torch.zeros(
+                (0, 3),
+                device=self.device), torch.zeros(
+                (0, 4),
+                dtype=torch.long, device=self.device) if output_tetmesh else torch.zeros(
+                (0, 3),
+                dtype=torch.long, device=self.device), torch.zeros(
+                (0),
+                device=self.device)
+        beta_fx12, alpha_fx8, gamma_f = self._normalize_weights(beta_fx12, alpha_fx8, gamma_f, surf_cubes)
+
+        case_ids = self._get_case_id(occ_fx8, surf_cubes, res)
+
+        surf_edges, idx_map, edge_counts, surf_edges_mask = self._identify_surf_edges(s_n, cube_fx8, surf_cubes)
+
+        vd, L_dev, vd_gamma, vd_idx_map = self._compute_vd(
+            x_nx3, cube_fx8[surf_cubes], surf_edges, s_n, case_ids, beta_fx12, alpha_fx8, gamma_f, idx_map, grad_func)
+        vertices, faces, s_edges, edge_indices = self._triangulate(
+            s_n, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, grad_func)
+        if not output_tetmesh:
+            return vertices, faces, L_dev
+        else:
+            vertices, tets = self._tetrahedralize(
+                x_nx3, s_n, cube_fx8, vertices, faces, surf_edges, s_edges, vd_idx_map, case_ids, edge_indices,
+                surf_cubes, training)
+            return vertices, tets, L_dev
+
+    def _compute_reg_loss(self, vd, ue, edge_group_to_vd, vd_num_edges):
+        """
+        Regularizer L_dev as in Equation 8
+        """
+        dist = torch.norm(ue - torch.index_select(input=vd, index=edge_group_to_vd, dim=0), dim=-1)
+        mean_l2 = torch.zeros_like(vd[:, 0])
+        mean_l2 = (mean_l2).index_add_(0, edge_group_to_vd, dist) / vd_num_edges.squeeze(1).float()
+        mad = (dist - torch.index_select(input=mean_l2, index=edge_group_to_vd, dim=0)).abs()
+        return mad
+
+    def _normalize_weights(self, beta_fx12, alpha_fx8, gamma_f, surf_cubes):
+        """
+        Normalizes the given weights to be non-negative. If input weights are None, it creates and returns a set of weights of ones.
+        """
+        n_cubes = surf_cubes.shape[0]
+
+        if beta_fx12 is not None:
+            beta_fx12 = (torch.tanh(beta_fx12) * self.weight_scale + 1)
+        else:
+            beta_fx12 = torch.ones((n_cubes, 12), dtype=torch.float, device=self.device)
+
+        if alpha_fx8 is not None:
+            alpha_fx8 = (torch.tanh(alpha_fx8) * self.weight_scale + 1)
+        else:
+            alpha_fx8 = torch.ones((n_cubes, 8), dtype=torch.float, device=self.device)
+
+        if gamma_f is not None:
+            gamma_f = torch.sigmoid(gamma_f) * self.weight_scale + (1 - self.weight_scale)/2
+        else:
+            gamma_f = torch.ones((n_cubes), dtype=torch.float, device=self.device)
+
+        return beta_fx12[surf_cubes], alpha_fx8[surf_cubes], gamma_f[surf_cubes]
+
+    @torch.no_grad()
+    def _get_case_id(self, occ_fx8, surf_cubes, res):
+        """
+        Obtains the ID of topology cases based on cell corner occupancy. This function resolves the 
+        ambiguity in the Dual Marching Cubes (DMC) configurations as described in Section 1.3 of the 
+        supplementary material. It should be noted that this function assumes a regular grid.
+        """
+        case_ids = (occ_fx8[surf_cubes] * self.cube_corners_idx.to(self.device).unsqueeze(0)).sum(-1)
+
+        problem_config = self.check_table.to(self.device)[case_ids]
+        to_check = problem_config[..., 0] == 1
+        problem_config = problem_config[to_check]
+        if not isinstance(res, (list, tuple)):
+            res = [res, res, res]
+
+        # The 'problematic_configs' only contain configurations for surface cubes. Next, we construct a 3D array,
+        # 'problem_config_full', to store configurations for all cubes (with default config for non-surface cubes).
+        # This allows efficient checking on adjacent cubes.
+        problem_config_full = torch.zeros(list(res) + [5], device=self.device, dtype=torch.long)
+        vol_idx = torch.nonzero(problem_config_full[..., 0] == 0)  # N, 3
+        vol_idx_problem = vol_idx[surf_cubes][to_check]
+        problem_config_full[vol_idx_problem[..., 0], vol_idx_problem[..., 1], vol_idx_problem[..., 2]] = problem_config
+        vol_idx_problem_adj = vol_idx_problem + problem_config[..., 1:4]
+
+        within_range = (
+            vol_idx_problem_adj[..., 0] >= 0) & (
+            vol_idx_problem_adj[..., 0] < res[0]) & (
+            vol_idx_problem_adj[..., 1] >= 0) & (
+            vol_idx_problem_adj[..., 1] < res[1]) & (
+            vol_idx_problem_adj[..., 2] >= 0) & (
+            vol_idx_problem_adj[..., 2] < res[2])
+
+        vol_idx_problem = vol_idx_problem[within_range]
+        vol_idx_problem_adj = vol_idx_problem_adj[within_range]
+        problem_config = problem_config[within_range]
+        problem_config_adj = problem_config_full[vol_idx_problem_adj[..., 0],
+                                                 vol_idx_problem_adj[..., 1], vol_idx_problem_adj[..., 2]]
+        # If two cubes with cases C16 and C19 share an ambiguous face, both cases are inverted.
+        to_invert = (problem_config_adj[..., 0] == 1)
+        idx = torch.arange(case_ids.shape[0], device=self.device)[to_check][within_range][to_invert]
+        case_ids.index_put_((idx,), problem_config[to_invert][..., -1])
+        return case_ids
+
+    @torch.no_grad()
+    def _identify_surf_edges(self, s_n, cube_fx8, surf_cubes):
+        """
+        Identifies grid edges that intersect with the underlying surface by checking for opposite signs. As each edge 
+        can be shared by multiple cubes, this function also assigns a unique index to each surface-intersecting edge 
+        and marks the cube edges with this index.
+        """
+        occ_n = s_n < 0
+        all_edges = cube_fx8[surf_cubes][:, self.cube_edges].reshape(-1, 2)
+        unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True)
+
+        unique_edges = unique_edges.long()
+        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
+
+        surf_edges_mask = mask_edges[_idx_map]
+        counts = counts[_idx_map]
+
+        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=cube_fx8.device) * -1
+        mapping[mask_edges] = torch.arange(mask_edges.sum(), device=cube_fx8.device)
+        # Shaped as [number of cubes x 12 edges per cube]. This is later used to map a cube edge to the unique index
+        # for a surface-intersecting edge. Non-surface-intersecting edges are marked with -1.
+        idx_map = mapping[_idx_map]
+        surf_edges = unique_edges[mask_edges]
+        return surf_edges, idx_map, counts, surf_edges_mask
+
+    @torch.no_grad()
+    def _identify_surf_cubes(self, s_n, cube_fx8):
+        """
+        Identifies grid cubes that intersect with the underlying surface by checking if the signs at 
+        all corners are not identical.
+        """
+        occ_n = s_n < 0
+        occ_fx8 = occ_n[cube_fx8.reshape(-1)].reshape(-1, 8)
+        _occ_sum = torch.sum(occ_fx8, -1)
+        surf_cubes = (_occ_sum > 0) & (_occ_sum < 8)
+        return surf_cubes, occ_fx8
+
+    def _linear_interp(self, edges_weight, edges_x):
+        """
+        Computes the location of zero-crossings on 'edges_x' using linear interpolation with 'edges_weight'.
+        """
+        edge_dim = edges_weight.dim() - 2
+        assert edges_weight.shape[edge_dim] == 2
+        edges_weight = torch.cat([torch.index_select(input=edges_weight, index=torch.tensor(1, device=self.device), dim=edge_dim), -
+                                 torch.index_select(input=edges_weight, index=torch.tensor(0, device=self.device), dim=edge_dim)], edge_dim)
+        denominator = edges_weight.sum(edge_dim)
+        ue = (edges_x * edges_weight).sum(edge_dim) / denominator
+        return ue
+
+    def _solve_vd_QEF(self, p_bxnx3, norm_bxnx3, c_bx3=None):
+        p_bxnx3 = p_bxnx3.reshape(-1, 7, 3)
+        norm_bxnx3 = norm_bxnx3.reshape(-1, 7, 3)
+        c_bx3 = c_bx3.reshape(-1, 3)
+        A = norm_bxnx3
+        B = ((p_bxnx3) * norm_bxnx3).sum(-1, keepdims=True)
+
+        A_reg = (torch.eye(3, device=p_bxnx3.device) * self.qef_reg_scale).unsqueeze(0).repeat(p_bxnx3.shape[0], 1, 1)
+        B_reg = (self.qef_reg_scale * c_bx3).unsqueeze(-1)
+        A = torch.cat([A, A_reg], 1)
+        B = torch.cat([B, B_reg], 1)
+        dual_verts = torch.linalg.lstsq(A, B).solution.squeeze(-1)
+        return dual_verts
+
+    def _compute_vd(self, x_nx3, surf_cubes_fx8, surf_edges, s_n, case_ids, beta_fx12, alpha_fx8, gamma_f, idx_map, grad_func):
+        """
+        Computes the location of dual vertices as described in Section 4.2
+        """
+        alpha_nx12x2 = torch.index_select(input=alpha_fx8, index=self.cube_edges, dim=1).reshape(-1, 12, 2)
+        surf_edges_x = torch.index_select(input=x_nx3, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 3)
+        surf_edges_s = torch.index_select(input=s_n, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 1)
+        zero_crossing = self._linear_interp(surf_edges_s, surf_edges_x)
+
+        idx_map = idx_map.reshape(-1, 12)
+        num_vd = torch.index_select(input=self.num_vd_table, index=case_ids, dim=0)
+        edge_group, edge_group_to_vd, edge_group_to_cube, vd_num_edges, vd_gamma = [], [], [], [], []
+
+        total_num_vd = 0
+        vd_idx_map = torch.zeros((case_ids.shape[0], 12), dtype=torch.long, device=self.device, requires_grad=False)
+        if grad_func is not None:
+            normals = torch.nn.functional.normalize(grad_func(zero_crossing), dim=-1)
+            vd = []
+        for num in torch.unique(num_vd):
+            cur_cubes = (num_vd == num)  # consider cubes with the same numbers of vd emitted (for batching)
+            curr_num_vd = cur_cubes.sum() * num
+            curr_edge_group = self.dmc_table[case_ids[cur_cubes], :num].reshape(-1, num * 7)
+            curr_edge_group_to_vd = torch.arange(
+                curr_num_vd, device=self.device).unsqueeze(-1).repeat(1, 7) + total_num_vd
+            total_num_vd += curr_num_vd
+            curr_edge_group_to_cube = torch.arange(idx_map.shape[0], device=self.device)[
+                cur_cubes].unsqueeze(-1).repeat(1, num * 7).reshape_as(curr_edge_group)
+
+            curr_mask = (curr_edge_group != -1)
+            edge_group.append(torch.masked_select(curr_edge_group, curr_mask))
+            edge_group_to_vd.append(torch.masked_select(curr_edge_group_to_vd.reshape_as(curr_edge_group), curr_mask))
+            edge_group_to_cube.append(torch.masked_select(curr_edge_group_to_cube, curr_mask))
+            vd_num_edges.append(curr_mask.reshape(-1, 7).sum(-1, keepdims=True))
+            vd_gamma.append(torch.masked_select(gamma_f, cur_cubes).unsqueeze(-1).repeat(1, num).reshape(-1))
+
+            if grad_func is not None:
+                with torch.no_grad():
+                    cube_e_verts_idx = idx_map[cur_cubes]
+                    curr_edge_group[~curr_mask] = 0
+
+                    verts_group_idx = torch.gather(input=cube_e_verts_idx, dim=1, index=curr_edge_group)
+                    verts_group_idx[verts_group_idx == -1] = 0
+                    verts_group_pos = torch.index_select(
+                        input=zero_crossing, index=verts_group_idx.reshape(-1), dim=0).reshape(-1, num.item(), 7, 3)
+                    v0 = x_nx3[surf_cubes_fx8[cur_cubes][:, 0]].reshape(-1, 1, 1, 3).repeat(1, num.item(), 1, 1)
+                    curr_mask = curr_mask.reshape(-1, num.item(), 7, 1)
+                    verts_centroid = (verts_group_pos * curr_mask).sum(2) / (curr_mask.sum(2))
+
+                    normals_bx7x3 = torch.index_select(input=normals, index=verts_group_idx.reshape(-1), dim=0).reshape(
+                        -1, num.item(), 7,
+                        3)
+                    curr_mask = curr_mask.squeeze(2)
+                    vd.append(self._solve_vd_QEF((verts_group_pos - v0) * curr_mask, normals_bx7x3 * curr_mask,
+                                                 verts_centroid - v0.squeeze(2)) + v0.reshape(-1, 3))
+        edge_group = torch.cat(edge_group)
+        edge_group_to_vd = torch.cat(edge_group_to_vd)
+        edge_group_to_cube = torch.cat(edge_group_to_cube)
+        vd_num_edges = torch.cat(vd_num_edges)
+        vd_gamma = torch.cat(vd_gamma)
+
+        if grad_func is not None:
+            vd = torch.cat(vd)
+            L_dev = torch.zeros([1], device=self.device)
+        else:
+            vd = torch.zeros((total_num_vd, 3), device=self.device)
+            beta_sum = torch.zeros((total_num_vd, 1), device=self.device)
+
+            idx_group = torch.gather(input=idx_map.reshape(-1), dim=0, index=edge_group_to_cube * 12 + edge_group)
+
+            x_group = torch.index_select(input=surf_edges_x, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 3)
+            s_group = torch.index_select(input=surf_edges_s, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 1)
+
+            zero_crossing_group = torch.index_select(
+                input=zero_crossing, index=idx_group.reshape(-1), dim=0).reshape(-1, 3)
+
+            alpha_group = torch.index_select(input=alpha_nx12x2.reshape(-1, 2), dim=0,
+                                             index=edge_group_to_cube * 12 + edge_group).reshape(-1, 2, 1)
+            ue_group = self._linear_interp(s_group * alpha_group, x_group)
+
+            beta_group = torch.gather(input=beta_fx12.reshape(-1), dim=0,
+                                      index=edge_group_to_cube * 12 + edge_group).reshape(-1, 1)
+            beta_sum = beta_sum.index_add_(0, index=edge_group_to_vd, source=beta_group)
+            vd = vd.index_add_(0, index=edge_group_to_vd, source=ue_group * beta_group) / beta_sum
+            L_dev = self._compute_reg_loss(vd, zero_crossing_group, edge_group_to_vd, vd_num_edges)
+
+        v_idx = torch.arange(vd.shape[0], device=self.device)  # + total_num_vd
+
+        vd_idx_map = (vd_idx_map.reshape(-1)).scatter(dim=0, index=edge_group_to_cube *
+                                                      12 + edge_group, src=v_idx[edge_group_to_vd])
+
+        return vd, L_dev, vd_gamma, vd_idx_map
+
+    def _triangulate(self, s_n, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, grad_func):
+        """
+        Connects four neighboring dual vertices to form a quadrilateral. The quadrilaterals are then split into 
+        triangles based on the gamma parameter, as described in Section 4.3.
+        """
+        with torch.no_grad():
+            group_mask = (edge_counts == 4) & surf_edges_mask  # surface edges shared by 4 cubes.
+            group = idx_map.reshape(-1)[group_mask]
+            vd_idx = vd_idx_map[group_mask]
+            edge_indices, indices = torch.sort(group, stable=True)
+            quad_vd_idx = vd_idx[indices].reshape(-1, 4)
+
+            # Ensure all face directions point towards the positive SDF to maintain consistent winding.
+            s_edges = s_n[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1)].reshape(-1, 2)
+            flip_mask = s_edges[:, 0] > 0
+            quad_vd_idx = torch.cat((quad_vd_idx[flip_mask][:, [0, 1, 3, 2]],
+                                     quad_vd_idx[~flip_mask][:, [2, 3, 1, 0]]))
+        if grad_func is not None:
+            # when grad_func is given, split quadrilaterals along the diagonals with more consistent gradients.
+            with torch.no_grad():
+                vd_gamma = torch.nn.functional.normalize(grad_func(vd), dim=-1)
+                quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3)
+                gamma_02 = (quad_gamma[:, 0] * quad_gamma[:, 2]).sum(-1, keepdims=True)
+                gamma_13 = (quad_gamma[:, 1] * quad_gamma[:, 3]).sum(-1, keepdims=True)
+        else:
+            quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4)
+            gamma_02 = torch.index_select(input=quad_gamma, index=torch.tensor(
+                0, device=self.device), dim=1) * torch.index_select(input=quad_gamma, index=torch.tensor(2, device=self.device), dim=1)
+            gamma_13 = torch.index_select(input=quad_gamma, index=torch.tensor(
+                1, device=self.device), dim=1) * torch.index_select(input=quad_gamma, index=torch.tensor(3, device=self.device), dim=1)
+        if not training:
+            mask = (gamma_02 > gamma_13).squeeze(1)
+            faces = torch.zeros((quad_gamma.shape[0], 6), dtype=torch.long, device=quad_vd_idx.device)
+            faces[mask] = quad_vd_idx[mask][:, self.quad_split_1]
+            faces[~mask] = quad_vd_idx[~mask][:, self.quad_split_2]
+            faces = faces.reshape(-1, 3)
+        else:
+            vd_quad = torch.index_select(input=vd, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3)
+            vd_02 = (torch.index_select(input=vd_quad, index=torch.tensor(0, device=self.device), dim=1) +
+                     torch.index_select(input=vd_quad, index=torch.tensor(2, device=self.device), dim=1)) / 2
+            vd_13 = (torch.index_select(input=vd_quad, index=torch.tensor(1, device=self.device), dim=1) +
+                     torch.index_select(input=vd_quad, index=torch.tensor(3, device=self.device), dim=1)) / 2
+            weight_sum = (gamma_02 + gamma_13) + 1e-8
+            vd_center = ((vd_02 * gamma_02.unsqueeze(-1) + vd_13 * gamma_13.unsqueeze(-1)) /
+                         weight_sum.unsqueeze(-1)).squeeze(1)
+            vd_center_idx = torch.arange(vd_center.shape[0], device=self.device) + vd.shape[0]
+            vd = torch.cat([vd, vd_center])
+            faces = quad_vd_idx[:, self.quad_split_train].reshape(-1, 4, 2)
+            faces = torch.cat([faces, vd_center_idx.reshape(-1, 1, 1).repeat(1, 4, 1)], -1).reshape(-1, 3)
+        return vd, faces, s_edges, edge_indices
+
+    def _tetrahedralize(
+            self, x_nx3, s_n, cube_fx8, vertices, faces, surf_edges, s_edges, vd_idx_map, case_ids, edge_indices,
+            surf_cubes, training):
+        """
+        Tetrahedralizes the interior volume to produce a tetrahedral mesh, as described in Section 4.5.
+        """
+        occ_n = s_n < 0
+        occ_fx8 = occ_n[cube_fx8.reshape(-1)].reshape(-1, 8)
+        occ_sum = torch.sum(occ_fx8, -1)
+
+        inside_verts = x_nx3[occ_n]
+        mapping_inside_verts = torch.ones((occ_n.shape[0]), dtype=torch.long, device=self.device) * -1
+        mapping_inside_verts[occ_n] = torch.arange(occ_n.sum(), device=self.device) + vertices.shape[0]
+        """ 
+        For each grid edge connecting two grid vertices with different
+        signs, we first form a four-sided pyramid by connecting one
+        of the grid vertices with four mesh vertices that correspond
+        to the grid edge and then subdivide the pyramid into two tetrahedra
+        """
+        inside_verts_idx = mapping_inside_verts[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1, 2)[
+            s_edges < 0]]
+        if not training:
+            inside_verts_idx = inside_verts_idx.unsqueeze(1).expand(-1, 2).reshape(-1)
+        else:
+            inside_verts_idx = inside_verts_idx.unsqueeze(1).expand(-1, 4).reshape(-1)
+
+        tets_surface = torch.cat([faces, inside_verts_idx.unsqueeze(-1)], -1)
+        """ 
+        For each grid edge connecting two grid vertices with the
+        same sign, the tetrahedron is formed by the two grid vertices
+        and two vertices in consecutive adjacent cells
+        """
+        inside_cubes = (occ_sum == 8)
+        inside_cubes_center = x_nx3[cube_fx8[inside_cubes].reshape(-1)].reshape(-1, 8, 3).mean(1)
+        inside_cubes_center_idx = torch.arange(
+            inside_cubes_center.shape[0], device=inside_cubes.device) + vertices.shape[0] + inside_verts.shape[0]
+
+        surface_n_inside_cubes = surf_cubes | inside_cubes
+        edge_center_vertex_idx = torch.ones(((surface_n_inside_cubes).sum(), 13),
+                                            dtype=torch.long, device=x_nx3.device) * -1
+        surf_cubes = surf_cubes[surface_n_inside_cubes]
+        inside_cubes = inside_cubes[surface_n_inside_cubes]
+        edge_center_vertex_idx[surf_cubes, :12] = vd_idx_map.reshape(-1, 12)
+        edge_center_vertex_idx[inside_cubes, 12] = inside_cubes_center_idx
+
+        all_edges = cube_fx8[surface_n_inside_cubes][:, self.cube_edges].reshape(-1, 2)
+        unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True)
+        unique_edges = unique_edges.long()
+        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 2
+        mask = mask_edges[_idx_map]
+        counts = counts[_idx_map]
+        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=self.device) * -1
+        mapping[mask_edges] = torch.arange(mask_edges.sum(), device=self.device)
+        idx_map = mapping[_idx_map]
+
+        group_mask = (counts == 4) & mask
+        group = idx_map.reshape(-1)[group_mask]
+        edge_indices, indices = torch.sort(group)
+        cube_idx = torch.arange((_idx_map.shape[0] // 12), dtype=torch.long,
+                                device=self.device).unsqueeze(1).expand(-1, 12).reshape(-1)[group_mask]
+        edge_idx = torch.arange((12), dtype=torch.long, device=self.device).unsqueeze(
+            0).expand(_idx_map.shape[0] // 12, -1).reshape(-1)[group_mask]
+        # Identify the face shared by the adjacent cells.
+        cube_idx_4 = cube_idx[indices].reshape(-1, 4)
+        edge_dir = self.edge_dir_table[edge_idx[indices]].reshape(-1, 4)[..., 0]
+        shared_faces_4x2 = self.dir_faces_table[edge_dir].reshape(-1)
+        cube_idx_4x2 = cube_idx_4[:, self.adj_pairs].reshape(-1)
+        # Identify an edge of the face with different signs and
+        # select the mesh vertex corresponding to the identified edge.
+        case_ids_expand = torch.ones((surface_n_inside_cubes).sum(), dtype=torch.long, device=x_nx3.device) * 255
+        case_ids_expand[surf_cubes] = case_ids
+        cases = case_ids_expand[cube_idx_4x2]
+        quad_edge = edge_center_vertex_idx[cube_idx_4x2, self.tet_table[cases, shared_faces_4x2]].reshape(-1, 2)
+        mask = (quad_edge == -1).sum(-1) == 0
+        inside_edge = mapping_inside_verts[unique_edges[mask_edges][edge_indices].reshape(-1)].reshape(-1, 2)
+        tets_inside = torch.cat([quad_edge, inside_edge], -1)[mask]
+
+        tets = torch.cat([tets_surface, tets_inside])
+        vertices = torch.cat([vertices, inside_verts, inside_cubes_center])
+        return vertices, tets
diff --git a/nsr/geometry/rep_3d/flexicubes_geometry.py b/nsr/geometry/rep_3d/flexicubes_geometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf050ee20361f78957839942f83fe77efde231b7
--- /dev/null
+++ b/nsr/geometry/rep_3d/flexicubes_geometry.py
@@ -0,0 +1,120 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+import numpy as np
+import os
+from . import Geometry
+from .flexicubes import FlexiCubes # replace later
+from .dmtet import sdf_reg_loss_batch
+import torch.nn.functional as F
+
+def get_center_boundary_index(grid_res, device):
+    v = torch.zeros((grid_res + 1, grid_res + 1, grid_res + 1), dtype=torch.bool, device=device)
+    v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = True
+    center_indices = torch.nonzero(v.reshape(-1))
+
+    v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = False
+    v[:2, ...] = True
+    v[-2:, ...] = True
+    v[:, :2, ...] = True
+    v[:, -2:, ...] = True
+    v[:, :, :2] = True
+    v[:, :, -2:] = True
+    boundary_indices = torch.nonzero(v.reshape(-1))
+    return center_indices, boundary_indices
+
+###############################################################################
+#  Geometry interface
+###############################################################################
+class FlexiCubesGeometry(Geometry):
+    def __init__(
+            self, grid_res=64, scale=2.0, device='cuda', renderer=None,
+            render_type='neural_render', args=None):
+        super(FlexiCubesGeometry, self).__init__()
+        self.grid_res = grid_res
+        self.device = device
+        self.args = args
+        self.fc = FlexiCubes(device, weight_scale=0.5)
+        self.verts, self.indices = self.fc.construct_voxel_grid(grid_res)
+        if isinstance(scale, list):
+            self.verts[:, 0] = self.verts[:, 0] * scale[0]
+            self.verts[:, 1] = self.verts[:, 1] * scale[1]
+            self.verts[:, 2] = self.verts[:, 2] * scale[1]
+        else:
+            self.verts = self.verts * scale
+            
+        all_edges = self.indices[:, self.fc.cube_edges].reshape(-1, 2)
+        self.all_edges = torch.unique(all_edges, dim=0)
+
+        # Parameters used for fix boundary sdf
+        self.center_indices, self.boundary_indices = get_center_boundary_index(self.grid_res, device)
+        self.renderer = renderer
+        self.render_type = render_type
+
+    def getAABB(self):
+        return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values
+
+    def get_mesh(self, v_deformed_nx3, sdf_n, weight_n=None, with_uv=False, indices=None, is_training=False):
+        if indices is None:
+            indices = self.indices
+
+        verts, faces, v_reg_loss = self.fc(v_deformed_nx3, sdf_n, indices, self.grid_res,
+                                            beta_fx12=weight_n[:, :12], alpha_fx8=weight_n[:, 12:20],
+                                            gamma_f=weight_n[:, 20], training=is_training
+                                            )
+        return verts, faces, v_reg_loss
+
+
+    def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False):
+        return_value = dict()
+        if self.render_type == 'neural_render':
+            tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal = self.renderer.render_mesh(
+                mesh_v_nx3.unsqueeze(dim=0),
+                mesh_f_fx3.int(),
+                camera_mv_bx4x4,
+                mesh_v_nx3.unsqueeze(dim=0),
+                resolution=resolution,
+                device=self.device,
+                hierarchical_mask=hierarchical_mask
+            )
+
+            return_value['tex_pos'] = tex_pos
+            return_value['mask'] = mask
+            return_value['hard_mask'] = hard_mask
+            return_value['rast'] = rast
+            return_value['v_pos_clip'] = v_pos_clip
+            return_value['mask_pyramid'] = mask_pyramid
+            return_value['depth'] = depth
+            return_value['normal'] = normal
+        else:
+            raise NotImplementedError
+
+        return return_value
+
+    def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256):
+        # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1
+        v_list = []
+        f_list = []
+        n_batch = v_deformed_bxnx3.shape[0]
+        all_render_output = []
+        for i_batch in range(n_batch):
+            verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch])
+            v_list.append(verts_nx3)
+            f_list.append(faces_fx3)
+            render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution)
+            all_render_output.append(render_output)
+
+        # Concatenate all render output
+        return_keys = all_render_output[0].keys()
+        return_value = dict()
+        for k in return_keys:
+            value = [v[k] for v in all_render_output]
+            return_value[k] = value
+            # We can do concatenation outside of the render
+        return return_value
diff --git a/nsr/geometry/rep_3d/tables.py b/nsr/geometry/rep_3d/tables.py
new file mode 100644
index 0000000000000000000000000000000000000000..5873e7727b5595a1e4fbc3bd10ae5be8f3d06cca
--- /dev/null
+++ b/nsr/geometry/rep_3d/tables.py
@@ -0,0 +1,791 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+dmc_table = [
+[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 5, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 4, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 8, 11, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 5, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 4, 5, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 5, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 5, 7, 8, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 7, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 9, 10, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 4, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 4, 5, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 4, 5, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 5, 7, 9, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 5, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 5, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
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+[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 5, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 7, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]]
+]
+num_vd_table = [0, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 2, 2,
+2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2,
+1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 3, 2, 2, 1, 1, 1, 1,
+1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 3, 2, 2, 2, 2, 2, 1, 3, 4, 2,
+2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2,
+3, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 3, 2, 4, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1,
+2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,
+1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,
+1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
+1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
+check_table = [
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 194],
+[1, -1, 0, 0, 193],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 164],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 161],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 152],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 145],
+[1, 0, 0, 1, 144],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 137],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 133],
+[1, 0, 1, 0, 132],
+[1, 1, 0, 0, 131],
+[1, 1, 0, 0, 130],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 100],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 98],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 96],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 88],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 82],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 74],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 72],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 70],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 67],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 65],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 56],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 52],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 44],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 40],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 38],
+[1, 0, -1, 0, 37],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 33],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 28],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 26],
+[1, 0, 0, -1, 25],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 20],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 18],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 9],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 6],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0]
+]
+tet_table = [
+[-1, -1, -1, -1, -1, -1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[4, 4, 4, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, -1],
+[1, 1, 1, 1, 1, 1],
+[4, 4, 4, 4, 4, 4],
+[0, 4, 0, 4, 4, -1],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[5, 5, 5, 5, 5, 5],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, -1, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, -1, 2, 4, 4, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 4, 4, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 4, 2, 4, 4, 2],
+[0, 4, 0, 4, 4, 0],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 5, 2, 5, 5, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[0, 1, 1, -1, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[4, 1, 1, 4, 4, 1],
+[0, 1, 1, 0, 0, 1],
+[4, 0, 0, 4, 4, 0],
+[2, 2, 2, 2, 2, 2],
+[-1, 1, 1, 4, 4, 1],
+[0, 1, 1, 4, 4, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[5, 1, 1, 5, 5, 1],
+[0, 1, 1, 0, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[8, 8, 8, 8, 8, 8],
+[1, 1, 1, 4, 4, 1],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 4, 4, 1],
+[0, 4, 0, 4, 4, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 5, 5, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[5, 5, 5, 5, 5, 5],
+[6, 6, 6, 6, 6, 6],
+[6, -1, 0, 6, 0, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 1, 1, 6, 1, 6],
+[4, 4, 4, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[6, 4, -1, 6, 4, 6],
+[6, 4, 0, 6, 4, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 1, 1, 6, 1, 6],
+[5, 5, 5, 5, 5, 5],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 2, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 4, 2, 2, 4, 2],
+[0, 4, 0, 4, 4, 0],
+[2, 0, 2, 2, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[6, 1, 1, 6, -1, 6],
+[6, 1, 1, 6, 0, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 2, 2, 6, 2, 6],
+[4, 1, 1, 4, 4, 1],
+[0, 1, 1, 0, 0, 1],
+[4, 0, 0, 4, 4, 4],
+[2, 2, 2, 2, 2, 2],
+[6, 1, 1, 6, 4, 6],
+[6, 1, 1, 6, 4, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 2, 2, 6, 2, 6],
+[5, 1, 1, 5, 5, 1],
+[0, 1, 1, 0, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[6, 6, 6, 6, 6, 6],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 4, 1],
+[0, 4, 0, 4, 4, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 5, 0, 5, 0, 5],
+[5, 5, 5, 5, 5, 5],
+[5, 5, 5, 5, 5, 5],
+[0, 5, 0, 5, 0, 5],
+[-1, 5, 0, 5, 0, 5],
+[1, 5, 1, 5, 1, 5],
+[4, 5, -1, 5, 4, 5],
+[0, 5, 0, 5, 0, 5],
+[4, 5, 0, 5, 4, 5],
+[1, 5, 1, 5, 1, 5],
+[4, 4, 4, 4, 4, 4],
+[0, 4, 0, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[6, 6, 6, 6, 6, 6],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 5, 2, 5, -1, 5],
+[0, 5, 0, 5, 0, 5],
+[2, 5, 2, 5, 0, 5],
+[1, 5, 1, 5, 1, 5],
+[2, 5, 2, 5, 4, 5],
+[0, 5, 0, 5, 0, 5],
+[2, 5, 2, 5, 4, 5],
+[1, 5, 1, 5, 1, 5],
+[2, 4, 2, 4, 4, 2],
+[0, 4, 0, 4, 4, 4],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 6, 2, 6, 6, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[0, 1, 1, 1, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[4, 1, 1, 1, 4, 1],
+[0, 1, 1, 1, 0, 1],
+[4, 0, 0, 4, 4, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[5, 5, 5, 5, 5, 5],
+[1, 1, 1, 1, 4, 1],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[6, 0, 0, 6, 0, 6],
+[0, 0, 0, 0, 0, 0],
+[6, 6, 6, 6, 6, 6],
+[5, 5, 5, 5, 5, 5],
+[5, 5, 0, 5, 0, 5],
+[5, 5, 0, 5, 0, 5],
+[5, 5, 1, 5, 1, 5],
+[4, 4, 4, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 0, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[4, 4, 4, 4, 4, 4],
+[4, 4, 0, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[8, 8, 8, 8, 8, 8],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 1, 1, 4, 4, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[2, 4, 2, 4, 4, 2],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[5, 5, 5, 5, 5, 5],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[12, 12, 12, 12, 12, 12]
+]
diff --git a/nsr/gs.py b/nsr/gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ead51536ccb55eebd63d27aadc991edb28b6d218
--- /dev/null
+++ b/nsr/gs.py
@@ -0,0 +1,206 @@
+import numpy as np
+
+from pdb import set_trace as st
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diff_gaussian_rasterization import (
+    GaussianRasterizationSettings,
+    GaussianRasterizer,
+)
+
+import kiui
+
+class GaussianRenderer:
+    def __init__(self, output_size, out_chans, rendering_kwargs, **kwargs):
+        
+        # self.opt = opt
+        self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device="cuda")
+        # self.bg_color = torch.tensor([0,0,1], dtype=torch.float32, device="cuda")
+
+        self.output_size = output_size
+        self.out_chans = out_chans
+        self.rendering_kwargs = rendering_kwargs
+        
+        # intrinsics
+        # self.tan_half_fov = np.tan(0.5 * np.deg2rad(self.opt.fovy))
+        # self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32)
+        # self.proj_matrix[0, 0] = 1 / self.tan_half_fov
+        # self.proj_matrix[1, 1] = 1 / self.tan_half_fov
+        # self.proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear)
+        # self.proj_matrix[3, 2] = - (opt.zfar * opt.znear) / (opt.zfar - opt.znear)
+        # self.proj_matrix[2, 3] = 1
+        
+    def render(self, gaussians, cam_view, cam_view_proj, cam_pos, tanfov,  bg_color=None, scale_modifier=1):
+        # gaussians: [B, N, 14]
+        # cam_view, cam_view_proj: [B, V, 4, 4]
+        # cam_pos: [B, V, 3]
+
+        device = gaussians.device
+        B, V = cam_view.shape[:2]
+
+        # loop of loop...
+        images = []
+        alphas = []
+        depths = []
+
+        if bg_color is None:
+            bg_color = self.bg_color
+
+        for b in range(B):
+
+            # pos, opacity, scale, rotation, shs
+            means3D = gaussians[b, :, 0:3].contiguous().float()
+            opacity = gaussians[b, :, 3:4].contiguous().float()
+            scales = gaussians[b, :, 4:7].contiguous().float()
+            rotations = gaussians[b, :, 7:11].contiguous().float()
+            rgbs = gaussians[b, :, 11:].contiguous().float() # [N, 3]
+
+            for v in range(V):
+                
+                # render novel views
+                view_matrix = cam_view[b, v].float()
+                view_proj_matrix = cam_view_proj[b, v].float()
+                campos = cam_pos[b, v].float()
+
+                raster_settings = GaussianRasterizationSettings(
+                    image_height=self.output_size,
+                    image_width=self.output_size,
+                    tanfovx=tanfov,
+                    tanfovy=tanfov,
+                    bg=bg_color,
+                    scale_modifier=scale_modifier,
+                    viewmatrix=view_matrix,
+                    projmatrix=view_proj_matrix,
+                    sh_degree=0,
+                    campos=campos,
+                    prefiltered=False,
+                    debug=False,
+                )
+
+                rasterizer = GaussianRasterizer(raster_settings=raster_settings)
+
+                # Rasterize visible Gaussians to image, obtain their radii (on screen).
+                rendered_image, radii, rendered_depth, rendered_alpha = rasterizer(
+                    means3D=means3D,
+                    means2D=torch.zeros_like(means3D, dtype=torch.float32, device=device),
+                    shs=None,
+                    colors_precomp=rgbs,
+                    opacities=opacity,
+                    scales=scales,
+                    rotations=rotations,
+                    cov3D_precomp=None,
+                )
+
+                rendered_image = rendered_image.clamp(0, 1)
+
+                images.append(rendered_image)
+                alphas.append(rendered_alpha)
+                depths.append(rendered_depth)
+
+        images = torch.stack(images, dim=0).view(B, V, 3, self.output_size, self.output_size)
+        alphas = torch.stack(alphas, dim=0).view(B, V, 1, self.output_size, self.output_size)
+        depths = torch.stack(depths, dim=0).view(B, V, 1, self.output_size, self.output_size)
+
+        # images = torch.stack(images, dim=0).view(B*V, 3, self.output_size, self.output_size)
+        # alphas = torch.stack(alphas, dim=0).view(B*V, 1, self.output_size, self.output_size)
+        # depths = torch.stack(depths, dim=0).view(B*V, 1, self.output_size, self.output_size)
+
+        return {
+            "image": images, # [B, V, 3, H, W]
+            "alpha": alphas, # [B, V, 1, H, W]
+            "depth": depths,
+        }
+
+
+    def save_ply(self, gaussians, path, compatible=True):
+        # gaussians: [B, N, 14]
+        # compatible: save pre-activated gaussians as in the original paper
+
+        assert gaussians.shape[0] == 1, 'only support batch size 1'
+
+        from plyfile import PlyData, PlyElement
+     
+        means3D = gaussians[0, :, 0:3].contiguous().float()
+        opacity = gaussians[0, :, 3:4].contiguous().float()
+        scales = gaussians[0, :, 4:7].contiguous().float()
+        rotations = gaussians[0, :, 7:11].contiguous().float()
+        shs = gaussians[0, :, 11:].unsqueeze(1).contiguous().float() # [N, 1, 3]
+
+        # prune by opacity
+        mask = opacity.squeeze(-1) >= 0.005
+        means3D = means3D[mask]
+        opacity = opacity[mask]
+        scales = scales[mask]
+        rotations = rotations[mask]
+        shs = shs[mask]
+
+        # invert activation to make it compatible with the original ply format
+        if compatible:
+            opacity = kiui.op.inverse_sigmoid(opacity)
+            scales = torch.log(scales + 1e-8)
+            shs = (shs - 0.5) / 0.28209479177387814
+
+        xyzs = means3D.detach().cpu().numpy()
+        f_dc = shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+        opacities = opacity.detach().cpu().numpy()
+        scales = scales.detach().cpu().numpy()
+        rotations = rotations.detach().cpu().numpy()
+
+        l = ['x', 'y', 'z']
+        # All channels except the 3 DC
+        for i in range(f_dc.shape[1]):
+            l.append('f_dc_{}'.format(i))
+        l.append('opacity')
+        for i in range(scales.shape[1]):
+            l.append('scale_{}'.format(i))
+        for i in range(rotations.shape[1]):
+            l.append('rot_{}'.format(i))
+
+        dtype_full = [(attribute, 'f4') for attribute in l]
+
+        elements = np.empty(xyzs.shape[0], dtype=dtype_full)
+        attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1)
+        elements[:] = list(map(tuple, attributes))
+        el = PlyElement.describe(elements, 'vertex')
+
+        PlyData([el]).write(path)
+    
+    def load_ply(self, path, compatible=True):
+
+        from plyfile import PlyData, PlyElement
+
+        plydata = PlyData.read(path)
+
+        xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
+                        np.asarray(plydata.elements[0]["y"]),
+                        np.asarray(plydata.elements[0]["z"])),  axis=1)
+        print("Number of points at loading : ", xyz.shape[0])
+
+        opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
+
+        shs = np.zeros((xyz.shape[0], 3))
+        shs[:, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
+        shs[:, 1] = np.asarray(plydata.elements[0]["f_dc_1"])
+        shs[:, 2] = np.asarray(plydata.elements[0]["f_dc_2"])
+
+        scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
+        scales = np.zeros((xyz.shape[0], len(scale_names)))
+        for idx, attr_name in enumerate(scale_names):
+            scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
+
+        rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot_")]
+        rots = np.zeros((xyz.shape[0], len(rot_names)))
+        for idx, attr_name in enumerate(rot_names):
+            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
+          
+        gaussians = np.concatenate([xyz, opacities, scales, rots, shs], axis=1)
+        gaussians = torch.from_numpy(gaussians).float() # cpu
+
+        if compatible:
+            gaussians[..., 3:4] = torch.sigmoid(gaussians[..., 3:4])
+            gaussians[..., 4:7] = torch.exp(gaussians[..., 4:7])
+            gaussians[..., 11:] = 0.28209479177387814 * gaussians[..., 11:] + 0.5
+
+        return gaussians
\ No newline at end of file
diff --git a/nsr/gs_surfel.py b/nsr/gs_surfel.py
new file mode 100644
index 0000000000000000000000000000000000000000..2591a99c15b31d5e4201ca5991beaaeadaf4ddcd
--- /dev/null
+++ b/nsr/gs_surfel.py
@@ -0,0 +1,364 @@
+# modified from : 2dgs/gaussian_renderer/__init__.py
+import numpy as np
+
+from pdb import set_trace as st
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# from diff_gaussian_rasterization import (
+#     GaussianRasterizationSettings,
+#     GaussianRasterizer,
+# )
+
+from torch.profiler import profile, record_function, ProfilerActivity
+from diff_surfel_rasterization import GaussianRasterizationSettings, GaussianRasterizer
+from utils.point_utils import depth_to_normal, depth_to_normal_2
+
+import kiui
+
+
+class GaussianRenderer2DGS:
+    def __init__(self, output_size, out_chans, rendering_kwargs, **kwargs):
+        
+        # self.opt = opt
+        self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device="cuda")
+        # self.bg_color = torch.tensor([0,0,1], dtype=torch.float32, device="cuda")
+
+        self.output_size = output_size
+        self.out_chans = out_chans
+        self.rendering_kwargs = rendering_kwargs
+ 
+        # intrinsics
+        # self.tan_half_fov = np.tan(0.5 * np.deg2rad(self.opt.fovy))
+        # self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32)
+        # self.proj_matrix[0, 0] = 1 / self.tan_half_fov
+        # self.proj_matrix[1, 1] = 1 / self.tan_half_fov
+        # self.proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear)
+        # self.proj_matrix[3, 2] = - (opt.zfar * opt.znear) / (opt.zfar - opt.znear)
+        # self.proj_matrix[2, 3] = 1
+        
+    def render(self, gaussians, cam_view, cam_view_proj, cam_pos, tanfov,  bg_color=None, scale_modifier=1, output_size=None):
+        # gaussians: [B, N, 14-1]
+        # cam_view, cam_view_proj: [B, V, 4, 4]
+        # cam_pos: [B, V, 3]
+
+        if output_size is None:
+            output_size = self.output_size
+
+        device = gaussians.device
+        B, V = cam_view.shape[:2]
+        assert gaussians.shape[2] == 13 # scale with 2dof
+        gaussians = gaussians.contiguous().float() # gs rendering in fp32
+
+        # loop of loop...
+        images = []
+        alphas = []
+        depths = []
+        # surf_normals = []
+        rend_normals = []
+        dists = []
+
+        if bg_color is None:
+            bg_color = self.bg_color
+
+        for b in range(B):
+
+            # pos, opacity, scale, rotation, shs
+            means3D = gaussians[b, :, 0:3].contiguous().float()
+            opacity = gaussians[b, :, 3:4].contiguous().float()
+            scales = gaussians[b, :, 4:6].contiguous().float()
+            rotations = gaussians[b, :, 6:10].contiguous().float()
+            rgbs = gaussians[b, :, 10:13].contiguous().float() # [N, 3]
+
+            for v in range(V):
+                
+                # render novel views
+                view_matrix = cam_view[b, v].float() # world_view_transform
+                view_proj_matrix = cam_view_proj[b, v].float()
+                campos = cam_pos[b, v].float()
+
+                # with profile(activities=[ProfilerActivity.CUDA, ProfilerActivity.CPU,], record_shapes=True) as prof:
+
+                    # with record_function("rendering"):
+
+                raster_settings = GaussianRasterizationSettings(
+                    image_height=output_size,
+                    image_width=output_size,
+                    tanfovx=tanfov,
+                    tanfovy=tanfov,
+                    bg=bg_color,
+                    scale_modifier=scale_modifier,
+                    viewmatrix=view_matrix,
+                    projmatrix=view_proj_matrix,
+                    sh_degree=0,
+                    campos=campos,
+                    prefiltered=False,
+                    debug=False,
+                )
+
+                rasterizer = GaussianRasterizer(raster_settings=raster_settings)
+
+                # Rasterize visible Gaussians to image, obtain their radii (on screen).
+                # rendered_image, radii, rendered_depth, rendered_alpha = rasterizer(
+                rendered_image, radii, allmap = rasterizer(
+                    means3D=means3D,
+                    means2D=torch.zeros_like(means3D, dtype=torch.float32, device=device),
+                    shs=None,
+                    colors_precomp=rgbs,
+                    opacities=opacity,
+                    scales=scales,
+                    rotations=rotations,
+                    cov3D_precomp=None,
+                    # cov3D_precomp = cov3D_precomp
+                )
+
+                # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+
+                # with profile(activities=[ProfilerActivity.CUDA, ProfilerActivity.CPU,], record_shapes=True) as prof:
+
+                # ! additional regularizations
+                render_alpha = allmap[1:2]
+
+                # get normal map
+                # transform normal from view space to world space
+                # with record_function("render_normal"):
+                render_normal = allmap[2:5]
+                # render_normal = (render_normal.permute(1,2,0) @ (viewpoint_camera.world_view_transform[:3,:3].T)).permute(2,0,1)
+                render_normal = (render_normal.permute(1,2,0) @ (view_matrix[:3,:3].T)).permute(2,0,1)
+                
+                # with record_function("render_depth"):
+
+                # get median depth map
+                render_depth_median = allmap[5:6]
+                render_depth_median = torch.nan_to_num(render_depth_median, 0, 0)
+
+                # get expected depth map
+                render_depth_expected = allmap[0:1]
+                render_depth_expected = (render_depth_expected / render_alpha)
+                render_depth_expected = torch.nan_to_num(render_depth_expected, 0, 0)
+                
+                # get depth distortion map
+                render_dist = allmap[6:7]
+
+                # psedo surface attributes
+                # surf depth is either median or expected by setting depth_ratio to 1 or 0
+                # for bounded scene, use median depth, i.e., depth_ratio = 1; 
+                # for unbounded scene, use expected depth, i.e., depth_ration = 0, to reduce disk anliasing.
+
+                # ! hard coded depth_ratio = 1 for objaverse
+                surf_depth = render_depth_median
+                # with record_function("surf_normal"):
+                #     depth_ratio = 1
+                #     # surf_depth = render_depth_expected * (1-depth_ratio) + (depth_ratio) * render_depth_median
+                    
+                #     # assume the depth points form the 'surface' and generate psudo surface normal for regularizations.
+                #     # surf_normal = depth_to_normal(viewpoint_camera, surf_depth)
+                #     surf_normal = depth_to_normal_2(world_view_transform=view_matrix, tanfov=tanfov, W=self.output_size, H=self.output_size, depth=surf_depth)
+                #     surf_normal = surf_normal.permute(2,0,1)
+                #     # remember to multiply with accum_alpha since render_normal is unnormalized.
+                #     surf_normal = surf_normal * (render_alpha).detach()
+
+                # ! images
+                rendered_image = rendered_image.clamp(0, 1)
+
+                # images.append(rendered_image)
+                # alphas.append(rendered_alpha)
+                # depths.append(rendered_depth)
+
+                images.append(rendered_image)
+                alphas.append(render_alpha)
+                depths.append(surf_depth)
+                # surf_normals.append(surf_normal)
+                rend_normals.append(render_normal)
+                dists.append(render_dist)
+
+                # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+                # st()
+                pass
+
+        images = torch.stack(images, dim=0).view(B, V, 3, output_size, output_size)
+        alphas = torch.stack(alphas, dim=0).view(B, V, 1, output_size, output_size)
+        depths = torch.stack(depths, dim=0).view(B, V, 1, output_size, output_size)
+        
+        # approximated surface normal? No, direct depth supervision here.
+        # surf_normals = torch.stack(surf_normals, dim=0).view(B, V, 3, self.output_size, self.output_size)
+
+        # disk normal
+        rend_normals = torch.stack(rend_normals, dim=0).view(B, V, 3, output_size, output_size)
+        dists = torch.stack(dists, dim=0).view(B, V, 1, output_size, output_size)
+
+        # images = torch.stack(images, dim=0).view(B*V, 3, self.output_size, self.output_size)
+        # alphas = torch.stack(alphas, dim=0).view(B*V, 1, self.output_size, self.output_size)
+        # depths = torch.stack(depths, dim=0).view(B*V, 1, self.output_size, self.output_size)
+
+        return {
+            "image": images, # [B, V, 3, H, W]
+            "alpha": alphas, # [B, V, 1, H, W]
+            "depth": depths,
+            # "surf_normal": surf_normals,
+            "rend_normal": rend_normals,
+            "dist": dists
+        }
+
+    # TODO, save/load 2dgs Gaussians
+
+    def save_2dgs_ply(self, path, gaussians, compatible=True):
+        # gaussians: [B, N, 13]
+
+        mkdir_p(os.path.dirname(path))
+        assert gaussians.shape[0] == 1, 'only support batch size 1'
+
+        from plyfile import PlyData, PlyElement
+     
+        means3D = gaussians[0, :, 0:3].contiguous().float()
+        opacity = gaussians[0, :, 3:4].contiguous().float()
+        scales = gaussians[0, :, 4:6].contiguous().float()
+        rotations = gaussians[0, :, 6:10].contiguous().float()
+        shs = gaussians[0, :, 10:].unsqueeze(1).contiguous().float() # [N, 1, 3]
+
+        # invert activation to make it compatible with the original ply format
+        if compatible:
+            opacity = kiui.op.inverse_sigmoid(opacity)
+            scales = torch.log(scales + 1e-8)
+            shs = (shs - 0.5) / 0.28209479177387814
+
+        xyzs = means3D.detach().cpu().numpy()
+        f_dc = shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+        opacities = opacity.detach().cpu().numpy()
+        scales = scales.detach().cpu().numpy()
+        rotations = rotations.detach().cpu().numpy()
+
+        # xyz = self._xyz.detach().cpu().numpy()
+        # normals = np.zeros_like(xyz)
+        # f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+        # f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+        # opacities = self._opacity.detach().cpu().numpy()
+        # scale = self._scaling.detach().cpu().numpy()
+        # rotation = self._rotation.detach().cpu().numpy()
+
+        # dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
+
+        l = ['x', 'y', 'z']
+        # All channels except the 3 DC
+        for i in range(f_dc.shape[1]):
+            l.append('f_dc_{}'.format(i))
+
+        # save normals also
+        for i in range(f_dc.shape[1]):
+            l.append('f_dc_{}'.format(i))
+
+        l.append('opacity')
+        for i in range(scales.shape[1]):
+            l.append('scale_{}'.format(i))
+        for i in range(rotations.shape[1]):
+            l.append('rot_{}'.format(i))
+
+        dtype_full = [(attribute, 'f4') for attribute in l]
+
+        elements = np.empty(xyz.shape[0], dtype=dtype_full)
+        # attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1)
+        # attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
+        attributes = np.concatenate((xyz, normals, f_dc, opacities, scale, rotation), axis=1)
+        elements[:] = list(map(tuple, attributes))
+        el = PlyElement.describe(elements, 'vertex')
+        PlyData([el]).write(path)
+
+
+
+    # def save_ply(self, gaussians, path, compatible=True):
+    #     # gaussians: [B, N, 14]
+    #     # compatible: save pre-activated gaussians as in the original paper
+
+    #     assert gaussians.shape[0] == 1, 'only support batch size 1'
+
+    #     from plyfile import PlyData, PlyElement
+     
+    #     means3D = gaussians[0, :, 0:3].contiguous().float()
+    #     opacity = gaussians[0, :, 3:4].contiguous().float()
+    #     scales = gaussians[0, :, 4:7].contiguous().float()
+    #     rotations = gaussians[0, :, 7:11].contiguous().float()
+    #     shs = gaussians[0, :, 11:].unsqueeze(1).contiguous().float() # [N, 1, 3]
+
+    #     # prune by opacity
+    #     mask = opacity.squeeze(-1) >= 0.005
+    #     means3D = means3D[mask]
+    #     opacity = opacity[mask]
+    #     scales = scales[mask]
+    #     rotations = rotations[mask]
+    #     shs = shs[mask]
+
+    #     # invert activation to make it compatible with the original ply format
+    #     if compatible:
+    #         opacity = kiui.op.inverse_sigmoid(opacity)
+    #         scales = torch.log(scales + 1e-8)
+    #         shs = (shs - 0.5) / 0.28209479177387814
+
+    #     xyzs = means3D.detach().cpu().numpy()
+    #     f_dc = shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+    #     opacities = opacity.detach().cpu().numpy()
+    #     scales = scales.detach().cpu().numpy()
+    #     rotations = rotations.detach().cpu().numpy()
+
+    #     l = ['x', 'y', 'z']
+    #     # All channels except the 3 DC
+    #     for i in range(f_dc.shape[1]):
+    #         l.append('f_dc_{}'.format(i))
+    #     l.append('opacity')
+    #     for i in range(scales.shape[1]):
+    #         l.append('scale_{}'.format(i))
+    #     for i in range(rotations.shape[1]):
+    #         l.append('rot_{}'.format(i))
+
+    #     dtype_full = [(attribute, 'f4') for attribute in l]
+
+    #     elements = np.empty(xyzs.shape[0], dtype=dtype_full)
+    #     attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1)
+    #     elements[:] = list(map(tuple, attributes))
+    #     el = PlyElement.describe(elements, 'vertex')
+
+    #     PlyData([el]).write(path)
+    
+    def load_2dgs_ply(self, path, compatible=True):
+
+        from plyfile import PlyData, PlyElement
+
+        plydata = PlyData.read(path)
+
+        xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
+                        np.asarray(plydata.elements[0]["y"]),
+                        np.asarray(plydata.elements[0]["z"])),  axis=1)
+        print("Number of points at loading : ", xyz.shape[0])
+
+        opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
+
+        shs = np.zeros((xyz.shape[0], 3))
+        shs[:, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
+        shs[:, 1] = np.asarray(plydata.elements[0]["f_dc_1"])
+        shs[:, 2] = np.asarray(plydata.elements[0]["f_dc_2"])
+
+        scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
+        scales = np.zeros((xyz.shape[0], len(scale_names)))
+        for idx, attr_name in enumerate(scale_names):
+            scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
+
+        rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot_")]
+        rots = np.zeros((xyz.shape[0], len(rot_names)))
+        for idx, attr_name in enumerate(rot_names):
+            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
+
+
+        normal_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot_")]
+        rots = np.zeros((xyz.shape[0], len(rot_names)))
+        for idx, attr_name in enumerate(rot_names):
+            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
+          
+        gaussians = np.concatenate([xyz, opacities, scales, rots, shs], axis=1)
+        gaussians = torch.from_numpy(gaussians).float() # cpu
+
+        if compatible:
+            gaussians[..., 3:4] = torch.sigmoid(gaussians[..., 3:4])
+            gaussians[..., 4:7] = torch.exp(gaussians[..., 4:7])
+            gaussians[..., 11:] = 0.28209479177387814 * gaussians[..., 11:] + 0.5
+
+        return gaussians
\ No newline at end of file
diff --git a/nsr/losses/__init__.py b/nsr/losses/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..1447a4305c77bfbe3d50a0d27bb9d2d893ec7621
--- /dev/null
+++ b/nsr/losses/__init__.py
@@ -0,0 +1,10 @@
+# 2d reconstruction losses
+from .id_loss import IDLoss
+# from .lms import HeatmapLoss # for faces
+# from .lpips_deprecated.lpips import LPIPS
+
+# manage import
+__all__ = [
+    # 'LPIPS',
+    'IDLoss',
+]
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diff --git a/nsr/losses/builder.py b/nsr/losses/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b386e6f237857119f7149ea36357e577a19ab114
--- /dev/null
+++ b/nsr/losses/builder.py
@@ -0,0 +1,1096 @@
+EPS = 1e-7
+
+import kornia
+from typing import Dict, Iterator, List, Optional, Tuple, Union
+import torchvision
+from guided_diffusion import dist_util, logger
+from pdb import set_trace as st
+from torch.nn import functional as F
+import numpy as np
+import torch
+import torch.nn as nn
+import lpips
+
+from . import *
+
+from .sdfstudio_losses import ScaleAndShiftInvariantLoss
+from ldm.util import default, instantiate_from_config
+from .vqperceptual import hinge_d_loss, vanilla_d_loss
+from torch.autograd import Variable
+
+from math import exp
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    C1 = 0.01 ** 2
+    C2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1).mean(1).mean(1)
+
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm") != -1:
+        nn.init.normal_(m.weight.data, 1.0, 0.02)
+        nn.init.constant_(m.bias.data, 0)
+
+
+# Main loss function used for ZoeDepth. Copy/paste from AdaBins repo (https://github.com/shariqfarooq123/AdaBins/blob/0952d91e9e762be310bb4cd055cbfe2448c0ce20/loss.py#L7)
+def extract_key(prediction, key):
+    if isinstance(prediction, dict):
+        return prediction[key]
+    return prediction
+
+
+class SILogLoss(nn.Module):
+    """SILog loss (pixel-wise)"""
+
+    def __init__(self, beta=0.15):
+        super(SILogLoss, self).__init__()
+        self.name = 'SILog'
+        self.beta = beta
+
+    def forward(self,
+                input,
+                target,
+                mask=None,
+                interpolate=True,
+                return_interpolated=False):
+        # input = extract_key(input, KEY_OUTPUT)
+        if input.shape[-1] != target.shape[-1] and interpolate:
+            input = nn.functional.interpolate(input,
+                                              target.shape[-2:],
+                                              mode='bilinear',
+                                              align_corners=True)
+            intr_input = input
+        else:
+            intr_input = input
+
+        if target.ndim == 3:
+            target = target.unsqueeze(1)
+
+        if mask is not None:
+            if mask.ndim == 3:
+                mask = mask.unsqueeze(1)
+
+            input = input[mask]
+            target = target[mask]
+
+        # with torch.amp.autocast(enabled=False):  # amp causes NaNs in this loss function
+
+        alpha = 1e-7
+        g = torch.log(input + alpha) - torch.log(target + alpha)
+
+        # n, c, h, w = g.shape
+        # norm = 1/(h*w)
+        # Dg = norm * torch.sum(g**2) - (0.85/(norm**2)) * (torch.sum(g))**2
+
+        Dg = torch.var(g) + self.beta * torch.pow(torch.mean(g), 2)
+
+        loss = 10 * torch.sqrt(Dg)
+
+        if torch.isnan(loss):
+            print("Nan SILog loss")
+            print("input:", input.shape)
+            print("target:", target.shape)
+            print("G", torch.sum(torch.isnan(g)))
+            print("Input min max", torch.min(input), torch.max(input))
+            print("Target min max", torch.min(target), torch.max(target))
+            print("Dg", torch.isnan(Dg))
+            print("loss", torch.isnan(loss))
+
+        if not return_interpolated:
+            return loss
+
+        return loss, intr_input
+
+
+def get_outnorm(x: torch.Tensor, out_norm: str = '') -> torch.Tensor:
+    """ Common function to get a loss normalization value. Can
+        normalize by either the batch size ('b'), the number of
+        channels ('c'), the image size ('i') or combinations
+        ('bi', 'bci', etc)
+    """
+    # b, c, h, w = x.size()
+    img_shape = x.shape
+
+    if not out_norm:
+        return 1
+
+    norm = 1
+    if 'b' in out_norm:
+        # normalize by batch size
+        # norm /= b
+        norm /= img_shape[0]
+    if 'c' in out_norm:
+        # normalize by the number of channels
+        # norm /= c
+        norm /= img_shape[-3]
+    if 'i' in out_norm:
+        # normalize by image/map size
+        # norm /= h*w
+        norm /= img_shape[-1] * img_shape[-2]
+
+    return norm
+
+
+class CharbonnierLoss(torch.nn.Module):
+    """Charbonnier Loss (L1)"""
+
+    def __init__(self, eps=1e-6, out_norm: str = 'bci'):
+        super(CharbonnierLoss, self).__init__()
+        self.eps = eps
+        self.out_norm = out_norm
+
+    def forward(self, x, y):
+        norm = get_outnorm(x, self.out_norm)
+        loss = torch.sum(torch.sqrt((x - y).pow(2) + self.eps**2))
+        return loss * norm
+
+
+def feature_vae_loss(feature):
+    # kld = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(), dim = 1), dim = 0)
+
+    # feature dim: B C H W
+    mu = feature.mean(1)
+    var = feature.var(1)
+    log_var = torch.log(var)
+    kld = torch.mean(-0.5 * torch.sum(1 + log_var - mu**2 - var, dim=1), dim=0)
+    return kld
+
+
+def kl_coeff(step, total_step, constant_step, min_kl_coeff, max_kl_coeff):
+    # return max(min(max_kl_coeff * (step - constant_step) / total_step, max_kl_coeff), min_kl_coeff)
+    kl_lambda = max(
+        min(
+            min_kl_coeff + (max_kl_coeff - min_kl_coeff) *
+            (step - constant_step) / total_step, max_kl_coeff), min_kl_coeff)
+    return torch.tensor(kl_lambda, device=dist_util.dev())
+
+
+def depth_smoothness_loss(alpha_pred, depth_pred):
+    # from PesonNeRF paper.
+    # all Tensor shape B 1 H W
+    geom_loss = (
+        alpha_pred[..., :-1] * alpha_pred[..., 1:] * (
+            depth_pred[..., :-1] - depth_pred[..., 1:]  # W dim
+        ).square()).mean()  # mean of ([8, 1, 64, 63])
+
+    geom_loss += (alpha_pred[..., :-1, :] * alpha_pred[..., 1:, :] *
+                  (depth_pred[..., :-1, :] - depth_pred[..., 1:, :]).square()
+                  ).mean()  # H dim, ([8, 1, 63, 64])
+
+    return geom_loss
+
+
+# https://github.com/elliottwu/unsup3d/blob/master/unsup3d/networks.py#L140
+class LPIPSLoss(torch.nn.Module):
+
+    def __init__(
+        self,
+        loss_weight=1.0,
+        use_input_norm=True,
+        range_norm=True,
+        # n1p1_input=True,
+    ):
+        super(LPIPSLoss, self).__init__()
+        # self.perceptual = lpips.LPIPS(net="alex", spatial=False).eval()
+        self.perceptual = lpips.LPIPS(net="vgg", spatial=False).eval()
+        self.loss_weight = loss_weight
+        self.use_input_norm = use_input_norm
+        self.range_norm = range_norm
+
+        # if self.use_input_norm:
+        #     # the mean is for image with range [0, 1]
+        #     self.register_buffer(
+        #         'mean',
+        #         torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+        #     # the std is for image with range [0, 1]
+        #     self.register_buffer(
+        #         'std',
+        #         torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+    def forward(self, pred, target, conf_sigma_percl=None):
+        # st()
+        # ! add large image support, only sup 128x128 patch
+        lpips_loss = self.perceptual(target.contiguous(), pred.contiguous())
+        return self.loss_weight * lpips_loss.mean()
+
+
+# mask-aware perceptual loss
+class PerceptualLoss(nn.Module):
+
+    def __init__(self, requires_grad=False):
+        super(PerceptualLoss, self).__init__()
+        mean_rgb = torch.FloatTensor([0.485, 0.456, 0.406])
+        std_rgb = torch.FloatTensor([0.229, 0.224, 0.225])
+        self.register_buffer('mean_rgb', mean_rgb)
+        self.register_buffer('std_rgb', std_rgb)
+
+        vgg_pretrained_features = torchvision.models.vgg16(
+            pretrained=True).features
+        self.slice1 = nn.Sequential()
+        self.slice2 = nn.Sequential()
+        self.slice3 = nn.Sequential()
+        self.slice4 = nn.Sequential()
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def normalize(self, x):
+        out = x / 2 + 0.5
+        out = (out - self.mean_rgb.view(1, 3, 1, 1)) / self.std_rgb.view(
+            1, 3, 1, 1)
+        return out
+
+    def __call__(self, im1, im2, mask=None, conf_sigma=None):
+        im = torch.cat([im1, im2], 0)
+        im = self.normalize(im)  # normalize input
+
+        ## compute features
+        feats = []
+        f = self.slice1(im)
+        feats += [torch.chunk(f, 2, dim=0)]
+        f = self.slice2(f)
+        feats += [torch.chunk(f, 2, dim=0)]
+        f = self.slice3(f)
+        feats += [torch.chunk(f, 2, dim=0)]
+        f = self.slice4(f)
+        feats += [torch.chunk(f, 2, dim=0)]
+
+        losses = []
+        for f1, f2 in feats[2:3]:  # use relu3_3 features only
+            loss = (f1 - f2)**2
+            if conf_sigma is not None:
+                loss = loss / (2 * conf_sigma**2 + EPS) + (conf_sigma +
+                                                           EPS).log()
+            if mask is not None:
+                b, c, h, w = loss.shape
+                _, _, hm, wm = mask.shape
+                sh, sw = hm // h, wm // w
+                mask0 = nn.functional.avg_pool2d(mask,
+                                                 kernel_size=(sh, sw),
+                                                 stride=(sh,
+                                                         sw)).expand_as(loss)
+                loss = (loss * mask0).sum() / mask0.sum()
+            else:
+                loss = loss.mean()
+            losses += [loss]
+        return sum(losses)
+
+
+# add confidence support, unsup3d version
+def photometric_loss_laplace(im1, im2, mask=None, conf_sigma=None):
+    loss = (im1 - im2).abs()
+    # loss = (im1 - im2).square()
+    if conf_sigma is not None:
+        loss = loss * 2**0.5 / (conf_sigma + EPS) + (conf_sigma + EPS).log()
+
+    if mask is not None:
+        mask = mask.expand_as(loss)
+        loss = (loss * mask).sum() / mask.sum()
+
+    else:
+        loss = loss.mean()
+
+    return loss
+
+
+# gaussian likelihood version, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
+# also used in the mask-aware vgg loss
+def photometric_loss(im1, im2, mask=None, conf_sigma=None):
+    # loss = torch.nn.functional.mse_loss(im1, im2, reduce='none')
+    loss = (im1 - im2).square()
+
+    if conf_sigma is not None:
+        loss = loss / (2 * conf_sigma**2 + EPS) + (conf_sigma + EPS).log()
+
+    if mask is not None:
+        mask = mask.expand_as(loss)
+        loss = (loss * mask).sum() / mask.sum()
+
+    else:
+        loss = loss.mean()
+
+    return loss
+
+
+class E3DGELossClass(torch.nn.Module):
+
+    def __init__(self, device, opt) -> None:
+        super().__init__()
+
+        self.opt = opt
+        self.device = device
+        self.criterionImg = {
+            'mse': torch.nn.MSELoss(),
+            'l1': torch.nn.L1Loss(),
+            'charbonnier': CharbonnierLoss(),
+        }[opt.color_criterion]
+
+        self.criterion_latent = {
+            'mse': torch.nn.MSELoss(),
+            'l1': torch.nn.L1Loss(),
+            'vae': feature_vae_loss
+        }[opt.latent_criterion]
+
+        # self.criterionLPIPS = LPIPS(net_type='alex', device=device).eval()
+        if opt.lpips_lambda > 0:
+            self.criterionLPIPS = LPIPSLoss(loss_weight=opt.lpips_lambda)
+        # self.criterionLPIPS = torch.nn.MSELoss()
+
+        if opt.id_lambda > 0:
+            self.criterionID = IDLoss(device=device).eval()
+        self.id_loss_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
+
+        # define 3d rec loss, for occupancy
+        # self.criterion3d_rec = torch.nn.SmoothL1Loss(reduction='none')
+        # self.criterion_alpha = torch.nn.SmoothL1Loss()
+
+        # self.criterion3d_rec = torch.nn.MSELoss(reduction='none')
+        self.criterion_alpha = torch.nn.L1Loss()
+
+        if self.opt.xyz_lambda > 0:
+            # self.criterion_xyz = torch.nn.SmoothL1Loss()
+            self.criterion_xyz = torch.nn.L1Loss() # follow LION, but noisy xyz here...
+
+        if self.opt.depth_lambda > 0:
+        # ! this depth loss not converging, no idea why
+            self.criterion3d_rec = ScaleAndShiftInvariantLoss(alpha=0.5,
+                                                              scales=1)
+        else:
+            self.criterion3d_rec = torch.nn.SmoothL1Loss(reduction='none')
+
+        #     self.silog_loss = SILogLoss()
+
+        if self.opt.lambda_opa_reg > 0:
+            # self.beta_mvp_dist = torch.distributions.beta.Beta(torch.tensor(0.5, device=device), torch.tensor(0.5, device=device))
+            # self.beta_mvp_base_dist = torch.distributions.beta.Beta(torch.tensor(10, device=device), torch.tensor(0.5, device=device)) # force close to 1 for base
+            # self.beta_mvp_base_dist = torch.distributions.beta.Beta(torch.tensor(0.6, device=device), torch.tensor(0.2, device=device)) # force close to 1 for base
+            self.beta_mvp_base_dist = torch.distributions.beta.Beta(torch.tensor(0.5, device=device), torch.tensor(0.25, device=device)) # force close to 1 for base
+
+        logger.log('init loss class finished', )
+
+    def calc_scale_invariant_depth_loss(self, pred_depth: torch.Tensor,
+                                        gt_depth: torch.Tensor,
+                                        gt_depth_mask: torch.Tensor):
+        """apply 3d shape reconstruction supervision. Basically supervise the depth with L1 loss
+        """
+
+        shape_loss_dict = {}
+        assert gt_depth_mask is not None
+        shape_loss = self.criterion3d_rec(pred_depth, gt_depth, gt_depth_mask)
+
+        # if shape_loss > 0.2:  # hinge loss, avoid ood gradient
+        #     shape_loss = torch.zeros_like(shape_loss)
+        # else:
+        shape_loss = shape_loss.clamp(0.04) # g-buffer depth is very noisy
+        shape_loss *= self.opt.depth_lambda
+
+        shape_loss_dict['loss_depth'] = shape_loss
+        # shape_loss_dict['depth_fgratio'] = gt_depth_mask.mean()
+
+        # return l_si, shape_loss_dict
+        return shape_loss, shape_loss_dict
+
+    def calc_depth_loss(self, pred_depth: torch.Tensor, gt_depth: torch.Tensor,
+                        gt_depth_mask: torch.Tensor):
+        """apply 3d shape reconstruction supervision. Basically supervise the depth with L1 loss
+        """
+
+        shape_loss_dict = {}
+        shape_loss = self.criterion3d_rec(pred_depth, gt_depth)
+        assert gt_depth_mask is not None
+
+        shape_loss *= gt_depth_mask
+        shape_loss = shape_loss.sum() / gt_depth_mask.sum()
+
+        # else:
+        #     shape_loss /= pred_depth.numel()
+        # l_si = self.silog_loss(pred_depth, gt_depth, mask=None, interpolate=True, return_interpolated=False)
+
+        # l_si *= self.opt.depth_lambda
+        # shape_loss_dict['loss_depth'] = l_si
+        shape_loss_dict['loss_depth'] = shape_loss.clamp(
+            min=0, max=0.1) * self.opt.depth_lambda
+
+        # shape_loss_dict['loss_depth'] = shape_loss.clamp(
+        #     min=0, max=0.5) * self.opt.depth_lambda
+
+        # return l_si, shape_loss_dict
+        return shape_loss, shape_loss_dict
+
+    @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def calc_alpha_loss(self, pred_alpha, gt_depth_mask):
+        # return self.criterionImg(alpha, gt_depth_mask.float())
+
+        if gt_depth_mask.ndim == 3:
+            gt_depth_mask = gt_depth_mask.unsqueeze(1)
+
+        if gt_depth_mask.shape[1] == 3:
+            gt_depth_mask = gt_depth_mask[:, 0:1, ...]  # B 1 H W
+
+        assert pred_alpha.shape == gt_depth_mask.shape
+
+        alpha_loss = self.criterion_alpha(pred_alpha, gt_depth_mask)
+        # st()
+
+        return alpha_loss
+
+    @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def calc_mask_mse_loss(
+            self,
+            input,
+            gt,
+            gt_depth_mask,
+            #    conf_sigma=None,
+            conf_sigma_l1=None,
+            #    conf_sigma_percl=None,
+            use_fg_ratio=False):
+        if gt_depth_mask.ndim == 3:
+            gt_depth_mask = gt_depth_mask.unsqueeze(1).repeat_interleave(3, 1)
+        else:
+            assert gt_depth_mask.shape == input.shape
+        gt_depth_mask = gt_depth_mask.float()
+
+        if conf_sigma_l1 is None:
+            rec_loss = torch.nn.functional.mse_loss(
+                input.float(), gt.float(),
+                reduction='none')  # 'sum' already divide by batch size n
+        else:
+            rec_loss = photometric_loss(
+                input, gt, gt_depth_mask, conf_sigma_l1
+            )  # ! only cauclate laplace on the foreground, or bg confidence low, large gradient.
+            return rec_loss
+            # rec_loss = torch.nn.functional.l1_loss( # for laplace loss
+            #     input.float(), gt.float(),
+            #     reduction='none')  # 'sum' already divide by batch size n
+        # gt_depth_mask = torch.ones_like(gt_depth_mask) # ! DEBUGGING
+
+        # if conf_sigma is not None: # from unsup3d, but a L2 version
+        #     rec_loss = rec_loss * 2**0.5 / (conf_sigma + EPS) + (conf_sigma +
+        #                                                          EPS).log()
+        #     return rec_loss.mean()
+        # rec_loss = torch.exp(-(rec_loss * 2**0.5 / (conf_sigma + EPS))) * 1/(conf_sigma +
+        #                                                      EPS) / (2**0.5)
+
+        fg_size = gt_depth_mask.sum()
+        # fg_ratio = fg_size / torch.ones_like(gt_depth_mask).sum() if use_fg_ratio else 1
+        fg_loss = rec_loss * gt_depth_mask
+        fg_loss = fg_loss.sum() / fg_size  # * fg_ratio
+
+        if self.opt.bg_lamdba > 0:
+            bg_loss = rec_loss * (1 - gt_depth_mask)
+            bg_loss = bg_loss.sum() / (1 - gt_depth_mask).sum()
+            rec_loss = fg_loss + bg_loss * self.opt.bg_lamdba
+        else:
+            rec_loss = fg_loss
+
+        return rec_loss
+
+    @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def calc_2d_rec_loss(
+        self,
+        input,
+        gt,
+        depth_fg_mask,
+        test_mode=True,
+        step=1,
+        ignore_lpips=False,
+        # conf_sigma=None,
+        conf_sigma_l1=None,
+        conf_sigma_percl=None,
+        pred_alpha=None,
+    ):
+        opt = self.opt
+        loss_dict = {}
+
+        # logger.log(test_mode)
+        # logger.log(input.min(), input.max(), gt.min(), gt.max())
+        if test_mode or not opt.fg_mse:
+            rec_loss = self.criterionImg(input, gt)
+        else:
+            rec_loss = self.calc_mask_mse_loss(
+                input,
+                gt,
+                depth_fg_mask,
+                conf_sigma_l1=conf_sigma_l1,
+            )
+            #   conf_sigma_percl=conf_sigma_percl)
+            #    conf_sigma)
+
+        # if step == 300:
+        #     st()
+
+        if opt.lpips_lambda > 0 and step >= opt.lpips_delay_iter and not ignore_lpips:  # tricky solution to avoid NAN in LPIPS loss
+
+            # with torch.autocast(device_type='cuda',
+            #                     dtype=torch.float16,
+            #                     enabled=False):
+            # if test_mode or not opt.fg_mse:  # no need to calculate background lpips for ease of computation
+            # inp_for_lpips = input * pred_alpha + torch.ones_like(input) * (1-pred_alpha)
+            # gt_for_lpips = gt * depth_fg_mask + torch.ones_like(gt) * (1-depth_fg_mask)
+            
+            inp_for_lpips = input * pred_alpha
+            gt_for_lpips = gt * depth_fg_mask
+
+            width = input.shape[-1]
+            if width == 192: # triplane here
+                lpips_loss = self.criterionLPIPS( # loss on 128x128 center crop
+                    inp_for_lpips[:, :, width//2-64:width//2+64, width//2-64:width//2+64],
+                    gt_for_lpips[:, :, width//2-64:width//2+64, width//2-64:width//2+64],
+                    conf_sigma_percl=conf_sigma_percl,
+                )
+            elif width >256:
+            # elif width >192:
+                # lpips_loss = self.criterionLPIPS(
+                #     F.interpolate(inp_for_lpips, (256,256), mode='bilinear'),
+                #     F.interpolate(gt_for_lpips, (256,256), mode='bilinear'),
+                #     conf_sigma_percl=conf_sigma_percl,
+                # )
+
+                # patch = 80
+
+                # patch = 128
+                patch = 144
+                middle_point = width // 2
+                lpips_loss = self.criterionLPIPS( # loss on 128x128 center crop
+                    inp_for_lpips[:, :, middle_point-patch:middle_point+patch, middle_point-patch:middle_point+patch],
+                    gt_for_lpips[:, :,  middle_point-patch:middle_point+patch, middle_point-patch:middle_point+patch],
+                    conf_sigma_percl=conf_sigma_percl,
+                )
+
+            else: # directly supervise when <= 256
+                # ! add foreground mask
+                assert pred_alpha is not None
+                lpips_loss = self.criterionLPIPS(
+                    inp_for_lpips,
+                    gt_for_lpips,
+                    # conf_sigma_percl=conf_sigma_percl,
+                )
+            # else:  # fg lpips
+            #     assert depth_fg_mask.shape == input.shape
+            #     lpips_loss = self.criterionLPIPS(
+            #         input.contiguous() * depth_fg_mask,
+            #         gt.contiguous() * depth_fg_mask).mean()
+        else:
+            lpips_loss = torch.tensor(0., device=input.device)
+
+        if opt.ssim_lambda > 0:
+            loss_ssim = self.ssim_loss(input, gt)  #?
+        else:
+            loss_ssim = torch.tensor(0., device=input.device)
+
+        loss_psnr = self.psnr((input / 2 + 0.5), (gt / 2 + 0.5), 1.0)
+
+        if opt.id_lambda > 0:
+            loss_id = self._calc_loss_id(input, gt)
+        else:
+            loss_id = torch.tensor(0., device=input.device)
+
+        if opt.l1_lambda > 0:
+            loss_l1 = F.l1_loss(input, gt)
+        else:
+            loss_l1 = torch.tensor(0., device=input.device)
+
+        # loss = rec_loss * opt.l2_lambda + lpips_loss * opt.lpips_lambda + loss_id * opt.id_lambda + loss_ssim * opt.ssim_lambda
+        rec_loss = rec_loss * opt.l2_lambda
+        loss = rec_loss + lpips_loss + loss_id * opt.id_lambda + loss_ssim * opt.ssim_lambda + opt.l1_lambda * loss_l1
+
+        # if return_dict:
+        loss_dict['loss_l2'] = rec_loss
+        loss_dict['loss_id'] = loss_id
+        loss_dict['loss_lpips'] = lpips_loss
+        loss_dict['loss'] = loss
+        loss_dict['loss_ssim'] = loss_ssim
+
+        # metrics to report, not involved in training
+        loss_dict['mae'] = loss_l1
+        loss_dict['PSNR'] = loss_psnr
+        loss_dict['SSIM'] = 1 - loss_ssim  # Todo
+        loss_dict['ID_SIM'] = 1 - loss_id
+
+        return loss, loss_dict
+
+    @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def calc_shape_rec_loss(
+        self,
+        pred_shape: dict,
+        gt_shape: dict,
+        device,
+    ):
+        """apply 3d shape reconstruction supervision. Basically supervise the densities with L1 loss
+
+        Args:
+            pred_shape (dict): dict contains reconstructed shape information
+            gt_shape (dict): dict contains gt shape information
+            supervise_sdf (bool, optional): whether supervise sdf rec. Defaults to True.
+            supervise_surface_normal (bool, optional): whether supervise surface rec. Defaults to False.
+
+        Returns:
+            dict: shape reconstruction loss
+        """
+
+        shape_loss_dict = {}
+        shape_loss = 0
+        # assert supervise_sdf or supervise_surface_normal, 'should at least supervise one types of shape reconstruction'
+        # todo, add weights
+
+        if self.opt.shape_uniform_lambda > 0:
+            shape_loss_dict['coarse'] = self.criterion3d_rec(
+                pred_shape['coarse_densities'].squeeze(),
+                gt_shape['coarse_densities'].squeeze())
+            shape_loss += shape_loss_dict[
+                'coarse'] * self.opt.shape_uniform_lambda
+
+        if self.opt.shape_importance_lambda > 0:
+            shape_loss_dict['fine'] = self.criterion3d_rec(
+                pred_shape['fine_densities'].squeeze(),  # ? how to supervise
+                gt_shape['fine_densities'].squeeze())
+            shape_loss += shape_loss_dict[
+                'fine'] * self.opt.shape_importance_lambda
+
+        loss_depth = self.criterion_alpha(pred_shape['image_depth'],
+                                          gt_shape['image_depth'])
+
+        shape_loss += loss_depth * self.opt.shape_depth_lambda
+        shape_loss_dict.update(dict(loss_depth=loss_depth))
+        # TODO, add on surface pts supervision ?
+
+        return shape_loss, shape_loss_dict
+
+    @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def psnr(self, input, target, max_val):
+        return kornia.metrics.psnr(input, target, max_val)
+
+    # @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def ssim_loss(self, img1, img2, window_size=11, size_average=True):
+        channel = img1.size(-3)
+        window = create_window(window_size, channel)
+
+        if img1.is_cuda:
+            window = window.cuda(img1.get_device())
+        window = window.type_as(img1)
+
+        return 1 - _ssim(img1, img2, window, window_size, channel, size_average)
+
+    @torch.autocast(device_type='cuda', dtype=torch.float16, enabled=False)
+    def forward(self,
+                pred,
+                gt,
+                test_mode=True,
+                step=1,
+                return_fg_mask=False,
+                conf_sigma_l1=None,
+                conf_sigma_percl=None,
+                ignore_kl=False,
+                ignore_lpips=False,
+                *args,
+                **kwargs):
+
+        with torch.autocast(device_type='cuda',
+                            dtype=torch.float16,
+                            enabled=False):
+
+            loss = torch.tensor(0., device=self.device)
+            loss_dict = {}
+
+            if 'image_mask' in pred:
+                pred_alpha = pred['image_mask']  # B 1 H W
+            else:
+                N, _, H, W = pred['image_depth'].shape
+                pred_alpha = pred['weights_samples'].permute(0, 2, 1).reshape(
+                    N, 1, H, W)
+
+            # balance rec_loss with logvar
+            # if 'depth_mask' in gt:
+            if self.opt.online_mask:
+                # https://github.com/elliottwu/unsup3d/blob/dc961410d61684561f19525c2f7e9ee6f4dacb91/unsup3d/model.py#L193
+                margin = (self.opt.max_depth - self.opt.min_depth) / 2
+                fg_mask = (pred['image_depth']
+                           < self.opt.max_depth + margin).float()  # B 1 H W
+                fg_mask = fg_mask.repeat_interleave(3, 1).float()
+
+            else:
+                if 'depth_mask' in gt:
+                    if gt['depth_mask'].shape[1] != 1:
+                        fg_mask = gt['depth_mask'].unsqueeze(1)
+                    else:
+                        fg_mask = gt['depth_mask']
+                    fg_mask = fg_mask.repeat_interleave(
+                        3, 1).float()
+                else:
+                    fg_mask = None
+
+            loss_2d, loss_2d_dict = self.calc_2d_rec_loss(
+                pred['image_raw'],
+                gt['img'],
+                fg_mask,
+                test_mode=test_mode,
+                step=step,
+                ignore_lpips=ignore_lpips,
+                conf_sigma_l1=conf_sigma_l1,
+                conf_sigma_percl=conf_sigma_percl,
+                pred_alpha=pred_alpha,
+                )
+            #   ignore_lpips=self.opt.fg_mse)
+
+            if self.opt.kl_lambda > 0 and not ignore_kl:
+                # assert 'posterior' in pred, 'logvar' in pred
+                assert 'posterior' in pred
+
+                if self.opt.kl_anneal:
+                    kl_lambda = kl_coeff(
+                        step=step,
+                        constant_step=5e3,  # 1w steps
+                        total_step=25e3,  # 5w steps in total
+                        min_kl_coeff=max(1e-9, self.opt.kl_lambda / 1e4),
+                        max_kl_coeff=self.opt.kl_lambda)
+                    loss_dict['kl_lambda'] = kl_lambda
+                else:
+                    loss_dict['kl_lambda'] = torch.tensor(
+                        self.opt.kl_lambda, device=dist_util.dev())
+
+                if self.opt.pt_ft_kl:
+                    pt_kl, ft_kl = pred['posterior'].kl(pt_ft_separate=True)
+                    kl_batch = pt_kl.shape[0]
+
+                    # loss_dict['kl_loss_pt'] = pt_kl.sum() * loss_dict['kl_lambda'] * 0.01 / kl_batch
+                    loss_dict['kl_loss_pt'] = pt_kl.sum() * loss_dict['kl_lambda'] * 0 # no compression at all.
+                    loss_dict['kl_loss_ft'] = ft_kl.sum() * loss_dict['kl_lambda'] / kl_batch
+                    loss = loss + loss_dict['kl_loss_pt'] + loss_dict['kl_loss_ft']
+
+                    loss_dict['latent_mu_pt'] = pred['posterior'].mean[:, :3].mean()
+                    loss_dict['latent_std_pt'] = pred['posterior'].std[:, :3].mean()
+
+                    loss_dict['latent_mu_ft'] = pred['posterior'].mean[:, 3:].mean()
+                    loss_dict['latent_std_ft'] = pred['posterior'].std[:, 3:].mean()
+
+                elif self.opt.ft_kl:
+
+                    ft_kl = pred['posterior'].kl(ft_separate=True)
+                    kl_batch = ft_kl.shape[0]
+
+                    loss_dict['kl_loss_ft'] = ft_kl.sum() * loss_dict['kl_lambda'] / kl_batch
+                    loss = loss + loss_dict['kl_loss_ft']
+
+                    loss_dict['latent_mu_ft'] = pred['posterior'].mean[:, :].square().mean().float().detach()
+                    loss_dict['latent_std_ft'] = pred['posterior'].std[:, :].mean().float().detach()
+
+                else:
+                    
+                    kl_loss = pred['posterior'].kl()
+                    kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+
+                    loss_dict['kl_loss'] = kl_loss * loss_dict['kl_lambda']
+                    loss += loss_dict['kl_loss']
+                    loss_dict['latent_mu'] = pred['posterior'].mean.mean()
+                    loss_dict['latent_std'] = pred['posterior'].std.mean()
+
+                # nll_loss = loss_2d / torch.exp(pred['logvar']) + pred['logvar'] # nll_loss
+                nll_loss = loss_2d
+                loss += nll_loss
+
+                loss_dict.update(dict(nll_loss=nll_loss))
+
+                # loss_dict['latent_mu'] = pred['latent_normalized'].mean()
+                # loss_dict['latent_max'] = pred['latent_normalized'].max()
+                # loss_dict['latent_min'] = pred['latent_normalized'].min()
+                # loss_dict['latent_std'] = pred['latent_normalized'].std()
+                #  pred[
+                #     'latent_normalized_2Ddiffusion'].mean()
+                # loss_dict['latent_std'] = pred[
+                #     'latent_normalized_2Ddiffusion'].std()
+                # loss_dict['latent_max'] = pred[
+                #     'latent_normalized_2Ddiffusion'].max()
+                # loss_dict['latent_min'] = pred[
+                #     'latent_normalized_2Ddiffusion'].min()
+
+            else:
+                loss += loss_2d
+
+            # if 'image_sr' in pred and pred['image_sr'].shape==gt['img_sr']:
+            if 'image_sr' in pred:
+
+                if 'depth_mask_sr' in gt:
+                    depth_mask_sr = gt['depth_mask_sr'].unsqueeze(
+                        1).repeat_interleave(3, 1).float()
+                else:
+                    depth_mask_sr = None
+
+                loss_sr, loss_sr_dict = self.calc_2d_rec_loss(
+                    pred['image_sr'],
+                    gt['img_sr'],
+                    depth_fg_mask=depth_mask_sr,
+                    # test_mode=test_mode,
+                    test_mode=True,
+                    step=step)
+                loss_sr_lambda = 1
+                if step < self.opt.sr_delay_iter:
+                    loss_sr_lambda = 0
+                loss += loss_sr * loss_sr_lambda
+                for k, v in loss_sr_dict.items():
+                    loss_dict['sr_' + k] = v * loss_sr_lambda
+
+            if self.opt.depth_lambda > 0:  # TODO, switch to scale-agnostic depth loss
+                assert 'depth' in gt
+                pred_depth = pred['image_depth']
+                if pred_depth.ndim == 4:
+                    pred_depth = pred_depth.squeeze(1)  # B H W
+
+                # _, shape_loss_dict = self.calc_depth_loss(
+                #     pred_depth, gt['depth'], fg_mask[:, 0, ...])
+                _, shape_loss_dict = self.calc_scale_invariant_depth_loss(
+                    pred_depth, gt['depth'], fg_mask[:, 0, ...])
+                loss += shape_loss_dict['loss_depth']
+                loss_dict.update(shape_loss_dict)
+
+            # if self.opt.latent_lambda > 0:  # make sure the latent suits diffusion learning
+            #     latent_mu = pred['latent'].mean()
+            #     loss_latent = self.criterion_latent(
+            #         latent_mu, torch.zeros_like(
+            #             latent_mu))  # only regularize the mean value here
+            #     loss_dict['loss_latent'] = loss_latent
+            #     loss += loss_latent * self.opt.latent_lambda
+
+            if self.opt.alpha_lambda > 0 and 'image_depth' in pred:
+                loss_alpha = self.calc_alpha_loss(pred_alpha, fg_mask)
+                loss_dict['loss_alpha'] = loss_alpha * self.opt.alpha_lambda
+                loss += loss_alpha * self.opt.alpha_lambda
+
+            if self.opt.depth_smoothness_lambda > 0:
+                loss_depth_smoothness = depth_smoothness_loss(
+                    pred_alpha,
+                    pred['image_depth']) * self.opt.depth_smoothness_lambda
+                loss_dict['loss_depth_smoothness'] = loss_depth_smoothness
+                loss += loss_depth_smoothness
+
+            loss_2d_dict['all_loss'] = loss
+            loss_dict.update(loss_2d_dict)
+
+            # if return_fg_mask:
+            return loss, loss_dict, fg_mask
+            # else:
+            #     return loss, loss_dict
+
+    def _calc_loss_id(self, input, gt):
+        if input.shape[-1] != 256:
+            arcface_input = self.id_loss_pool(input)
+            id_loss_gt = self.id_loss_pool(gt)
+        else:
+            arcface_input = input
+            id_loss_gt = gt
+
+        loss_id, _, _ = self.criterionID(arcface_input, id_loss_gt, id_loss_gt)
+
+        return loss_id
+
+    def calc_2d_rec_loss_misaligned(self, input, gt):
+        """id loss + vgg loss
+
+        Args:
+            input (_type_): _description_
+            gt (_type_): _description_
+            depth_mask (_type_): _description_
+            test_mode (bool, optional): _description_. Defaults to True.
+        """
+        opt = self.opt
+        loss_dict = {}
+
+        if opt.lpips_lambda > 0:
+            with torch.autocast(
+                    device_type='cuda', dtype=torch.float16,
+                    enabled=False):  # close AMP for lpips to avoid nan
+                lpips_loss = self.criterionLPIPS(input, gt)
+        else:
+            lpips_loss = torch.tensor(0., device=input.device)
+
+        if opt.id_lambda > 0:
+            loss_id = self._calc_loss_id(input, gt)
+        else:
+            loss_id = torch.tensor(0., device=input.device)
+
+        loss_dict['loss_id_real'] = loss_id
+        loss_dict['loss_lpips_real'] = lpips_loss
+
+        loss = lpips_loss * opt.lpips_lambda + loss_id * opt.id_lambda
+
+        return loss, loss_dict
+
+
+class E3DGE_with_AdvLoss(E3DGELossClass):
+    # adapted from sgm/modules/autoencoding/losses/discriminator_loss.py
+
+    def __init__(
+        self,
+        device,
+        opt,
+        discriminator_config: Optional[Dict] = None,
+        disc_num_layers: int = 3,
+        disc_in_channels: int = 3,
+        disc_start: int = 0,
+        disc_loss: str = "hinge",
+        disc_factor: float = 1.0,
+        disc_weight: float = 1.0,
+        regularization_weights: Union[None, Dict[str, float]] = None,
+        dtype=torch.float32,
+        # additional_log_keys: Optional[List[str]] = None,
+    ) -> None:
+        super().__init__(
+            device,
+            opt,
+        )
+
+        # ! initialize GAN loss
+        discriminator_config = default(
+            discriminator_config,
+            {
+                "target":
+                "nsr.losses.disc.NLayerDiscriminator",
+                "params": {
+                    "input_nc": disc_in_channels,
+                    "n_layers": disc_num_layers,
+                    "use_actnorm": False,
+                },
+            },
+        )
+
+        self.discriminator = instantiate_from_config(
+            discriminator_config).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight # self.regularization_weights = default(regularization_weights, {})
+
+        # self.forward_keys = [
+        #     "optimizer_idx",
+        #     "global_step",
+        #     "last_layer",
+        #     "split",
+        #     "regularization_log",
+        # ]
+
+        # self.additional_log_keys = set(default(additional_log_keys, []))
+        # self.additional_log_keys.update(set(
+        #     self.regularization_weights.keys()))
+
+    def get_trainable_parameters(self) -> Iterator[nn.Parameter]:
+        return self.discriminator.parameters()
+
+    def forward(self,
+                pred,
+                gt,
+                behaviour: str,
+                test_mode=True,
+                step=1,
+                return_fg_mask=False,
+                conf_sigma_l1=None,
+                conf_sigma_percl=None,
+                ignore_d_loss=False,
+                *args,
+                **kwargs):
+
+        # now the GAN part
+        reconstructions = pred['image_raw']
+        inputs = gt['img']
+
+        if behaviour == 'g_step':
+
+            nll_loss, loss_dict, fg_mask = super().forward(
+                pred,
+                gt,
+                test_mode,
+                step, 
+                return_fg_mask,
+                conf_sigma_l1,
+                conf_sigma_percl,
+                *args,
+                **kwargs)
+
+            # generator update
+            if not ignore_d_loss and (step >= self.discriminator_iter_start or not self.training):
+                logits_fake = self.discriminator(reconstructions.contiguous())
+                g_loss = -torch.mean(logits_fake)
+                if self.training:
+                    d_weight = torch.tensor(self.discriminator_weight)
+                    # d_weight = self.calculate_adaptive_weight(
+                    #     nll_loss, g_loss, last_layer=last_layer)
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+            
+            g_loss = g_loss * d_weight * self.disc_factor
+
+            loss = nll_loss + g_loss
+
+            # TODO
+            loss_dict.update({
+                f"loss/g": g_loss.detach().mean(),
+            })
+
+            # return loss, log
+            return loss, loss_dict, fg_mask
+
+        elif behaviour == 'd_step' and not ignore_d_loss:
+            # second pass for discriminator update
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(
+                reconstructions.contiguous().detach())
+
+            if step >= self.discriminator_iter_start or not self.training:
+                d_loss = self.disc_factor * self.disc_loss(
+                    logits_real, logits_fake)
+            else:
+                d_loss = torch.tensor(0.0, requires_grad=True)
+
+            loss_dict = {}
+
+            loss_dict.update({
+                "loss/disc": d_loss.clone().detach().mean(),
+                "logits/real": logits_real.detach().mean(),
+                "logits/fake": logits_fake.detach().mean(),
+            })
+
+            return d_loss, loss_dict, None
+        else:
+            raise NotImplementedError(f"Unknown optimizer behaviour {behaviour}")
diff --git a/nsr/losses/disc.py b/nsr/losses/disc.py
new file mode 100644
index 0000000000000000000000000000000000000000..43e64ea7af0c1184e858525deef4b19fde86c511
--- /dev/null
+++ b/nsr/losses/disc.py
@@ -0,0 +1,88 @@
+import functools
+
+import torch.nn as nn
+
+from .util import ActNorm
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm") != -1:
+        nn.init.normal_(m.weight.data, 1.0, 0.02)
+        nn.init.constant_(m.bias.data, 0)
+
+
+class NLayerDiscriminator(nn.Module):
+    """Defines a PatchGAN discriminator as in Pix2Pix
+    --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+    """
+
+    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
+        """Construct a PatchGAN discriminator
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super(NLayerDiscriminator, self).__init__()
+        if not use_actnorm:
+            norm_layer = nn.BatchNorm2d
+        else:
+            norm_layer = ActNorm
+        if (
+            type(norm_layer) == functools.partial
+        ):  # no need to use bias as BatchNorm2d has affine parameters
+            use_bias = norm_layer.func != nn.BatchNorm2d
+        else:
+            use_bias = norm_layer != nn.BatchNorm2d
+
+        kw = 4
+        padw = 1
+        sequence = [
+            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
+            nn.LeakyReLU(0.2, True),
+        ]
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = min(2**n, 8)
+            sequence += [
+                nn.Conv2d(
+                    ndf * nf_mult_prev,
+                    ndf * nf_mult,
+                    kernel_size=kw,
+                    stride=2,
+                    padding=padw,
+                    bias=use_bias,
+                ),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True),
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2**n_layers, 8)
+        sequence += [
+            nn.Conv2d(
+                ndf * nf_mult_prev,
+                ndf * nf_mult,
+                kernel_size=kw,
+                stride=1,
+                padding=padw,
+                bias=use_bias,
+            ),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True),
+        ]
+
+        sequence += [
+            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
+        ]  # output 1 channel prediction map
+        self.main = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        """Standard forward."""
+        return self.main(input)
diff --git a/nsr/losses/helpers.py b/nsr/losses/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..abaf044f5fb0d7ec98b21e820cb8291560024e3b
--- /dev/null
+++ b/nsr/losses/helpers.py
@@ -0,0 +1,378 @@
+from collections import namedtuple
+from pdb import set_trace as st
+import torch
+import numpy as np
+import torch.nn.functional as F
+import torch.nn as nn
+from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
+"""
+ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+# from nsr.networks_stylegan2 import FullyConnectedLayer as EqualLinear
+
+# class GradualStyleBlock(Module):
+
+#     def __init__(self, in_c, out_c, spatial):
+#         super(GradualStyleBlock, self).__init__()
+#         self.out_c = out_c
+#         self.spatial = spatial
+#         num_pools = int(np.log2(spatial))
+#         modules = []
+#         modules += [
+#             Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
+#             nn.LeakyReLU()
+#         ]
+#         for i in range(num_pools - 1):
+#             modules += [
+#                 Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
+#                 nn.LeakyReLU()
+#             ]
+#         self.convs = nn.Sequential(*modules)
+#         self.linear = EqualLinear(out_c, out_c, lr_multiplier=1)
+
+#     def forward(self, x):
+#         x = self.convs(x)
+#         x = x.reshape(-1, self.out_c)
+# x = self.linear(x)
+# return x
+
+
+# from project.models.model import ModulatedConv2d
+class DemodulatedConv2d(nn.Module):
+    def __init__(self,
+                 in_channel,
+                 out_channel,
+                 kernel_size=3,
+                 stride=1,
+                 padding=0,
+                 bias=False,
+                 dilation=1):
+        super().__init__()
+        # https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/411. fix droplet issue
+
+        self.eps = 1e-8
+
+        if not isinstance(kernel_size, tuple):
+            self.kernel_size = (kernel_size, kernel_size)
+        else:
+            self.kernel_size = kernel_size
+
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+        self.weight = nn.Parameter(
+            # torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+            torch.randn(1, out_channel, in_channel, *kernel_size))
+        self.bias = None
+        if bias:
+            self.bias = nn.Parameter(torch.randn(out_channel))
+
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+
+    def forward(self, input):
+        batch, in_channel, height, width = input.shape
+
+        demod = torch.rsqrt(self.weight.pow(2).sum([2, 3, 4]) + 1e-8)
+        demod = demod.repeat_interleave(batch, 0)
+        weight = self.weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+        weight = weight.view(
+            # batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+            batch * self.out_channel,
+            in_channel,
+            *self.kernel_size)
+
+        input = input.view(1, batch * in_channel, height, width)
+        if self.bias is None:
+            out = F.conv2d(input,
+                           weight,
+                           padding=self.padding,
+                           groups=batch,
+                           dilation=self.dilation,
+                           stride=self.stride)
+        else:
+            out = F.conv2d(input,
+                           weight,
+                           bias=self.bias,
+                           padding=self.padding,
+                           groups=batch,
+                           dilation=self.dilation,
+                           stride=self.stride)
+        _, _, height, width = out.shape
+        out = out.view(batch, self.out_channel, height, width)
+
+        return out
+
+
+class Flatten(Module):
+    def forward(self, input):
+        return input.reshape(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+    norm = torch.norm(input, 2, axis, True)
+    output = torch.div(input, norm)
+    return output
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+    """ A named tuple describing a ResNet block. """
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+    return [Bottleneck(in_channel, depth, stride)
+            ] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+    if num_layers == 50:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=14),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=13),
+            get_block(in_channel=128, depth=256, num_units=30),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=8),
+            get_block(in_channel=128, depth=256, num_units=36),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    else:
+        raise ValueError(
+            "Invalid number of layers: {}. Must be one of [50, 100, 152]".
+            format(num_layers))
+    return blocks
+
+
+class SEModule(Module):
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = AdaptiveAvgPool2d(1)
+        self.fc1 = Conv2d(channels,
+                          channels // reduction,
+                          kernel_size=1,
+                          padding=0,
+                          bias=False)
+        self.relu = ReLU(inplace=True)
+        self.fc2 = Conv2d(channels // reduction,
+                          channels,
+                          kernel_size=1,
+                          padding=0,
+                          bias=False)
+        self.sigmoid = Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class bottleneck_IR(Module):
+    def __init__(self,
+                 in_channel,
+                 depth,
+                 stride,
+                 norm_layer=None,
+                 demodulate=False):
+        super(bottleneck_IR, self).__init__()
+        if norm_layer is None:
+            norm_layer = BatchNorm2d
+        if demodulate:
+            conv2d = DemodulatedConv2d
+        else:
+            conv2d = Conv2d
+
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                # Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                norm_layer(depth))
+
+
+# BatchNorm2d(depth)
+        self.res_layer = Sequential(
+            # BatchNorm2d(in_channel),
+            norm_layer(in_channel),
+            # Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            PReLU(depth),
+            # Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            norm_layer(depth))
+        # BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+        return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR_SE, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            BatchNorm2d(depth), SEModule(depth, 16))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+        return res + shortcut
+
+
+def _upsample_add(x, y):
+    """Upsample and add two feature maps.
+	Args:
+	  x: (Variable) top feature map to be upsampled.
+	  y: (Variable) lateral feature map.
+	Returns:
+	  (Variable) added feature map.
+	Note in PyTorch, when input size is odd, the upsampled feature map
+	with `F.upsample(..., scale_factor=2, mode='nearest')`
+	maybe not equal to the lateral feature map size.
+	e.g.
+	original input size: [N,_,15,15] ->
+	conv2d feature map size: [N,_,8,8] ->
+	upsampled feature map size: [N,_,16,16]
+	So we choose bilinear upsample which supports arbitrary output sizes.
+	"""
+    _, _, H, W = y.size()
+    return F.interpolate(x, size=(H, W), mode='bilinear',
+                         align_corners=True) + y
+
+
+# from NeuRay
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=3,
+                     stride=stride,
+                     padding=dilation,
+                     groups=groups,
+                     bias=False,
+                     dilation=dilation,
+                     padding_mode='reflect')
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=1,
+                     stride=stride,
+                     bias=False,
+                     padding_mode='reflect')
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_out,
+                 dim_inter=None,
+                 use_norm=True,
+                 norm_layer=nn.BatchNorm2d,
+                 bias=False):
+        super().__init__()
+        if dim_inter is None:
+            dim_inter = dim_out
+
+        if use_norm:
+            self.conv = nn.Sequential(
+                norm_layer(dim_in),
+                nn.ReLU(True),
+                nn.Conv2d(dim_in,
+                          dim_inter,
+                          3,
+                          1,
+                          1,
+                          bias=bias,
+                          padding_mode='reflect'),
+                norm_layer(dim_inter),
+                nn.ReLU(True),
+                nn.Conv2d(dim_inter,
+                          dim_out,
+                          3,
+                          1,
+                          1,
+                          bias=bias,
+                          padding_mode='reflect'),
+            )
+        else:
+            self.conv = nn.Sequential(
+                nn.ReLU(True),
+                nn.Conv2d(dim_in, dim_inter, 3, 1, 1),
+                nn.ReLU(True),
+                nn.Conv2d(dim_inter, dim_out, 3, 1, 1),
+            )
+
+        self.short_cut = None
+        if dim_in != dim_out:
+            self.short_cut = nn.Conv2d(dim_in, dim_out, 1, 1)
+
+    def forward(self, feats):
+        feats_out = self.conv(feats)
+        if self.short_cut is not None:
+            feats_out = self.short_cut(feats) + feats_out
+        else:
+            feats_out = feats_out + feats
+        return feats_out
+
+
+class conv(nn.Module):
+    def __init__(self, num_in_layers, num_out_layers, kernel_size, stride):
+        super(conv, self).__init__()
+        self.kernel_size = kernel_size
+        self.conv = nn.Conv2d(num_in_layers,
+                              num_out_layers,
+                              kernel_size=kernel_size,
+                              stride=stride,
+                              padding=(self.kernel_size - 1) // 2,
+                              padding_mode='reflect')
+        self.bn = nn.InstanceNorm2d(num_out_layers,
+                                    track_running_stats=False,
+                                    affine=True)
+
+    def forward(self, x):
+        return F.elu(self.bn(self.conv(x)), inplace=True)
+
+
+class upconv(nn.Module):
+    def __init__(self, num_in_layers, num_out_layers, kernel_size, scale):
+        super(upconv, self).__init__()
+        self.scale = scale
+        self.conv = conv(num_in_layers, num_out_layers, kernel_size, 1)
+
+    def forward(self, x):
+        x = nn.functional.interpolate(x,
+                                      scale_factor=self.scale,
+                                      align_corners=True,
+                                      mode='bilinear')
+        return self.conv(x)
+
diff --git a/nsr/losses/id_loss.py b/nsr/losses/id_loss.py
new file mode 100755
index 0000000000000000000000000000000000000000..8b0cc5f3421a6ff74048963d9734eae8f6226d7e
--- /dev/null
+++ b/nsr/losses/id_loss.py
@@ -0,0 +1,63 @@
+import torch
+from pdb import set_trace as st
+from torch import nn
+from .model_irse import Backbone
+from .paths_config import model_paths
+
+
+class IDLoss(nn.Module):
+
+    def __init__(self, device):
+        # super(IDLoss, self).__init__()
+        super().__init__()
+        print('Loading ResNet ArcFace')
+        self.facenet = Backbone(input_size=112,
+                                num_layers=50,
+                                drop_ratio=0.6,
+                                mode='ir_se').to(device)
+        # self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
+        try:
+            face_net_model = torch.load(model_paths['ir_se50'],
+                                        map_location=device)
+        except Exception as e:
+            face_net_model = torch.load(model_paths['ir_se50_hwc'],
+                                        map_location=device)
+
+        self.facenet.load_state_dict(face_net_model)
+
+        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
+        self.facenet.eval()
+
+    def extract_feats(self, x):
+        x = x[:, :, 35:223, 32:220]  # Crop interesting region
+        x = self.face_pool(x)
+        x_feats = self.facenet(x)
+        return x_feats
+
+    def forward(self, y_hat, y, x):
+        n_samples, _, H, W = x.shape
+        assert H == W == 256, 'idloss needs 256*256 input images'
+
+        x_feats = self.extract_feats(x)
+        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
+        y_hat_feats = self.extract_feats(y_hat)
+        y_feats = y_feats.detach()
+        loss = 0
+        sim_improvement = 0
+        id_logs = []
+        count = 0
+        for i in range(n_samples):
+            diff_target = y_hat_feats[i].dot(y_feats[i])
+            diff_input = y_hat_feats[i].dot(x_feats[i])
+            diff_views = y_feats[i].dot(x_feats[i])
+            id_logs.append({
+                'diff_target': float(diff_target),
+                'diff_input': float(diff_input),
+                'diff_views': float(diff_views)
+            })
+            loss += 1 - diff_target
+            id_diff = float(diff_target) - float(diff_views)
+            sim_improvement += id_diff
+            count += 1
+
+        return loss / count, sim_improvement / count, id_logs
diff --git a/nsr/losses/lms.py b/nsr/losses/lms.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8e71f89d6e429f4e81f11675042beb09c4009ff
--- /dev/null
+++ b/nsr/losses/lms.py
@@ -0,0 +1,94 @@
+# ------------------------------------------------------------------------------
+# https://github.dev/HRNet/HigherHRNet-Human-Pose-Estimation
+# Copyright (c) Microsoft
+# Licensed under the MIT License.
+# Written by Bin Xiao (leoxiaobin@gmail.com)
+# Modified by Bowen Cheng (bcheng9@illinois.edu)
+# ------------------------------------------------------------------------------
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import logging
+
+import torch
+import torch.nn as nn
+from pdb import set_trace as st
+
+logger = logging.getLogger(__name__)
+
+
+class HeatmapGenerator():
+    def __init__(self, heatmap_size, num_joints=68, sigma=2):
+        self.heatmap_size = heatmap_size
+        # self.image_size = image_size
+        self.num_joints = num_joints
+        if sigma < 0:
+            sigma = self.heatmap_size / 64
+        self.sigma = sigma
+        size = 6 * sigma + 3
+        x = np.arange(0, size, 1, float)
+        y = x[:, np.newaxis]
+        x0, y0 = 3 * sigma + 1, 3 * sigma + 1
+        self.g = np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2))
+
+    # def __call__(self, joints, image_size: np.ndarray):
+    def __call__(self, joints, image_size: int):
+        """generate heatmap gt from joints
+
+        Args:
+            joints (np.ndarray): N,3
+
+        Returns:
+            hms: N,H,W
+        """
+        hms = np.zeros((self.num_joints, self.heatmap_size, self.heatmap_size),
+                       dtype=np.float32)
+        sigma = self.sigma
+
+        # feat_stride = image_size / [self.heatmap_size, self.heatmap_size]
+        feat_stride = image_size / self.heatmap_size
+        for idx, pt in enumerate(joints):
+            # for idx, pt in enumerate(p):
+            if pt[2] > 0:
+                # x = int(pt[0] / feat_stride[0] + 0.5)
+                # y = int(pt[1] / feat_stride[1] + 0.5) # normalize joints to heatmap size
+                x = int(pt[0] / feat_stride + 0.5)
+                y = int(pt[1] / feat_stride +
+                        0.5)  # normalize joints to heatmap size
+                if x < 0 or y < 0 or \
+                    x >= self.heatmap_size or y >= self.heatmap_size:
+                    continue
+
+                ul = int(np.round(x - 3 * sigma - 1)), int(
+                    np.round(y - 3 * sigma - 1))
+                br = int(np.round(x + 3 * sigma + 2)), int(
+                    np.round(y + 3 * sigma + 2))
+
+                c, d = max(0, -ul[0]), min(br[0], self.heatmap_size) - ul[0]
+                a, b = max(0, -ul[1]), min(br[1], self.heatmap_size) - ul[1]
+
+                cc, dd = max(0, ul[0]), min(br[0], self.heatmap_size)
+                aa, bb = max(0, ul[1]), min(br[1], self.heatmap_size)
+                hms[idx, aa:bb, cc:dd] = np.maximum(hms[idx, aa:bb, cc:dd],
+                                                    self.g[a:b, c:d])
+        return hms
+
+
+class HeatmapLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, pred, gt, mask=None):
+        # todo, add mask
+        assert pred.size() == gt.size()
+        loss = ((pred - gt)**2)
+        if mask is not None:
+            loss = loss * mask[:, None, :, :].expand_as(pred)
+        # loss = loss.mean(dim=3).mean(dim=2).mean(dim=1)
+        loss = loss.mean()
+        # loss = loss.mean(dim=3).mean(dim=2).sum(dim=1)
+        return loss
diff --git a/nsr/losses/model_irse.py b/nsr/losses/model_irse.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c5a0f6d927eb8523a6df7589bd5e05c936b9669
--- /dev/null
+++ b/nsr/losses/model_irse.py
@@ -0,0 +1,110 @@
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
+from .helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
+"""
+Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Backbone(Module):
+    def __init__(self,
+                 input_size,
+                 num_layers,
+                 mode='ir',
+                 drop_ratio=0.4,
+                 affine=True):
+        super(Backbone, self).__init__()
+        assert input_size in [112, 224], "input_size should be 112 or 224"
+        assert num_layers in [50, 100,
+                              152], "num_layers should be 50, 100 or 152"
+        assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+        blocks = get_blocks(num_layers)
+        if mode == 'ir':
+            unit_module = bottleneck_IR
+        elif mode == 'ir_se':
+            unit_module = bottleneck_IR_SE
+        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+                                      BatchNorm2d(64), PReLU(64))
+        if input_size == 112:
+            self.output_layer = Sequential(BatchNorm2d(512),
+                                           Dropout(drop_ratio), Flatten(),
+                                           Linear(512 * 7 * 7, 512),
+                                           BatchNorm1d(512, affine=affine))
+        else:
+            self.output_layer = Sequential(BatchNorm2d(512),
+                                           Dropout(drop_ratio), Flatten(),
+                                           Linear(512 * 14 * 14, 512),
+                                           BatchNorm1d(512, affine=affine))
+
+        modules = []
+        for block in blocks:
+            for bottleneck in block:
+                modules.append(
+                    unit_module(bottleneck.in_channel, bottleneck.depth,
+                                bottleneck.stride))
+        self.body = Sequential(*modules)
+
+    def forward(self, x):
+        x = self.input_layer(x)
+        x = self.body(x)
+        x = self.output_layer(x)
+        return l2_norm(x)
+
+
+def IR_50(input_size):
+    """Constructs a ir-50 model."""
+    model = Backbone(input_size,
+                     num_layers=50,
+                     mode='ir',
+                     drop_ratio=0.4,
+                     affine=False)
+    return model
+
+
+def IR_101(input_size):
+    """Constructs a ir-101 model."""
+    model = Backbone(input_size,
+                     num_layers=100,
+                     mode='ir',
+                     drop_ratio=0.4,
+                     affine=False)
+    return model
+
+
+def IR_152(input_size):
+    """Constructs a ir-152 model."""
+    model = Backbone(input_size,
+                     num_layers=152,
+                     mode='ir',
+                     drop_ratio=0.4,
+                     affine=False)
+    return model
+
+
+def IR_SE_50(input_size):
+    """Constructs a ir_se-50 model."""
+    model = Backbone(input_size,
+                     num_layers=50,
+                     mode='ir_se',
+                     drop_ratio=0.4,
+                     affine=False)
+    return model
+
+
+def IR_SE_101(input_size):
+    """Constructs a ir_se-101 model."""
+    model = Backbone(input_size,
+                     num_layers=100,
+                     mode='ir_se',
+                     drop_ratio=0.4,
+                     affine=False)
+    return model
+
+
+def IR_SE_152(input_size):
+    """Constructs a ir_se-152 model."""
+    model = Backbone(input_size,
+                     num_layers=152,
+                     mode='ir_se',
+                     drop_ratio=0.4,
+                     affine=False)
+    return model
diff --git a/nsr/losses/paths_config.py b/nsr/losses/paths_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..efd4e8094ee6bbf1acb0c82c22528c31e4b5480e
--- /dev/null
+++ b/nsr/losses/paths_config.py
@@ -0,0 +1,24 @@
+model_paths = {
+    'ir_se50': 'pretrained_models/model_ir_se50.pth',
+    'resnet34': 'pretrained_models/resnet34-333f7ec4.pth',
+    'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
+    'stylegan_cars': 'pretrained_models/stylegan2-car-config-f.pt',
+    'stylegan_church': 'pretrained_models/stylegan2-church-config-f.pt',
+    'stylegan_horse': 'pretrained_models/stylegan2-horse-config-f.pt',
+    'stylegan_ada_wild': 'pretrained_models/afhqwild.pt',
+    'stylegan_toonify': 'pretrained_models/ffhq_cartoon_blended.pt',
+    'shape_predictor':
+    'pretrained_models/shape_predictor_68_face_landmarks.dat',
+    'circular_face': 'pretrained_models/CurricularFace_Backbone.pth',
+    'mtcnn_pnet': 'pretrained_models/mtcnn/pnet.npy',
+    'mtcnn_rnet': 'pretrained_models/mtcnn/rnet.npy',
+    'mtcnn_onet': 'pretrained_models/mtcnn/onet.npy',
+    'moco': 'pretrained_models/moco_v2_800ep_pretrain.pt'
+}
+
+project_basedir = '/mnt/lustre/yslan/Repo/Research/SIGA22/BaseModels/StyleSDF'
+
+for k, v in model_paths.items():
+    model_paths[k] = f'{project_basedir}/project/utils/misc/' + model_paths[k]
+
+model_paths['ir_se50_hwc'] = '/home/yslan/datasets/model_ir_se50.pth'
diff --git a/nsr/losses/sdfstudio_losses.py b/nsr/losses/sdfstudio_losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e560b07c8baec71febb8b4235d09e2cb68b2d0c
--- /dev/null
+++ b/nsr/losses/sdfstudio_losses.py
@@ -0,0 +1,771 @@
+# Copyright 2022 The Nerfstudio Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Collection of Losses.
+"""
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torchtyping import TensorType
+from torch.autograd import Variable
+import numpy as np
+from math import exp
+
+# from nerfstudio.cameras.rays import RaySamples
+# from nerfstudio.field_components.field_heads import FieldHeadNames
+
+L1Loss = nn.L1Loss
+MSELoss = nn.MSELoss
+
+LOSSES = {"L1": L1Loss, "MSE": MSELoss}
+
+EPS = 1.0e-7
+
+
+def outer(
+    t0_starts: TensorType[..., "num_samples_0"],
+    t0_ends: TensorType[..., "num_samples_0"],
+    t1_starts: TensorType[..., "num_samples_1"],
+    t1_ends: TensorType[..., "num_samples_1"],
+    y1: TensorType[..., "num_samples_1"],
+) -> TensorType[..., "num_samples_0"]:
+    """Faster version of
+
+    https://github.com/kakaobrain/NeRF-Factory/blob/f61bb8744a5cb4820a4d968fb3bfbed777550f4a/src/model/mipnerf360/helper.py#L117
+    https://github.com/google-research/multinerf/blob/b02228160d3179300c7d499dca28cb9ca3677f32/internal/stepfun.py#L64
+
+    Args:
+        t0_starts: start of the interval edges
+        t0_ends: end of the interval edges
+        t1_starts: start of the interval edges
+        t1_ends: end of the interval edges
+        y1: weights
+    """
+    cy1 = torch.cat([torch.zeros_like(y1[..., :1]), torch.cumsum(y1, dim=-1)], dim=-1)
+
+    idx_lo = torch.searchsorted(t1_starts.contiguous(), t0_starts.contiguous(), side="right") - 1
+    idx_lo = torch.clamp(idx_lo, min=0, max=y1.shape[-1] - 1)
+    idx_hi = torch.searchsorted(t1_ends.contiguous(), t0_ends.contiguous(), side="right")
+    idx_hi = torch.clamp(idx_hi, min=0, max=y1.shape[-1] - 1)
+    cy1_lo = torch.take_along_dim(cy1[..., :-1], idx_lo, dim=-1)
+    cy1_hi = torch.take_along_dim(cy1[..., 1:], idx_hi, dim=-1)
+    y0_outer = cy1_hi - cy1_lo
+
+    return y0_outer
+
+
+def lossfun_outer(
+    t: TensorType[..., "num_samples+1"],
+    w: TensorType[..., "num_samples"],
+    t_env: TensorType[..., "num_samples+1"],
+    w_env: TensorType[..., "num_samples"],
+):
+    """
+    https://github.com/kakaobrain/NeRF-Factory/blob/f61bb8744a5cb4820a4d968fb3bfbed777550f4a/src/model/mipnerf360/helper.py#L136
+    https://github.com/google-research/multinerf/blob/b02228160d3179300c7d499dca28cb9ca3677f32/internal/stepfun.py#L80
+
+    Args:
+        t: interval edges
+        w: weights
+        t_env: interval edges of the upper bound enveloping historgram
+        w_env: weights that should upper bound the inner (t,w) histogram
+    """
+    w_outer = outer(t[..., :-1], t[..., 1:], t_env[..., :-1], t_env[..., 1:], w_env)
+    return torch.clip(w - w_outer, min=0) ** 2 / (w + EPS)
+
+
+def ray_samples_to_sdist(ray_samples):
+    """Convert ray samples to s space"""
+    starts = ray_samples.spacing_starts
+    ends = ray_samples.spacing_ends
+    sdist = torch.cat([starts[..., 0], ends[..., -1:, 0]], dim=-1)  # (num_rays, num_samples + 1)
+    return sdist
+
+
+def interlevel_loss(weights_list, ray_samples_list):
+    """Calculates the proposal loss in the MipNeRF-360 paper.
+
+    https://github.com/kakaobrain/NeRF-Factory/blob/f61bb8744a5cb4820a4d968fb3bfbed777550f4a/src/model/mipnerf360/model.py#L515
+    https://github.com/google-research/multinerf/blob/b02228160d3179300c7d499dca28cb9ca3677f32/internal/train_utils.py#L133
+    """
+    c = ray_samples_to_sdist(ray_samples_list[-1]).detach()
+    w = weights_list[-1][..., 0].detach()
+    loss_interlevel = 0.0
+    for ray_samples, weights in zip(ray_samples_list[:-1], weights_list[:-1]):
+        sdist = ray_samples_to_sdist(ray_samples)
+        cp = sdist  # (num_rays, num_samples + 1)
+        wp = weights[..., 0]  # (num_rays, num_samples)
+        loss_interlevel += torch.mean(lossfun_outer(c, w, cp, wp))
+    return loss_interlevel
+
+
+## zip-NeRF losses
+def blur_stepfun(x, y, r):
+    x_c = torch.cat([x - r, x + r], dim=-1)
+    x_r, x_idx = torch.sort(x_c, dim=-1)
+    zeros = torch.zeros_like(y[:, :1])
+    y_1 = (torch.cat([y, zeros], dim=-1) - torch.cat([zeros, y], dim=-1)) / (2 * r)
+    x_idx = x_idx[:, :-1]
+    y_2 = torch.cat([y_1, -y_1], dim=-1)[
+        torch.arange(x_idx.shape[0]).reshape(-1, 1).expand(x_idx.shape).to(x_idx.device), x_idx
+    ]
+
+    y_r = torch.cumsum((x_r[:, 1:] - x_r[:, :-1]) * torch.cumsum(y_2, dim=-1), dim=-1)
+    y_r = torch.cat([zeros, y_r], dim=-1)
+    return x_r, y_r
+
+
+def interlevel_loss_zip(weights_list, ray_samples_list):
+    """Calculates the proposal loss in the Zip-NeRF paper."""
+    c = ray_samples_to_sdist(ray_samples_list[-1]).detach()
+    w = weights_list[-1][..., 0].detach()
+
+    # 1. normalize
+    w_normalize = w / (c[:, 1:] - c[:, :-1])
+
+    loss_interlevel = 0.0
+    for ray_samples, weights, r in zip(ray_samples_list[:-1], weights_list[:-1], [0.03, 0.003]):
+        # 2. step blur with different r
+        x_r, y_r = blur_stepfun(c, w_normalize, r)
+        y_r = torch.clip(y_r, min=0)
+        assert (y_r >= 0.0).all()
+
+        # 3. accumulate
+        y_cum = torch.cumsum((y_r[:, 1:] + y_r[:, :-1]) * 0.5 * (x_r[:, 1:] - x_r[:, :-1]), dim=-1)
+        y_cum = torch.cat([torch.zeros_like(y_cum[:, :1]), y_cum], dim=-1)
+
+        # 4 loss
+        sdist = ray_samples_to_sdist(ray_samples)
+        cp = sdist  # (num_rays, num_samples + 1)
+        wp = weights[..., 0]  # (num_rays, num_samples)
+
+        # resample
+        inds = torch.searchsorted(x_r, cp, side="right")
+        below = torch.clamp(inds - 1, 0, x_r.shape[-1] - 1)
+        above = torch.clamp(inds, 0, x_r.shape[-1] - 1)
+        cdf_g0 = torch.gather(x_r, -1, below)
+        bins_g0 = torch.gather(y_cum, -1, below)
+        cdf_g1 = torch.gather(x_r, -1, above)
+        bins_g1 = torch.gather(y_cum, -1, above)
+
+        t = torch.clip(torch.nan_to_num((cp - cdf_g0) / (cdf_g1 - cdf_g0), 0), 0, 1)
+        bins = bins_g0 + t * (bins_g1 - bins_g0)
+
+        w_gt = bins[:, 1:] - bins[:, :-1]
+
+        # TODO here might be unstable when wp is very small
+        loss_interlevel += torch.mean(torch.clip(w_gt - wp, min=0) ** 2 / (wp + 1e-5))
+
+    return loss_interlevel
+
+
+# Verified
+def lossfun_distortion(t, w):
+    """
+    https://github.com/kakaobrain/NeRF-Factory/blob/f61bb8744a5cb4820a4d968fb3bfbed777550f4a/src/model/mipnerf360/helper.py#L142
+    https://github.com/google-research/multinerf/blob/b02228160d3179300c7d499dca28cb9ca3677f32/internal/stepfun.py#L266
+    """
+    ut = (t[..., 1:] + t[..., :-1]) / 2
+    dut = torch.abs(ut[..., :, None] - ut[..., None, :])
+    loss_inter = torch.sum(w * torch.sum(w[..., None, :] * dut, dim=-1), dim=-1)
+
+    loss_intra = torch.sum(w**2 * (t[..., 1:] - t[..., :-1]), dim=-1) / 3
+
+    return loss_inter + loss_intra
+
+
+def distortion_loss(weights_list, ray_samples_list):
+    """From mipnerf360"""
+    c = ray_samples_to_sdist(ray_samples_list[-1])
+    w = weights_list[-1][..., 0]
+    loss = torch.mean(lossfun_distortion(c, w))
+    return loss
+
+
+# def nerfstudio_distortion_loss(
+#     ray_samples: RaySamples,
+#     densities: TensorType["bs":..., "num_samples", 1] = None,
+#     weights: TensorType["bs":..., "num_samples", 1] = None,
+# ) -> TensorType["bs":..., 1]:
+#     """Ray based distortion loss proposed in MipNeRF-360. Returns distortion Loss.
+
+#     .. math::
+
+#         \\mathcal{L}(\\mathbf{s}, \\mathbf{w}) =\\iint\\limits_{-\\infty}^{\\,\\,\\,\\infty}
+#         \\mathbf{w}_\\mathbf{s}(u)\\mathbf{w}_\\mathbf{s}(v)|u - v|\\,d_{u}\\,d_{v}
+
+#     where :math:`\\mathbf{w}_\\mathbf{s}(u)=\\sum_i w_i \\mathbb{1}_{[\\mathbf{s}_i, \\mathbf{s}_{i+1})}(u)`
+#     is the weight at location :math:`u` between bin locations :math:`s_i` and :math:`s_{i+1}`.
+
+#     Args:
+#         ray_samples: Ray samples to compute loss over
+#         densities: Predicted sample densities
+#         weights: Predicted weights from densities and sample locations
+#     """
+#     if torch.is_tensor(densities):
+#         assert not torch.is_tensor(weights), "Cannot use both densities and weights"
+#         # Compute the weight at each sample location
+#         weights = ray_samples.get_weights(densities)
+#     if torch.is_tensor(weights):
+#         assert not torch.is_tensor(densities), "Cannot use both densities and weights"
+
+#     starts = ray_samples.spacing_starts
+#     ends = ray_samples.spacing_ends
+
+#     assert starts is not None and ends is not None, "Ray samples must have spacing starts and ends"
+#     midpoints = (starts + ends) / 2.0  # (..., num_samples, 1)
+
+#     loss = (
+#         weights * weights[..., None, :, 0] * torch.abs(midpoints - midpoints[..., None, :, 0])
+#     )  # (..., num_samples, num_samples)
+#     loss = torch.sum(loss, dim=(-1, -2))[..., None]  # (..., num_samples)
+#     loss = loss + 1 / 3.0 * torch.sum(weights**2 * (ends - starts), dim=-2)
+
+#     return loss
+
+
+def orientation_loss(
+    weights: TensorType["bs":..., "num_samples", 1],
+    normals: TensorType["bs":..., "num_samples", 3],
+    viewdirs: TensorType["bs":..., 3],
+):
+    """Orientation loss proposed in Ref-NeRF.
+    Loss that encourages that all visible normals are facing towards the camera.
+    """
+    w = weights
+    n = normals
+    v = viewdirs
+    n_dot_v = (n * v[..., None, :]).sum(axis=-1)
+    return (w[..., 0] * torch.fmin(torch.zeros_like(n_dot_v), n_dot_v) ** 2).sum(dim=-1)
+
+
+def pred_normal_loss(
+    weights: TensorType["bs":..., "num_samples", 1],
+    normals: TensorType["bs":..., "num_samples", 3],
+    pred_normals: TensorType["bs":..., "num_samples", 3],
+):
+    """Loss between normals calculated from density and normals from prediction network."""
+    return (weights[..., 0] * (1.0 - torch.sum(normals * pred_normals, dim=-1))).sum(dim=-1)
+
+
+def monosdf_normal_loss(normal_pred: torch.Tensor, normal_gt: torch.Tensor):
+    """normal consistency loss as monosdf
+
+    Args:
+        normal_pred (torch.Tensor): volume rendered normal
+        normal_gt (torch.Tensor): monocular normal
+    """
+    normal_gt = torch.nn.functional.normalize(normal_gt, p=2, dim=-1)
+    normal_pred = torch.nn.functional.normalize(normal_pred, p=2, dim=-1)
+    l1 = torch.abs(normal_pred - normal_gt).sum(dim=-1).mean()
+    cos = (1.0 - torch.sum(normal_pred * normal_gt, dim=-1)).mean()
+    return l1 + cos
+
+
+# copy from MiDaS
+def compute_scale_and_shift(prediction, target, mask):
+    # system matrix: A = [[a_00, a_01], [a_10, a_11]]
+    a_00 = torch.sum(mask * prediction * prediction, (1, 2))
+    a_01 = torch.sum(mask * prediction, (1, 2))
+    a_11 = torch.sum(mask, (1, 2))
+
+    # right hand side: b = [b_0, b_1]
+    b_0 = torch.sum(mask * prediction * target, (1, 2))
+    b_1 = torch.sum(mask * target, (1, 2))
+
+    # solution: x = A^-1 . b = [[a_11, -a_01], [-a_10, a_00]] / (a_00 * a_11 - a_01 * a_10) . b
+    x_0 = torch.zeros_like(b_0)
+    x_1 = torch.zeros_like(b_1)
+
+    det = a_00 * a_11 - a_01 * a_01
+    valid = det.nonzero()
+
+    x_0[valid] = (a_11[valid] * b_0[valid] - a_01[valid] * b_1[valid]) / det[valid]
+    x_1[valid] = (-a_01[valid] * b_0[valid] + a_00[valid] * b_1[valid]) / det[valid]
+
+    return x_0, x_1
+
+
+def reduction_batch_based(image_loss, M):
+    # average of all valid pixels of the batch
+
+    # avoid division by 0 (if sum(M) = sum(sum(mask)) = 0: sum(image_loss) = 0)
+    divisor = torch.sum(M)
+
+    if divisor == 0:
+        return 0
+    else:
+        return torch.sum(image_loss) / divisor
+
+
+def reduction_image_based(image_loss, M):
+    # mean of average of valid pixels of an image
+
+    # avoid division by 0 (if M = sum(mask) = 0: image_loss = 0)
+    valid = M.nonzero()
+
+    image_loss[valid] = image_loss[valid] / M[valid]
+
+    return torch.mean(image_loss)
+
+
+def mse_loss(prediction, target, mask, reduction=reduction_batch_based):
+    M = torch.sum(mask, (1, 2))
+    res = prediction - target
+    image_loss = torch.sum(mask * res * res, (1, 2))
+
+    return reduction(image_loss, 2 * M)
+
+
+def gradient_loss(prediction, target, mask, reduction=reduction_batch_based):
+    M = torch.sum(mask, (1, 2))
+
+    diff = prediction - target
+    diff = torch.mul(mask, diff)
+
+    grad_x = torch.abs(diff[:, :, 1:] - diff[:, :, :-1])
+    mask_x = torch.mul(mask[:, :, 1:], mask[:, :, :-1])
+    grad_x = torch.mul(mask_x, grad_x)
+
+    grad_y = torch.abs(diff[:, 1:, :] - diff[:, :-1, :])
+    mask_y = torch.mul(mask[:, 1:, :], mask[:, :-1, :])
+    grad_y = torch.mul(mask_y, grad_y)
+
+    image_loss = torch.sum(grad_x, (1, 2)) + torch.sum(grad_y, (1, 2))
+
+    return reduction(image_loss, M)
+
+
+class MiDaSMSELoss(nn.Module):
+    def __init__(self, reduction="batch-based"):
+        super().__init__()
+
+        if reduction == "batch-based":
+            self.__reduction = reduction_batch_based
+        else:
+            self.__reduction = reduction_image_based
+
+    def forward(self, prediction, target, mask):
+        return mse_loss(prediction, target, mask, reduction=self.__reduction)
+
+
+class GradientLoss(nn.Module):
+    def __init__(self, scales=4, reduction="batch-based"):
+        super().__init__()
+
+        if reduction == "batch-based":
+            self.__reduction = reduction_batch_based
+        else:
+            self.__reduction = reduction_image_based
+
+        self.__scales = scales
+
+    def forward(self, prediction, target, mask):
+        total = 0
+
+        for scale in range(self.__scales):
+            step = pow(2, scale)
+
+            total += gradient_loss(
+                prediction[:, ::step, ::step],
+                target[:, ::step, ::step],
+                mask[:, ::step, ::step],
+                reduction=self.__reduction,
+            )
+
+        return total
+
+
+class ScaleAndShiftInvariantLoss(nn.Module):
+    def __init__(self, alpha=0.5, scales=4, reduction="batch-based"):
+        super().__init__()
+
+        self.__data_loss = MiDaSMSELoss(reduction=reduction)
+        self.__regularization_loss = GradientLoss(scales=scales, reduction=reduction)
+        self.__alpha = alpha
+
+        self.__prediction_ssi = None
+
+    def forward(self, prediction, target, mask):
+        scale, shift = compute_scale_and_shift(prediction, target, mask)
+        self.__prediction_ssi = scale.view(-1, 1, 1) * prediction + shift.view(-1, 1, 1)
+
+        total = self.__data_loss(self.__prediction_ssi, target, mask)
+        if self.__alpha > 0:
+            total += self.__alpha * self.__regularization_loss(self.__prediction_ssi, target, mask)
+
+        return total
+
+    def __get_prediction_ssi(self):
+        return self.__prediction_ssi
+
+    prediction_ssi = property(__get_prediction_ssi)
+
+
+# end copy
+
+
+# copy from https://github.com/svip-lab/Indoor-SfMLearner/blob/0d682b7ce292484e5e3e2161fc9fc07e2f5ca8d1/layers.py#L218
+class SSIM(nn.Module):
+    """Layer to compute the SSIM loss between a pair of images"""
+
+    def __init__(self, patch_size):
+        super(SSIM, self).__init__()
+        self.mu_x_pool = nn.AvgPool2d(patch_size, 1)
+        self.mu_y_pool = nn.AvgPool2d(patch_size, 1)
+        self.sig_x_pool = nn.AvgPool2d(patch_size, 1)
+        self.sig_y_pool = nn.AvgPool2d(patch_size, 1)
+        self.sig_xy_pool = nn.AvgPool2d(patch_size, 1)
+
+        self.refl = nn.ReflectionPad2d(patch_size // 2)
+
+        self.C1 = 0.01**2
+        self.C2 = 0.03**2
+
+    def forward(self, x, y):
+        x = self.refl(x)
+        y = self.refl(y)
+
+        mu_x = self.mu_x_pool(x)
+        mu_y = self.mu_y_pool(y)
+
+        sigma_x = self.sig_x_pool(x**2) - mu_x**2
+        sigma_y = self.sig_y_pool(y**2) - mu_y**2
+        sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y
+
+        SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)
+        SSIM_d = (mu_x**2 + mu_y**2 + self.C1) * (sigma_x + sigma_y + self.C2)
+
+        return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1)
+
+
+# TODO test different losses
+class NCC(nn.Module):
+    """Layer to compute the normalization cross correlation (NCC) of patches"""
+
+    def __init__(self, patch_size: int = 11, min_patch_variance: float = 0.01):
+        super(NCC, self).__init__()
+        self.patch_size = patch_size
+        self.min_patch_variance = min_patch_variance
+
+    def forward(self, x, y):
+        # TODO if we use gray image we should do it right after loading the image to save computations
+        # to gray image
+        x = torch.mean(x, dim=1)
+        y = torch.mean(y, dim=1)
+
+        x_mean = torch.mean(x, dim=(1, 2), keepdim=True)
+        y_mean = torch.mean(y, dim=(1, 2), keepdim=True)
+
+        x_normalized = x - x_mean
+        y_normalized = y - y_mean
+
+        norm = torch.sum(x_normalized * y_normalized, dim=(1, 2))
+        var = torch.square(x_normalized).sum(dim=(1, 2)) * torch.square(y_normalized).sum(dim=(1, 2))
+        denom = torch.sqrt(var + 1e-6)
+
+        ncc = norm / (denom + 1e-6)
+
+        # ignore pathces with low variances
+        not_valid = (torch.square(x_normalized).sum(dim=(1, 2)) < self.min_patch_variance) | (
+            torch.square(y_normalized).sum(dim=(1, 2)) < self.min_patch_variance
+        )
+        ncc[not_valid] = 1.0
+
+        score = 1 - ncc.clip(-1.0, 1.0)  # 0->2: smaller, better
+        return score[:, None, None, None]
+
+
+class MultiViewLoss(nn.Module):
+    """compute multi-view consistency loss"""
+
+    def __init__(self, patch_size: int = 11, topk: int = 4, min_patch_variance: float = 0.01):
+        super(MultiViewLoss, self).__init__()
+        self.patch_size = patch_size
+        self.topk = topk
+        self.min_patch_variance = min_patch_variance
+        # TODO make metric configurable
+        # self.ssim = SSIM(patch_size=patch_size)
+        # self.ncc = NCC(patch_size=patch_size)
+        self.ssim = NCC(patch_size=patch_size, min_patch_variance=min_patch_variance)
+
+        self.iter = 0
+
+    def forward(self, patches: torch.Tensor, valid: torch.Tensor):
+        """take the mim
+
+        Args:
+            patches (torch.Tensor): _description_
+            valid (torch.Tensor): _description_
+
+        Returns:
+            _type_: _description_
+        """
+        num_imgs, num_rays, _, num_channels = patches.shape
+
+        if num_rays <= 0:
+            return torch.tensor(0.0).to(patches.device)
+
+        ref_patches = (
+            patches[:1, ...]
+            .reshape(1, num_rays, self.patch_size, self.patch_size, num_channels)
+            .expand(num_imgs - 1, num_rays, self.patch_size, self.patch_size, num_channels)
+            .reshape(-1, self.patch_size, self.patch_size, num_channels)
+            .permute(0, 3, 1, 2)
+        )  # [N_src*N_rays, 3, patch_size, patch_size]
+        src_patches = (
+            patches[1:, ...]
+            .reshape(num_imgs - 1, num_rays, self.patch_size, self.patch_size, num_channels)
+            .reshape(-1, self.patch_size, self.patch_size, num_channels)
+            .permute(0, 3, 1, 2)
+        )  # [N_src*N_rays, 3, patch_size, patch_size]
+
+        # apply same reshape to the valid mask
+        src_patches_valid = (
+            valid[1:, ...]
+            .reshape(num_imgs - 1, num_rays, self.patch_size, self.patch_size, 1)
+            .reshape(-1, self.patch_size, self.patch_size, 1)
+            .permute(0, 3, 1, 2)
+        )  # [N_src*N_rays, 1, patch_size, patch_size]
+
+        ssim = self.ssim(ref_patches.detach(), src_patches)
+        ssim = torch.mean(ssim, dim=(1, 2, 3))
+        ssim = ssim.reshape(num_imgs - 1, num_rays)
+
+        # ignore invalid patch by setting ssim error to very large value
+        ssim_valid = (
+            src_patches_valid.reshape(-1, self.patch_size * self.patch_size).all(dim=-1).reshape(num_imgs - 1, num_rays)
+        )
+        # we should mask the error after we select the topk value, otherwise we might select far way patches that happens to be inside the image
+        # ssim[torch.logical_not(ssim_valid)] = 1.1  # max ssim_error is 1
+
+        min_ssim, idx = torch.topk(ssim, k=self.topk, largest=False, dim=0, sorted=True)
+
+        min_ssim_valid = ssim_valid[idx, torch.arange(num_rays)[None].expand_as(idx)]
+        # TODO how to set this value for better visualization
+        min_ssim[torch.logical_not(min_ssim_valid)] = 0.0  # max ssim_error is 1
+
+        if False:
+            # visualization of topK error computations
+
+            import cv2
+            import numpy as np
+
+            vis_patch_num = num_rays
+            K = min(100, vis_patch_num)
+
+            image = (
+                patches[:, :vis_patch_num, :, :]
+                .reshape(-1, vis_patch_num, self.patch_size, self.patch_size, 3)
+                .permute(1, 2, 0, 3, 4)
+                .reshape(vis_patch_num * self.patch_size, -1, 3)
+            )
+
+            src_patches_reshaped = src_patches.reshape(
+                num_imgs - 1, num_rays, 3, self.patch_size, self.patch_size
+            ).permute(1, 0, 3, 4, 2)
+            idx = idx.permute(1, 0)
+
+            selected_patch = (
+                src_patches_reshaped[torch.arange(num_rays)[:, None].expand(idx.shape), idx]
+                .permute(0, 2, 1, 3, 4)
+                .reshape(num_rays, self.patch_size, self.topk * self.patch_size, 3)[:vis_patch_num]
+                .reshape(-1, self.topk * self.patch_size, 3)
+            )
+
+            # apply same reshape to the valid mask
+            src_patches_valid_reshaped = src_patches_valid.reshape(
+                num_imgs - 1, num_rays, 1, self.patch_size, self.patch_size
+            ).permute(1, 0, 3, 4, 2)
+
+            selected_patch_valid = (
+                src_patches_valid_reshaped[torch.arange(num_rays)[:, None].expand(idx.shape), idx]
+                .permute(0, 2, 1, 3, 4)
+                .reshape(num_rays, self.patch_size, self.topk * self.patch_size, 1)[:vis_patch_num]
+                .reshape(-1, self.topk * self.patch_size, 1)
+            )
+            # valid to image
+            selected_patch_valid = selected_patch_valid.expand_as(selected_patch).float()
+            # breakpoint()
+
+            image = torch.cat([selected_patch_valid, selected_patch, image], dim=1)
+            # select top rays with highest errors
+
+            image = image.reshape(num_rays, self.patch_size, -1, 3)
+
+            _, idx2 = torch.topk(
+                torch.sum(min_ssim, dim=0) / (min_ssim_valid.float().sum(dim=0) + 1e-6),
+                k=K,
+                largest=True,
+                dim=0,
+                sorted=True,
+            )
+
+            image = image[idx2].reshape(K * self.patch_size, -1, 3)
+
+            cv2.imwrite(f"vis/{self.iter}.png", (image.detach().cpu().numpy() * 255).astype(np.uint8)[..., ::-1])
+            self.iter += 1
+            if self.iter == 9:
+                breakpoint()
+
+        return torch.sum(min_ssim) / (min_ssim_valid.float().sum() + 1e-6)
+
+
+# sensor depth loss, adapted from https://github.com/dazinovic/neural-rgbd-surface-reconstruction/blob/main/losses.py
+# class SensorDepthLoss(nn.Module):
+#     """Sensor Depth loss"""
+
+#     def __init__(self, truncation: float):
+#         super(SensorDepthLoss, self).__init__()
+#         self.truncation = truncation  #  0.05 * 0.3 5cm scaled
+
+#     def forward(self, batch, outputs):
+#         """take the mim
+
+#         Args:
+#             batch (Dict): inputs
+#             outputs (Dict): outputs data from surface model
+
+#         Returns:
+#             l1_loss: l1 loss
+#             freespace_loss: free space loss
+#             sdf_loss: sdf loss
+#         """
+#         depth_pred = outputs["depth"]
+#         depth_gt = batch["sensor_depth"].to(depth_pred.device)[..., None]
+#         valid_gt_mask = depth_gt > 0.0
+
+#         l1_loss = torch.sum(valid_gt_mask * torch.abs(depth_gt - depth_pred)) / (valid_gt_mask.sum() + 1e-6)
+
+#         # free space loss and sdf loss
+#         ray_samples = outputs["ray_samples"]
+#         filed_outputs = outputs["field_outputs"]
+#         pred_sdf = filed_outputs[FieldHeadNames.SDF][..., 0]
+#         directions_norm = outputs["directions_norm"]
+
+#         z_vals = ray_samples.frustums.starts[..., 0] / directions_norm
+
+#         truncation = self.truncation
+#         front_mask = valid_gt_mask & (z_vals < (depth_gt - truncation))
+#         back_mask = valid_gt_mask & (z_vals > (depth_gt + truncation))
+#         sdf_mask = valid_gt_mask & (~front_mask) & (~back_mask)
+
+#         num_fs_samples = front_mask.sum()
+#         num_sdf_samples = sdf_mask.sum()
+#         num_samples = num_fs_samples + num_sdf_samples + 1e-6
+#         fs_weight = 1.0 - num_fs_samples / num_samples
+#         sdf_weight = 1.0 - num_sdf_samples / num_samples
+
+#         free_space_loss = torch.mean((F.relu(truncation - pred_sdf) * front_mask) ** 2) * fs_weight
+
+#         sdf_loss = torch.mean(((z_vals + pred_sdf) - depth_gt) ** 2 * sdf_mask) * sdf_weight
+
+#         return l1_loss, free_space_loss, sdf_loss
+
+r"""Implements Stochastic Structural SIMilarity(S3IM) algorithm.
+It is proposed in the ICCV2023 paper  
+`S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields`.
+
+Arguments:
+    s3im_kernel_size (int): kernel size in ssim's convolution(default: 4)
+    s3im_stride (int): stride in ssim's convolution(default: 4)
+    s3im_repeat_time (int): repeat time in re-shuffle virtual patch(default: 10)
+    s3im_patch_height (height): height of virtual patch(default: 64)
+"""
+
+class S3IM(torch.nn.Module):
+    def __init__(self, s3im_kernel_size = 4, s3im_stride=4, s3im_repeat_time=10, s3im_patch_height=64, size_average = True):
+        super(S3IM, self).__init__()
+        self.s3im_kernel_size = s3im_kernel_size
+        self.s3im_stride = s3im_stride
+        self.s3im_repeat_time = s3im_repeat_time
+        self.s3im_patch_height = s3im_patch_height
+        self.size_average = size_average
+        self.channel = 1
+        self.s3im_kernel = self.create_kernel(s3im_kernel_size, self.channel)
+
+    
+    def gaussian(self, s3im_kernel_size, sigma):
+        gauss = torch.Tensor([exp(-(x - s3im_kernel_size//2)**2/float(2*sigma**2)) for x in range(s3im_kernel_size)])
+        return gauss/gauss.sum()
+
+    def create_kernel(self, s3im_kernel_size, channel):
+        _1D_window = self.gaussian(s3im_kernel_size, 1.5).unsqueeze(1)
+        _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+        s3im_kernel = Variable(_2D_window.expand(channel, 1, s3im_kernel_size, s3im_kernel_size).contiguous())
+        return s3im_kernel
+
+    def _ssim(self, img1, img2, s3im_kernel, s3im_kernel_size, channel, size_average = True, s3im_stride=None):
+        mu1 = F.conv2d(img1, s3im_kernel, padding = (s3im_kernel_size-1)//2, groups = channel, stride=s3im_stride)
+        mu2 = F.conv2d(img2, s3im_kernel, padding = (s3im_kernel_size-1)//2, groups = channel, stride=s3im_stride)
+
+        mu1_sq = mu1.pow(2)
+        mu2_sq = mu2.pow(2)
+        mu1_mu2 = mu1*mu2
+
+        sigma1_sq = F.conv2d(img1*img1, s3im_kernel, padding = (s3im_kernel_size-1)//2, groups = channel, stride=s3im_stride) - mu1_sq
+        sigma2_sq = F.conv2d(img2*img2, s3im_kernel, padding = (s3im_kernel_size-1)//2, groups = channel, stride=s3im_stride) - mu2_sq
+        sigma12 = F.conv2d(img1*img2, s3im_kernel, padding = (s3im_kernel_size-1)//2, groups = channel, stride=s3im_stride) - mu1_mu2
+
+        C1 = 0.01**2
+        C2 = 0.03**2
+
+        ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
+
+        if size_average:
+            return ssim_map.mean()
+        else:
+            return ssim_map.mean(1).mean(1).mean(1)
+    
+    def ssim_loss(self, img1, img2):
+        """
+        img1, img2: torch.Tensor([b,c,h,w])
+        """
+        (_, channel, _, _) = img1.size()
+
+        if channel == self.channel and self.s3im_kernel.data.type() == img1.data.type():
+            s3im_kernel = self.s3im_kernel
+        else:
+            s3im_kernel = self.create_kernel(self.s3im_kernel_size, channel)
+
+            if img1.is_cuda:
+                s3im_kernel = s3im_kernel.cuda(img1.get_device())
+            s3im_kernel = s3im_kernel.type_as(img1)
+
+            self.s3im_kernel = s3im_kernel
+            self.channel = channel
+
+
+        return self._ssim(img1, img2, s3im_kernel, self.s3im_kernel_size, channel, self.size_average, s3im_stride=self.s3im_stride)
+
+    def forward(self, src_vec, tar_vec):
+        loss = 0.0
+        index_list = []
+        for i in range(self.s3im_repeat_time):
+            if i == 0:
+                tmp_index = torch.arange(len(tar_vec))
+                index_list.append(tmp_index)
+            else:
+                ran_idx = torch.randperm(len(tar_vec))
+                index_list.append(ran_idx)
+        res_index = torch.cat(index_list)
+        tar_all = tar_vec[res_index]
+        src_all = src_vec[res_index]
+        tar_patch = tar_all.permute(1, 0).reshape(1, 3, self.s3im_patch_height, -1)
+        src_patch = src_all.permute(1, 0).reshape(1, 3, self.s3im_patch_height, -1)
+        loss = (1 - self.ssim_loss(src_patch, tar_patch))
+        return loss
+
diff --git a/nsr/losses/util.py b/nsr/losses/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..49c76e370bf16888ab61f42844b3c9f14ad9014c
--- /dev/null
+++ b/nsr/losses/util.py
@@ -0,0 +1,128 @@
+import hashlib
+import os
+
+import requests
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+
+URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"}
+
+CKPT_MAP = {"vgg_lpips": "vgg.pth"}
+
+MD5_MAP = {"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"}
+
+
+def download(url, local_path, chunk_size=1024):
+    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
+    with requests.get(url, stream=True) as r:
+        total_size = int(r.headers.get("content-length", 0))
+        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
+            with open(local_path, "wb") as f:
+                for data in r.iter_content(chunk_size=chunk_size):
+                    if data:
+                        f.write(data)
+                        pbar.update(chunk_size)
+
+
+def md5_hash(path):
+    with open(path, "rb") as f:
+        content = f.read()
+    return hashlib.md5(content).hexdigest()
+
+
+def get_ckpt_path(name, root, check=False):
+    assert name in URL_MAP
+    path = os.path.join(root, CKPT_MAP[name])
+    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
+        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
+        download(URL_MAP[name], path)
+        md5 = md5_hash(path)
+        assert md5 == MD5_MAP[name], md5
+    return path
+
+
+class ActNorm(nn.Module):
+    def __init__(
+        self, num_features, logdet=False, affine=True, allow_reverse_init=False
+    ):
+        assert affine
+        super().__init__()
+        self.logdet = logdet
+        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
+        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
+        self.allow_reverse_init = allow_reverse_init
+
+        self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))
+
+    def initialize(self, input):
+        with torch.no_grad():
+            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
+            mean = (
+                flatten.mean(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+            std = (
+                flatten.std(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+
+            self.loc.data.copy_(-mean)
+            self.scale.data.copy_(1 / (std + 1e-6))
+
+    def forward(self, input, reverse=False):
+        if reverse:
+            return self.reverse(input)
+        if len(input.shape) == 2:
+            input = input[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        _, _, height, width = input.shape
+
+        if self.training and self.initialized.item() == 0:
+            self.initialize(input)
+            self.initialized.fill_(1)
+
+        h = self.scale * (input + self.loc)
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+
+        if self.logdet:
+            log_abs = torch.log(torch.abs(self.scale))
+            logdet = height * width * torch.sum(log_abs)
+            logdet = logdet * torch.ones(input.shape[0]).to(input)
+            return h, logdet
+
+        return h
+
+    def reverse(self, output):
+        if self.training and self.initialized.item() == 0:
+            if not self.allow_reverse_init:
+                raise RuntimeError(
+                    "Initializing ActNorm in reverse direction is "
+                    "disabled by default. Use allow_reverse_init=True to enable."
+                )
+            else:
+                self.initialize(output)
+                self.initialized.fill_(1)
+
+        if len(output.shape) == 2:
+            output = output[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        h = output / self.scale - self.loc
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+        return h
diff --git a/nsr/losses/vqperceptual.py b/nsr/losses/vqperceptual.py
new file mode 100644
index 0000000000000000000000000000000000000000..6195f0a6ed7ee6fd32c1bccea071e6075e95ee43
--- /dev/null
+++ b/nsr/losses/vqperceptual.py
@@ -0,0 +1,17 @@
+import torch
+import torch.nn.functional as F
+
+
+def hinge_d_loss(logits_real, logits_fake):
+    loss_real = torch.mean(F.relu(1.0 - logits_real))
+    loss_fake = torch.mean(F.relu(1.0 + logits_fake))
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+    d_loss = 0.5 * (
+        torch.mean(torch.nn.functional.softplus(-logits_real))
+        + torch.mean(torch.nn.functional.softplus(logits_fake))
+    )
+    return d_loss
diff --git a/nsr/lsgm/__init__.py b/nsr/lsgm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..41d6b4c80aa783662862376381d4a59298c12156
--- /dev/null
+++ b/nsr/lsgm/__init__.py
@@ -0,0 +1,9 @@
+# sde diffusion
+from .train_util_diffusion_lsgm import TrainLoop3DDiffusionLSGM
+from .train_util_diffusion_vpsde import TrainLoop3DDiffusion_vpsde
+from .train_util_diffusion_lsgm_noD import TrainLoop3DDiffusionLSGM_noD
+
+# sgm, lsgm
+from .crossattn_cldm import *
+from .sgm_DiffusionEngine import *
+from .flow_matching_trainer import *
\ No newline at end of file
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diff --git a/nsr/lsgm/controlLDM.py b/nsr/lsgm/controlLDM.py
new file mode 100644
index 0000000000000000000000000000000000000000..d181a869008408c44cc4408393ea16431c5ad232
--- /dev/null
+++ b/nsr/lsgm/controlLDM.py
@@ -0,0 +1,556 @@
+"""
+from ControlNet/cldm/cldm.py
+"""
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+import einops
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+import dnnlib
+from dnnlib.util import requires_grad
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+
+
+class TrainLoop3DDiffusionLSGM_Control(TrainLoop3DDiffusionLSGMJointnoD):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 sde_diffusion,
+                 control_model,
+                 control_key,
+                 only_mid_control,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 resume_cldm_checkpoint=None,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 schedule_sampler=None,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 ignore_resume_opt=False,
+                 freeze_ae=False,
+                 denoised_ae=True,
+                 triplane_scaling_divider=10,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=None,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         **kwargs)
+        self.resume_cldm_checkpoint = resume_cldm_checkpoint
+        self.control_model = control_model
+        self.control_key = control_key
+        self.only_mid_control = only_mid_control
+        self.control_scales = [1.0] * 13
+        self.sd_locked = True
+        self._setup_control_model()
+
+    def _setup_control_model(self):
+
+        requires_grad(self.rec_model, False)
+        requires_grad(self.ddpm_model, self.sd_locked)
+
+        self.mp_cldm_trainer = MixedPrecisionTrainer(
+            model=self.control_model,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=self.fp16_scale_growth,
+            use_amp=self.use_amp,
+            model_name='cldm')
+
+        self.ddp_control_model = DDP(
+            self.control_model,
+            device_ids=[dist_util.dev()],
+            output_device=dist_util.dev(),
+            broadcast_buffers=False,
+            bucket_cap_mb=128,
+            find_unused_parameters=False,
+        )
+
+        # ! load trainable copy
+        try:
+            logger.log(f"load pretrained controlnet, not trainable copy.")
+            self._load_and_sync_parameters(model=self.control_model,
+                                           model_name='cldm',
+                                           resume_checkpoint=self.resume_cldm_checkpoint,
+                                           )  # if available
+        except:
+            logger.log(f"load trainable copy to controlnet")
+            self._load_and_sync_parameters(
+                model=self.control_model,
+                model_name='ddpm')  # load pre-trained SD
+
+        cldm_param = [{
+            'name': 'cldm.parameters()',
+            'params': self.control_model.parameters(),
+        }]
+        if self.sde_diffusion.args.unfix_logit:
+            self.ddpm_model.mixing_logit.requires_grad_(True)
+            cldm_param.append({
+                'name': 'mixing_logit',
+                'params': self.ddpm_model.mixing_logit,
+            })
+
+        self.opt_cldm = AdamW(cldm_param,
+                              lr=self.lr,
+                              weight_decay=self.weight_decay)
+        if self.sd_locked:
+            del self.opt
+
+    # def _load_model(self):
+    #     super()._load_model()
+    #     # ! load pre-trained "SD" and controlNet also
+    #     self._load_and_sync_parameters(model=self.contro,
+    #                                    model_name='cldm') #
+
+    # def _setup_opt(self):
+    # TODO, two optims groups.
+
+    # for rec_param_group in self._init_optim_groups(self.rec_model):
+    #     self.opt.add_param_group(rec_param_group)
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            # dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, step='cldm_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                # if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    # self.eval_ddpm_sample()
+                    self.eval_cldm()
+                    # if self.sde_diffusion.args.train_vae:
+                    #     self.eval_loop()
+
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save(self.mp_cldm_trainer,
+                          self.mp_cldm_trainer.model_name)
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save(self.mp_cldm_trainer,
+                              self.mp_cldm_trainer.model_name)
+                    # if self.sde_diffusion.args.train_vae:
+                    #     self.save(self.mp_trainer_rec,
+                    #               self.mp_trainer_rec.model_name)
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save(
+                self.mp_cldm_trainer,
+                self.mp_cldm_trainer.model_name)  # rec and ddpm all fixed.
+            # st()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def _update_cldm_ema(self):
+        for rate, params in zip(self.ema_rate, self.ema_cldm_params):
+            update_ema(params, self.mp_cldm_trainer.master_params, rate=rate)
+
+    def run_step(self, batch, step='cldm_step'):
+
+        # if step == 'diffusion_step_rec':
+
+        if step == 'cldm_step':
+            self.cldm_train_step(batch)
+
+        # if took_step_ddpm:
+        # self._update_cldm_ema()
+
+        self._anneal_lr()
+        self.log_step()
+
+    @th.no_grad()
+    def get_c_input(self, batch, bs=None, *args, **kwargs):
+        # x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
+        control = batch[self.control_key]
+        if bs is not None:
+            control = control[:bs]
+        # control = control.to(self.device)
+        # control = einops.rearrange(control, 'b h w c -> b c h w')
+        control = control.to(memory_format=th.contiguous_format).float()
+        # return x, dict(c_crossattn=[c], c_concat=[control])
+        return dict(c_concat=[control])
+
+    # for compatablity with p_sample, to lint
+    def apply_model_inference(self, x_noisy, t, c, model_kwargs={}):
+        control = self.ddp_control_model(x=x_noisy,
+                                         hint=th.cat(c['c_concat'], 1),
+                                         timesteps=t,
+                                         context=None)
+        control = [c * scale for c, scale in zip(control, self.control_scales)]
+        pred_params = self.ddp_ddpm_model(
+            x_noisy, t, **{
+                **model_kwargs, 'control': control
+            })
+        return pred_params
+
+    def apply_control_model(self, p_sample_batch, cond):
+        x_noisy, t, = (p_sample_batch[k] for k in ('eps_t_p', 't_p'))
+
+        control = self.ddp_control_model(x=x_noisy,
+                                         hint=th.cat(cond['c_concat'], 1),
+                                         timesteps=t,
+                                         context=None)
+        control = [c * scale for c, scale in zip(control, self.control_scales)]
+        return control
+
+    def apply_model(self, p_sample_batch, cond, model_kwargs={}):
+        control = self.apply_control_model(p_sample_batch,
+                                           cond)  # len(control): 13
+        return super().apply_model(p_sample_batch, **{
+            **model_kwargs, 'control': control
+        })
+
+    # ddpm + rec loss
+    def cldm_train_step(self, batch, behaviour='cano', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+
+        # ! enable the gradient of both models
+        requires_grad(self.ddp_control_model, True)
+
+        self.mp_cldm_trainer.zero_grad()  # !!!!
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_cldm_trainer.use_amp):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                vae_out = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='encoder_vae',
+                )  # pred: (B, 3, 64, 64)
+                # eps = vae_out[self.latent_name]
+                eps = vae_out.pop(self.latent_name)
+
+                p_sample_batch = self.prepare_ddpm(eps)
+                cond = self.get_c_input(micro)
+
+                # ! running diffusion forward
+                ddpm_ret = self.apply_model(p_sample_batch, cond)
+                if self.sde_diffusion.args.p_rendering_loss:
+
+                    target = micro
+                    pred = self.ddp_rec_model(
+                        # latent=vae_out,
+                        latent={
+                            # **vae_out, 
+                            self.latent_name:
+                            ddpm_ret['pred_x0_p'],
+                            'latent_name': self.latent_name
+                        },
+                        c=micro['c'],
+                        behaviour=self.render_latent_behaviour)
+
+                    # vae reconstruction loss
+                    with self.ddp_control_model.no_sync():  # type: ignore
+                        p_vae_recon_loss, rec_loss_dict = self.loss_class(
+                            pred, target, test_mode=False)
+                    log_rec3d_loss_dict(rec_loss_dict)
+                    # log_rec3d_loss_dict(
+                    #     dict(p_vae_recon_loss=p_vae_recon_loss, ))
+                    loss = p_vae_recon_loss + ddpm_ret['p_eps_objective']  # TODO, add obj_weight_t_p?
+                else:
+                    loss = ddpm_ret['p_eps_objective']
+
+                # =====================================================================
+
+            self.mp_cldm_trainer.backward(loss)  # joint gradient descent
+
+        # update ddpm accordingly
+        self.mp_cldm_trainer.optimize(self.opt_cldm)
+
+        if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            self.log_control_images(vae_out, p_sample_batch, micro,
+                                    ddpm_ret)
+
+    @th.inference_mode()
+    def log_control_images(self, vae_out, p_sample_batch, micro, ddpm_ret):
+
+        eps_t_p, t_p, logsnr_p = (p_sample_batch[k] for k in (
+            'eps_t_p',
+            't_p',
+            'logsnr_p',
+        ))
+        pred_eps_p = ddpm_ret['pred_eps_p']
+
+        vae_out.pop('posterior')  # for calculating kl loss
+        vae_out_for_pred = {
+            k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+            for k, v in vae_out.items()
+        }
+
+        pred = self.ddp_rec_model(latent=vae_out_for_pred,
+                                  c=micro['c'][0:1],
+                                  behaviour=self.render_latent_behaviour)
+        assert isinstance(pred, dict)
+
+        pred_img = pred['image_raw']
+        gt_img = micro['img']
+
+        if 'depth' in micro:
+            gt_depth = micro['depth']
+            if gt_depth.ndim == 3:
+                gt_depth = gt_depth.unsqueeze(1)
+            gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                    gt_depth.min())
+        else:
+            gt_depth = th.zeros_like(gt_img[:, 0:1, ...])
+
+        if 'image_depth' in pred:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+
+
+        gt_img = self.pool_128(gt_img)
+        gt_depth = self.pool_128(gt_depth)
+        cond = self.get_c_input(micro)
+        hint = th.cat(cond['c_concat'], 1)
+
+        gt_vis = th.cat([
+            gt_img,
+            self.pool_128(hint), gt_img,
+            gt_depth.repeat_interleave(3, dim=1)
+        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+        # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+        noised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=eps_t_p[0:1] * self.
+            triplane_scaling_divider,  # TODO, how to define the scale automatically
+            behaviour=self.render_latent_behaviour)
+
+        pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+            eps_t_p, pred_eps_p, logsnr_p)  # for VAE loss, denosied latent
+
+        # pred_xstart_3D
+        denoised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=pred_x0[0:1] * self.
+            triplane_scaling_divider,  # TODO, how to define the scale automatically?
+            behaviour=self.render_latent_behaviour)
+
+        pred_vis = th.cat(
+            [
+                self.pool_128(img) for img in (
+                    pred_img[0:1],
+                    noised_ae_pred['image_raw'][0:1],
+                    denoised_ae_pred['image_raw'][0:1],  # controlnet result
+                    pred_depth[0:1].repeat_interleave(3, dim=1))
+            ],
+            dim=-1)  # B, 3, H, W
+
+        vis = th.cat([gt_vis, pred_vis],
+                     dim=-2)[0].permute(1, 2,
+                                        0).cpu()  # ! pred in range[-1, 1]
+
+        # vis_grid = torchvision.utils.make_grid(vis) # HWC
+        vis = vis.numpy() * 127.5 + 127.5
+        vis = vis.clip(0, 255).astype(np.uint8)
+        Image.fromarray(vis).save(
+            f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg'
+        )
+        print(
+            'log denoised vis to: ',
+            f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg'
+        )
+
+        th.cuda.empty_cache()
+
+    @th.inference_mode()
+    def eval_cldm(self):
+        self.control_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False,
+                use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+        batch = next(iter(self.eval_data))
+
+        # use the first frame as the condition now
+        novel_view_cond = {
+            k: v[0:1].to(dist_util.dev())  # .repeat_interleave(
+            # micro['img'].shape[0], 0)
+            for k, v in batch.items()
+        }
+        cond = self.get_c_input(novel_view_cond)
+        hint = th.cat(cond['c_concat'], 1)
+
+        # record cond images
+        torchvision.utils.save_image(
+            hint,
+            f'{logger.get_dir()}/{self.step + self.resume_step}_cond.jpg',
+            normalize=True,
+            value_range=(-1, 1))
+
+        # broadcast to args.batch_size
+        cond = {
+            k:
+            [cond.repeat_interleave(args.batch_size, 0) for cond in cond_list]
+            for k, cond_list in cond.items()  # list of Tensors
+        }
+
+        for i in range(1):
+            triplane_sample = sample_fn(
+                self,
+                (
+                    args.batch_size,
+                    self.rec_model.decoder.ldm_z_channels * 3,  # type: ignore
+                    self.diffusion_input_size,
+                    self.diffusion_input_size),
+                cond=cond,
+                clip_denoised=args.clip_denoised,
+                model_kwargs=model_kwargs,
+                mixing_normal=True,  # !
+                device=dist_util.dev())
+            th.cuda.empty_cache()
+
+            self.render_video_given_triplane(
+                triplane_sample,
+                self.rec_model,  # compatible with join_model
+                name_prefix=f'{self.step + self.resume_step}_{i}')
+
+            del triplane_sample
+            th.cuda.empty_cache()
+
+        self.control_model.train()
\ No newline at end of file
diff --git a/nsr/lsgm/crossattn_cldm.py b/nsr/lsgm/crossattn_cldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e0f0744f1466f8a22b5ea0821eb0e9e50ca6215
--- /dev/null
+++ b/nsr/lsgm/crossattn_cldm.py
@@ -0,0 +1,1147 @@
+"""
+https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L30
+"""
+import copy
+import functools
+import random
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+from click import prompt
+import einops
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+from ldm.modules.encoders.modules import FrozenClipImageEmbedder, TextEmbedder, FrozenCLIPTextEmbedder, FrozenOpenCLIPImagePredictionEmbedder, FrozenOpenCLIPImageEmbedder
+
+import dnnlib
+from dnnlib.util import requires_grad
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+# from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+# from .controlLDM import TrainLoop3DDiffusionLSGM_Control  # joint diffusion and rec class
+from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+
+__conditioning_keys__ = {
+    'concat': 'c_concat',
+    'crossattn': 'c_crossattn',
+    'adm': 'y'
+}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class TrainLoop3DDiffusionLSGM_crossattn(TrainLoop3DDiffusionLSGMJointnoD):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 sde_diffusion,
+                 control_model,
+                 control_key,
+                 only_mid_control,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 resume_cldm_checkpoint=None,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 schedule_sampler=None,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 ignore_resume_opt=False,
+                 freeze_ae=False,
+                 denoised_ae=True,
+                 triplane_scaling_divider=10,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 normalize_clip_encoding=False,
+                 scale_clip_encoding=1.0,
+                 cfg_dropout_prob=0.,
+                 cond_key='img_sr',
+                 use_eos_feature=False,
+                 compile=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         control_model=control_model,
+                         control_key=control_key,
+                         only_mid_control=only_mid_control,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=resume_cldm_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         compile=compile,
+                         **kwargs)
+        self.conditioning_key = 'c_crossattn'
+        self.cond_key = cond_key
+        self.instantiate_cond_stage(normalize_clip_encoding,
+                                    scale_clip_encoding, cfg_dropout_prob,
+                                    use_eos_feature)
+        requires_grad(self.rec_model, False)
+        self.rec_model.eval()
+
+        # self.normalize_clip_encoding = normalize_clip_encoding
+        # self.cfg_dropout_prob = cfg_dropout_prob
+
+    def instantiate_cond_stage(self, normalize_clip_encoding,
+                               scale_clip_encoding, cfg_dropout_prob,
+                               use_eos_feature):
+        # https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L509C1-L509C46
+        # self.cond_stage_model.train = disabled_train  # type: ignore
+        if self.cond_key == 'caption':
+            self.cond_txt_model = TextEmbedder(dropout_prob=cfg_dropout_prob,
+                                               use_eos_feature=use_eos_feature)
+        elif self.cond_key == 'img':
+            self.cond_img_model = FrozenOpenCLIPImagePredictionEmbedder(
+                1, 1,
+                FrozenOpenCLIPImageEmbedder(freeze=True,
+                                            device=dist_util.dev(),
+                                            init_device=dist_util.dev()))
+
+        else:  # zero-shot Text to 3D using normalized clip latent
+            self.cond_stage_model = FrozenClipImageEmbedder(
+                'ViT-L/14',
+                dropout_prob=cfg_dropout_prob,
+                normalize_encoding=normalize_clip_encoding,
+                scale_clip_encoding=scale_clip_encoding)
+            self.cond_stage_model.freeze()
+
+            self.cond_txt_model = FrozenCLIPTextEmbedder(
+                dropout_prob=cfg_dropout_prob,
+                scale_clip_encoding=scale_clip_encoding)
+            self.cond_txt_model.freeze()
+
+    @th.no_grad()
+    def get_c_input(self,
+                    batch,
+                    bs=None,
+                    use_text=False,
+                    prompt="",
+                    force_drop_ids=None,
+                    *args,
+                    **kwargs):
+        if use_text:
+            cond_inp = prompt
+        else:
+            if 'caption' in self.cond_key:  # support caption-img
+                cond_inp = batch['caption']
+            else:
+                cond_inp = batch[self.cond_key]
+        # if bs is not None:
+        #     cond_inp = cond_inp[:bs]
+
+        # using clip to transform control to tokens for crossattn
+        control = None
+        if 'caption' in self.cond_key:
+            c = self.cond_txt_model(
+                cond_inp,
+                train=self.ddpm_model.training,
+                force_drop_ids=force_drop_ids,
+            )  # ! SD training text condition injection layer
+            if bs is None:  # duplicated sample
+                if c.shape[0] != batch['c'].shape[0]:
+                    c = th.repeat_interleave(c,
+                                             batch['c'].shape[0] // c.shape[0],
+                                             dim=0)
+            else:
+                assert c.shape[0] == bs
+
+            # st()
+            # if 'img' in self.cond_key:
+
+            # ! later
+            # if 'img' in batch:
+            #     control = batch['img'] + 0.02 * th.randn_like(
+            #         batch['img'])  # follow SVD?
+
+        elif self.cond_key == 'img':
+            c = self.cond_img_model(cond_inp)
+            # control = batch['img']
+            control = batch['img'] + 0.02 * th.randn_like(
+                batch['img'])  # follow SVD?
+
+        else:  # zero shot
+            if use_text:  # for test
+                assert prompt != ""
+                c = self.cond_txt_model.encode(prompt)  # ! for test
+            else:
+                cond_inp = cond_inp.to(
+                    memory_format=th.contiguous_format).float()
+                c = self.cond_stage_model(cond_inp)  # BS 768
+
+        # if c.shape[0] < batch['img_to_encoder'].shape[0]:
+        #     c = th.repeat_interleave(c, batch['img_to_encoder'].shape[0]//c.shape[0], dim=0)
+
+        # return dict(c_concat=[control])
+        # return  dict(c_crossattn=c, c_concat=batch['img'])
+        # if self.cond_key == 'img':
+        # return dict(c_crossattn=c, c_concat=control)
+        return dict(c_crossattn=c)
+
+        # else:
+        #     return dict(c_crossattn=c)
+
+        # return dict(__conditioning_keys__[self.cond_key]=)
+        # return {self.conditioning_key: [c], 'c_concat': [cond_inp]}
+        # return {self.conditioning_key: c, 'c_concat': [cond_inp]}
+
+    # TODO, merge the APIs
+    def apply_model_inference(self, x_noisy, t, c, model_kwargs={}):
+        pred_params = self.ddp_ddpm_model(x_noisy,
+                                          timesteps=t,
+                                          **{
+                                              **model_kwargs, 'context':
+                                              c['c_crossattn'],
+                                              'hint':
+                                              c.get('c_concat', None)
+                                          })
+        return pred_params
+
+    def apply_model(self, p_sample_batch, cond, model_kwargs={}):
+        return super().apply_model(
+            p_sample_batch,
+            **{
+                **model_kwargs, 'context': cond['c_crossattn'],
+                'hint': cond.get('c_concat', None)
+                # **cond,
+            })
+
+    def run_step(self, batch, step='ldm_step'):
+
+        # if step == 'diffusion_step_rec':
+
+        if step == 'ldm_step':
+            self.ldm_train_step(batch)
+
+        # if took_step_ddpm:
+        # self._update_cldm_ema()
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        # eval camera
+        camera = th.load('eval_pose.pt', map_location=dist_util.dev())
+
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            # dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, step='ldm_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                try:
+                    out = logger.dumpkvs()
+                    # * log to tensorboard
+                    for k, v in out.items():
+                        self.writer.add_scalar(f'Loss/{k}', v,
+                                            self.step + self.resume_step)
+                except Exception as e:
+                    continue # no storage
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            # st()
+            if self.step % self.eval_interval == 0:
+                # if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    # self.eval_ddpm_sample()
+                    # self.eval_cldm(use_ddim=True, unconditional_guidance_scale=7.5, prompt="") # during training, use image as condition
+                    if self.cond_key == 'caption':
+                        prompt_list = [
+                            # "a voxelized dog",
+                            # "a cute toy cat",
+                            'Yellow rubber duck',
+                            # "a blue plastic chair.",
+                            # "An Eiffel Tower.",
+                            # "A bowl of food.",
+                            # "An engineer.",
+                            # 'A bowl of vegetables',
+                            # 'A birthday cupcake',
+                            # 'A chair that looks like an avocado',
+                        ]
+                        self.eval_cldm(
+                            # use_ddim=False,
+                            # prompt="a voxelized dog",
+                            # prompt="a blue plastic chair.",
+                            # prompt="An Eiffel Tower.",
+                            # prompt="A bowl of food.",
+                            # prompt="An engineer.",
+                            # prompt='A bowl of vegetables',
+                            # prompt='A birthday cupcake',
+                            prompt=prompt_list[random.randint(0,len(prompt_list)-1)],
+                            use_train_trajectory=False,
+                            camera=camera)  # fix condition bug first
+                    else:
+                        # i23d
+                        self.eval_cldm(
+                            use_train_trajectory=False,
+                            camera=camera)  # fix condition bug first
+
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0 and self.step != 0:
+            # if self.step % self.save_interval == 0:
+                self.save(self.mp_trainer, self.mp_trainer.model_name)
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save(self.mp_trainer, self.mp_trainer.model_name)
+                
+                    # ! delete old ones
+                    if (self.step - 1) % 1e5 == 0:
+                        for idx in range(1,10):
+                            os.remove(f"model_{self.mp_trainer.model_name}{(self.step+self.resume_step-idx*1e4):07d}.pt" )
+
+                    # if self.sde_diffusion.args.train_vae:
+                    #     self.save(self.mp_trainer_rec,
+                    #               self.mp_trainer_rec.model_name)
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save(self.mp_trainer,
+                      self.mp_trainer.model_name)  # rec and ddpm all fixed.
+            # st()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    # ddpm + rec loss
+    def ldm_train_step(self, batch, behaviour='cano', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+
+        # ! enable the gradient of both models
+        requires_grad(self.ddpm_model, True)
+
+        self.mp_trainer.zero_grad()  # !!!!
+
+        if 'img' in batch:
+            batch_size = batch['img'].shape[0]
+        else:
+            batch_size = len(batch['caption'])
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.bfloat16,
+                                      enabled=self.mp_trainer.use_amp):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                if 'latent' in micro:
+                    vae_out = {self.latent_name: micro['latent']}
+                else:
+                    vae_out = self.ddp_rec_model(
+                        img=micro['img_to_encoder'],
+                        c=micro['c'],
+                        behaviour='encoder_vae',
+                    )  # pred: (B, 3, 64, 64)
+
+                eps = vae_out[self.latent_name] / self.triplane_scaling_divider
+                # eps = vae_out.pop(self.latent_name)
+
+                if 'bg_plane' in vae_out:
+                    eps = th.cat((eps, vae_out['bg_plane']),
+                                 dim=1)  # include background, B 12+4 32 32
+
+                p_sample_batch = self.prepare_ddpm(eps)
+                cond = self.get_c_input(micro, bs=eps.shape[0])
+
+                # ! running diffusion forward
+                ddpm_ret = self.apply_model(p_sample_batch, cond)
+                if self.sde_diffusion.args.p_rendering_loss:
+
+                    target = micro
+                    pred = self.ddp_rec_model(
+                        # latent=vae_out,
+                        latent={
+                            # **vae_out,
+                            self.latent_name: ddpm_ret['pred_x0_p'],
+                            'latent_name': self.latent_name
+                        },
+                        c=micro['c'],
+                        behaviour=self.render_latent_behaviour)
+
+                    # vae reconstruction loss
+                    with self.ddp_control_model.no_sync():  # type: ignore
+                        p_vae_recon_loss, rec_loss_dict = self.loss_class(
+                            pred, target, test_mode=False)
+                    log_rec3d_loss_dict(rec_loss_dict)
+                    # log_rec3d_loss_dict(
+                    #     dict(p_vae_recon_loss=p_vae_recon_loss, ))
+                    loss = p_vae_recon_loss + ddpm_ret[
+                        'p_eps_objective']  # TODO, add obj_weight_t_p?
+                else:
+                    loss = ddpm_ret['p_eps_objective'].mean()
+
+                # =====================================================================
+
+            self.mp_trainer.backward(loss)  # joint gradient descent
+
+        # update ddpm accordingly
+        self.mp_trainer.optimize(self.opt)
+
+        if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            self.log_control_images(vae_out, p_sample_batch, micro, ddpm_ret)
+
+    @th.inference_mode()
+    def log_control_images(self, vae_out, p_sample_batch, micro, ddpm_ret):
+
+        eps_t_p, t_p, logsnr_p = (p_sample_batch[k] for k in (
+            'eps_t_p',
+            't_p',
+            'logsnr_p',
+        ))
+        pred_eps_p = ddpm_ret['pred_eps_p']
+
+        if 'posterior' in vae_out:
+            vae_out.pop('posterior')  # for calculating kl loss
+        vae_out_for_pred = {
+            k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+            for k, v in vae_out.items()
+        }
+
+        pred = self.ddp_rec_model(latent=vae_out_for_pred,
+                                  c=micro['c'][0:1],
+                                  behaviour=self.render_latent_behaviour)
+        assert isinstance(pred, dict)
+
+        pred_img = pred['image_raw']
+        if 'img' in micro:
+            gt_img = micro['img']
+        else:
+            gt_img = th.zeros_like(pred['image_raw'])
+
+        if 'depth' in micro:
+            gt_depth = micro['depth']
+            if gt_depth.ndim == 3:
+                gt_depth = gt_depth.unsqueeze(1)
+            gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                      gt_depth.min())
+        else:
+            gt_depth = th.zeros_like(gt_img[:, 0:1, ...])
+
+        if 'image_depth' in pred:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+
+        gt_img = self.pool_128(gt_img)
+        gt_depth = self.pool_128(gt_depth)
+        # cond = self.get_c_input(micro)
+        # hint = th.cat(cond['c_concat'], 1)
+
+        gt_vis = th.cat(
+            [
+                gt_img,
+                gt_img,
+                gt_img,
+                # self.pool_128(hint),
+                # gt_img,
+                gt_depth.repeat_interleave(3, dim=1)
+            ],
+            dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+        # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+        if 'bg_plane' in vae_out:
+            noised_latent = {
+                'latent_normalized_2Ddiffusion':
+                eps_t_p[0:1, :12] * self.triplane_scaling_divider,
+                'bg_plane':
+                eps_t_p[0:1, 12:16] * self.triplane_scaling_divider,
+            }
+        else:
+            noised_latent = {
+                'latent_normalized_2Ddiffusion':
+                eps_t_p[0:1] * self.triplane_scaling_divider,
+            }
+
+        noised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=noised_latent,
+            # latent=eps_t_p[0:1] * self.
+            # triplane_scaling_divider,  # TODO, how to define the scale automatically
+            behaviour=self.render_latent_behaviour)
+
+        pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+            eps_t_p, pred_eps_p, logsnr_p)  # for VAE loss, denosied latent
+
+        if 'bg_plane' in vae_out:
+            denoised_latent = {
+                'latent_normalized_2Ddiffusion':
+                pred_x0[0:1, :12] * self.triplane_scaling_divider,
+                'bg_plane':
+                pred_x0[0:1, 12:16] * self.triplane_scaling_divider,
+            }
+        else:
+            denoised_latent = {
+                'latent_normalized_2Ddiffusion':
+                pred_x0[0:1] * self.triplane_scaling_divider,
+            }
+
+        # pred_xstart_3D
+        denoised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=denoised_latent,
+            # latent=pred_x0[0:1] * self.
+            # triplane_scaling_divider,  # TODO, how to define the scale automatically?
+            behaviour=self.render_latent_behaviour)
+
+        pred_vis = th.cat(
+            [
+                self.pool_128(img) for img in (
+                    pred_img[0:1],
+                    noised_ae_pred['image_raw'][0:1],
+                    denoised_ae_pred['image_raw'][0:1],  # controlnet result
+                    pred_depth[0:1].repeat_interleave(3, dim=1))
+            ],
+            dim=-1)  # B, 3, H, W
+
+        if 'img' in micro:
+            vis = th.cat([gt_vis, pred_vis],
+                         dim=-2)[0].permute(1, 2,
+                                            0).cpu()  # ! pred in range[-1, 1]
+        else:
+            vis = pred_vis[0].permute(1, 2, 0).cpu()
+
+        # vis_grid = torchvision.utils.make_grid(vis) # HWC
+        vis = vis.numpy() * 127.5 + 127.5
+        vis = vis.clip(0, 255).astype(np.uint8)
+        Image.fromarray(vis).save(
+            f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg'
+        )
+
+        # if self.cond_key == 'caption':
+        #     with open(f'{logger.get_dir()}/{self.step+self.resume_step}caption_{t_p[0].item():3}.txt', 'w') as f:
+        #         f.write(micro['caption'][0])
+
+        print(
+            'log denoised vis to: ',
+            f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg'
+        )
+
+        th.cuda.empty_cache()
+
+    @th.inference_mode()
+    def eval_cldm(
+        self,
+        prompt="",
+        use_ddim=False,
+        unconditional_guidance_scale=1.0,
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        num_instances=1,
+    ):
+        self.ddpm_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False,
+                use_ddim=use_ddim))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+
+        # use the first frame as the condition now
+        extra_kwargs = {}
+
+        uc = None
+        if args.use_ddim:
+            if unconditional_guidance_scale != 1.0:
+                uc = self.get_c_input(
+                    {self.cond_key: 'None'},
+                    use_text=True,
+                    prompt="None",
+                    bs=1,  # TODO, support BS>1 later
+                    force_drop_ids=np.array(
+                        [  # ! make sure using dropped tokens
+                            1
+                        ]))  # use specific prompt for debug
+            extra_kwargs.update(
+                dict(
+                    unconditional_guidance_scale=unconditional_guidance_scale,
+                    unconditional_conditioning=uc,  # TODO
+                    # {
+                    #     k : unconditional_guidance_scale
+                    #     for k in cond.keys()
+                    # }
+                ))
+
+        # hint = th.cat(cond['c_concat'], 1)
+
+        # record cond images
+        # broadcast to args.batch_size
+
+        for instance in range(num_instances):
+
+            if self.cond_key == 'caption':
+                if camera is not None:
+                    batch = {'c': camera.clone()}
+            else:
+                if use_train_trajectory:
+                    batch = next(iter(self.data))
+                else:
+                    try:
+                        batch = next(self.eval_data)
+                    except Exception as e:
+                        self.eval_data = iter(self.eval_data)
+                        batch = next(self.eval_data)
+
+                if camera is not None:
+                    batch['c'] = camera.clone()
+
+            # ! generate new samples
+
+            novel_view_cond = {
+                k:
+                v[0:1].to(dist_util.dev())
+                if isinstance(v, th.Tensor) else v[0:1]
+                # micro['img'].shape[0], 0)
+                for k, v in batch.items()
+            }
+
+            cond = self.get_c_input(
+                novel_view_cond, use_text=prompt != "",
+                prompt=prompt)  # use specific prompt for debug
+
+            cond = {
+                k: cond_v.repeat_interleave(args.batch_size, 0)
+                for k, cond_v in cond.items()
+                # if k == self.conditioning_key
+            }
+
+            if self.cond_key == 'caption':
+                if prompt != '':
+                    with open(
+                            f'{logger.get_dir()}/triplane_{self.step+self.resume_step}_{instance}_caption.txt',
+                            'w') as f:
+                        f.write(prompt)
+                else:
+                    with open(
+                            f'{logger.get_dir()}/triplane_{self.step+self.resume_step}_{instance}_caption.txt',
+                            'w') as f:
+                        try:
+                            f.write(novel_view_cond['caption'][0])
+                        except Exception as e:
+                            pass
+
+            elif self.cond_key == 'img':
+                torchvision.utils.save_image(
+                    cond['c_concat'],
+                    f'{logger.get_dir()}/{self.step + self.resume_step}_{instance}_cond.jpg',
+                    normalize=True,
+                    value_range=(-1, 1))
+
+            # continue
+
+            for i in range(num_samples):
+                triplane_sample = sample_fn(
+                    self,
+                    (
+                        args.batch_size,
+                        self.ddpm_model.in_channels
+                        if not self.ddpm_model.roll_out else 3 *
+                        self.ddpm_model.in_channels,  # type: ignore
+                        self.diffusion_input_size,
+                        self.diffusion_input_size),
+                    cond=cond,
+                    clip_denoised=args.clip_denoised,
+                    model_kwargs=model_kwargs,
+                    # mixing_normal=True,  # !
+                    mixing_normal=self.ddpm_model.mixed_prediction,  # !
+                    device=dist_util.dev(),
+                    **extra_kwargs)
+                th.cuda.empty_cache()
+
+                self.render_video_given_triplane(
+                    triplane_sample,
+                    self.rec_model,  # compatible with join_model
+                    name_prefix=
+                    f'{self.step + self.resume_step}_{instance}_{i}',
+                    save_img=save_img,
+                    render_reference=batch,
+                    export_mesh=False)
+
+                # save gt
+                # video_out = imageio.get_writer(
+                #     f'{logger.get_dir()}/triplane_{self.step + self.resume_step}_{i}_reference.mp4',
+                #     mode='I',
+                #     fps=15,
+                #     codec='libx264')
+
+                # for j in range(batch['img'].shape[0]
+                #             ):  # ! currently only export one plane at a time
+                #     cpu_gt = batch['img'][j].cpu().permute(1,2,0).numpy()
+                #     cpu_gt = (cpu_gt*127.5)+127.5
+                #     video_out.append_data(cpu_gt.astype(np.uint8))
+
+                # video_out.close()
+                # del video_out
+
+            # del triplane_sample
+            # th.cuda.empty_cache()
+
+        self.ddpm_model.train()
+
+
+class TrainLoop3DDiffusionLSGM_crossattn_controlNet(
+        TrainLoop3DDiffusionLSGM_crossattn):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 sde_diffusion,
+                 control_model,
+                 control_key,
+                 only_mid_control,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 resume_cldm_checkpoint=None,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 schedule_sampler=None,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 ignore_resume_opt=False,
+                 freeze_ae=False,
+                 denoised_ae=True,
+                 triplane_scaling_divider=10,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 normalize_clip_encoding=False,
+                 scale_clip_encoding=1,
+                 cfg_dropout_prob=0,
+                 cond_key='img_sr',
+                 use_eos_feature=False,
+                 compile=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         control_model=control_model,
+                         control_key=control_key,
+                         only_mid_control=only_mid_control,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=resume_cldm_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         normalize_clip_encoding=normalize_clip_encoding,
+                         scale_clip_encoding=scale_clip_encoding,
+                         cfg_dropout_prob=cfg_dropout_prob,
+                         cond_key=cond_key,
+                         use_eos_feature=use_eos_feature,
+                         compile=compile,
+                         **kwargs)
+
+        # st()
+        self.control_model = control_model
+        self.control_key = control_key
+        self.only_mid_control = only_mid_control
+        self.control_scales = [1.0] * 13
+        self.sd_locked = True
+        self._setup_control_model()
+
+    def _setup_control_model(self):
+
+        requires_grad(self.rec_model, False)
+        requires_grad(self.ddpm_model, False)
+
+        self.mp_cldm_trainer = MixedPrecisionTrainer(
+            model=self.control_model,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=self.fp16_scale_growth,
+            use_amp=self.use_amp,
+            model_name='cldm')
+
+        self.ddp_control_model = DDP(
+            self.control_model,
+            device_ids=[dist_util.dev()],
+            output_device=dist_util.dev(),
+            broadcast_buffers=False,
+            bucket_cap_mb=128,
+            find_unused_parameters=False,
+        )
+
+        requires_grad(self.ddp_control_model, True)
+
+        # ! load trainable copy
+        # TODO
+        # st()
+        try:
+            logger.log(f"load pretrained controlnet, not trainable copy.")
+            self._load_and_sync_parameters(model=self.control_model,
+                                           model_name='cldm',
+                                           resume_checkpoint=self.resume_cldm_checkpoint,
+                                           )  # if available
+        except:
+            logger.log(f"load trainable copy to controlnet")
+            model_state_dict = self.control_model.state_dict()
+            for k, v in self.ddpm_model.state_dict().items():
+                if k in model_state_dict.keys() and v.size(
+                ) == model_state_dict[k].size():
+                    model_state_dict[k] = v
+
+            self.control_model.load_state_dict(model_state_dict)
+
+            # self._load_and_sync_parameters(
+            #     model=self.control_model,
+            #     model_name='ddpm')  # load pre-trained SD
+
+        cldm_param = [{
+            'name': 'cldm.parameters()',
+            'params': self.control_model.parameters(),
+        }]
+        # if self.sde_diffusion.args.unfix_logit:
+        #     self.ddpm_model.mixing_logit.requires_grad_(True)
+        #     cldm_param.append({
+        #         'name': 'mixing_logit',
+        #         'params': self.ddpm_model.mixing_logit,
+        #     })
+
+        self.opt_cldm = AdamW(cldm_param,
+                              lr=self.lr,
+                              weight_decay=self.weight_decay)
+        if self.sd_locked:
+            del self.opt
+            del self.mp_trainer
+
+    # add control during inference
+    def apply_model_inference(self, x_noisy, t, c, model_kwargs={}):
+
+        control = self.ddp_control_model(
+            x=x_noisy,
+            #  hint=th.cat(c['c_concat'], 1),
+            hint=c['c_concat'],
+            timesteps=t,
+            context=None)
+        control = [c * scale for c, scale in zip(control, self.control_scales)]
+        model_kwargs.update({'control': control})
+
+        return super().apply_model_inference(x_noisy, t, c, model_kwargs)
+
+    def apply_control_model(self, p_sample_batch, cond):
+        x_noisy, t, = (p_sample_batch[k] for k in ('eps_t_p', 't_p'))
+
+        control = self.ddp_control_model(
+            x=x_noisy,
+            #  hint=th.cat(cond['c_concat'], 1),
+            hint=cond['c_concat'],
+            timesteps=t,
+            context=None)
+
+        control = [c * scale for c, scale in zip(control, self.control_scales)]
+        return control
+
+    def apply_model(self, p_sample_batch, cond, model_kwargs={}):
+
+        control = self.apply_control_model(p_sample_batch,
+                                           cond)  # len(control): 13
+        model_kwargs.update({'control': control})
+
+        return super().apply_model(p_sample_batch, cond, model_kwargs)
+
+    # cldm loss
+    def ldm_train_step(self, batch, behaviour='cano', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+
+        # ! enable the gradient of both models
+        requires_grad(self.ddp_control_model, True)
+        self.mp_cldm_trainer.zero_grad()  # !!!!
+
+        if 'img' in batch:
+            batch_size = batch['img'].shape[0]
+        else:
+            batch_size = len(batch['caption'])
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_cldm_trainer.use_amp):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                if 'latent' in micro:
+                    vae_out = {self.latent_name: micro['latent']}
+                else:
+                    vae_out = self.ddp_rec_model(
+                        img=micro['img_to_encoder'],
+                        c=micro['c'],
+                        behaviour='encoder_vae',
+                    )  # pred: (B, 3, 64, 64)
+
+                eps = vae_out[self.latent_name] / self.triplane_scaling_divider
+                # eps = vae_out.pop(self.latent_name)
+
+                if 'bg_plane' in vae_out:
+                    eps = th.cat((eps, vae_out['bg_plane']),
+                                 dim=1)  # include background, B 12+4 32 32
+
+                p_sample_batch = self.prepare_ddpm(eps)
+                cond = self.get_c_input(micro, bs=eps.shape[0])
+
+                # ! running diffusion forward
+                ddpm_ret = self.apply_model(p_sample_batch, cond)
+                if self.sde_diffusion.args.p_rendering_loss:
+
+                    target = micro
+                    pred = self.ddp_rec_model(
+                        # latent=vae_out,
+                        latent={
+                            # **vae_out,
+                            self.latent_name: ddpm_ret['pred_x0_p'],
+                            'latent_name': self.latent_name
+                        },
+                        c=micro['c'],
+                        behaviour=self.render_latent_behaviour)
+
+                    # vae reconstruction loss
+                    with self.ddp_control_model.no_sync():  # type: ignore
+                        p_vae_recon_loss, rec_loss_dict = self.loss_class(
+                            pred, target, test_mode=False)
+                    log_rec3d_loss_dict(rec_loss_dict)
+                    # log_rec3d_loss_dict(
+                    #     dict(p_vae_recon_loss=p_vae_recon_loss, ))
+                    loss = p_vae_recon_loss + ddpm_ret[
+                        'p_eps_objective']  # TODO, add obj_weight_t_p?
+                else:
+                    loss = ddpm_ret['p_eps_objective'].mean()
+
+                # =====================================================================
+
+            self.mp_cldm_trainer.backward(loss)  # joint gradient descent
+            # p self.control_model.input_hint_block[0].bias
+
+        # update ddpm accordingly
+        self.mp_cldm_trainer.optimize(self.opt_cldm)
+
+        if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            self.log_control_images(vae_out, p_sample_batch, micro, ddpm_ret)
+
+    def run_loop(self):
+        # eval camera
+        camera = th.load('eval_pose.pt', map_location=dist_util.dev())
+
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            # dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, step='ldm_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    # self.eval_ddpm_sample()
+                    # self.eval_cldm(use_ddim=True, unconditional_guidance_scale=7.5, prompt="") # during training, use image as condition
+                    if self.cond_key == 'caption':
+                        self.eval_cldm(
+                            use_ddim=False,
+                            prompt="a voxelized dog",
+                            # prompt="a blue plastic chair.",
+                            use_train_trajectory=False,
+                            camera=camera)  # fix condition bug first
+                    else:
+                        pass  # TODO
+                        # self.eval_cldm(use_ddim=False,
+                        #             prompt="",
+                        #             use_train_trajectory=False,
+                        #             camera=camera)  # fix condition bug first
+                    # if self.sde_diffusion.args.train_vae:
+                    #     self.eval_loop()
+
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0 and self.step != 0:
+                self.save(self.mp_cldm_trainer,
+                          self.mp_cldm_trainer.model_name)
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save(self.mp_trainer, self.mp_trainer.model_name)
+                    # if self.sde_diffusion.args.train_vae:
+                    #     self.save(self.mp_trainer_rec,
+                    #               self.mp_trainer_rec.model_name)
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save(self.mp_trainer, self.mp_trainer.model_name)
+            # self.save(self.mp_trainer,
+            #           self.mp_trainer.model_name)  # rec and ddpm all fixed.
+            # st()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
diff --git a/nsr/lsgm/flow_matching_trainer.py b/nsr/lsgm/flow_matching_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..91acd9f2e30d86debce0a39dff627efb83171f8c
--- /dev/null
+++ b/nsr/lsgm/flow_matching_trainer.py
@@ -0,0 +1,2246 @@
+"""
+https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L30
+"""
+import random
+import pytorch3d
+import copy
+import point_cloud_utils as pcu
+import cv2
+import matplotlib.pyplot as plt
+import torch
+import gc
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+from click import prompt
+import einops
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+import trimesh
+from nsr.camera_utils import generate_input_camera
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+from ldm.modules.encoders.modules import FrozenClipImageEmbedder, TextEmbedder, FrozenCLIPTextEmbedder, FrozenOpenCLIPImagePredictionEmbedder, FrozenOpenCLIPImageEmbedder
+from nsr.camera_utils import generate_input_camera, uni_mesh_path
+
+import dnnlib
+from dnnlib.util import requires_grad
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+# from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+# from .controlLDM import TrainLoop3DDiffusionLSGM_Control  # joint diffusion and rec class
+from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+
+# ! add new schedulers from https://github.com/Stability-AI/generative-models
+
+from .crossattn_cldm import TrainLoop3DDiffusionLSGM_crossattn
+
+# import SD stuffs
+from typing import Any, Dict, List, Optional, Tuple, Union
+from contextlib import contextmanager
+from omegaconf import ListConfig, OmegaConf
+from sgm.modules import UNCONDITIONAL_CONFIG
+
+from sgm.util import (default, disabled_train, get_obj_from_str,
+                      instantiate_from_config, log_txt_as_img)
+
+from transport import create_transport, Sampler
+import math
+
+# for gs rendering
+from utils.gs_utils.graphics_utils import getWorld2View2, getProjectionMatrix, getView2World
+from utils.general_utils import matrix_to_quaternion
+from utils.mesh_util import post_process_mesh, to_cam_open3d_compat
+from datasets.g_buffer_objaverse import focal2fov, fov2focal
+
+import open3d as o3d
+
+# from sgm.sampling_utils.demo.streamlit_helpers import init_sampling
+
+# Function to generate a rotation matrix for an arbitrary theta along the x-axis
+def rotation_matrix_x(theta_degrees):
+    theta = np.radians(theta_degrees)  # Convert degrees to radians
+    cos_theta = np.cos(theta)
+    sin_theta = np.sin(theta)
+    
+    rotation_matrix = np.array([[1, 0, 0],
+                                [0, cos_theta, -sin_theta],
+                                [0, sin_theta, cos_theta]])
+    return rotation_matrix
+
+
+def sample_uniform_cameras_on_sphere(num_samples=1):
+    # Step 1: Sample azimuth angles uniformly from [0, 2*pi)
+    theta = np.random.uniform(0, 2 * np.pi, num_samples)
+    
+    # Step 2: Sample cos(phi) uniformly from [-1, 1]
+    cos_phi = np.random.uniform(-1, 1, num_samples)
+    
+    # Step 3: Calculate the elevation angle (phi) from cos(phi)
+    phi = np.arccos(cos_phi)  # phi will be in [0, pi]
+    
+    # Step 4: Convert spherical coordinates to Cartesian coordinates (x, y, z)
+    # x = np.sin(phi) * np.cos(theta)
+    # y = np.sin(phi) * np.sin(theta)
+    # z = np.cos(phi)
+    
+    # Combine the x, y, z coordinates into a single array
+    # cameras = np.vstack((x, y, z)).T  # Shape: (num_samples, 3)
+    
+    # return cameras
+    return theta, phi
+
+
+
+class FlowMatchingEngine(TrainLoop3DDiffusionLSGM_crossattn):
+
+    def __init__(
+        self,
+        *,
+        rec_model,
+        denoise_model,
+        diffusion,
+        sde_diffusion,
+        control_model,
+        control_key,
+        only_mid_control,
+        loss_class,
+        data,
+        eval_data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        eval_interval,
+        save_interval,
+        resume_checkpoint,
+        resume_cldm_checkpoint=None,
+        use_fp16=False,
+        fp16_scale_growth=0.001,
+        schedule_sampler=None,
+        weight_decay=0,
+        lr_anneal_steps=0,
+        iterations=10001,
+        ignore_resume_opt=False,
+        freeze_ae=False,
+        denoised_ae=True,
+        triplane_scaling_divider=10,
+        use_amp=False,
+        diffusion_input_size=224,
+        normalize_clip_encoding=False,
+        scale_clip_encoding=1,
+        cfg_dropout_prob=0,
+        cond_key='img_sr',
+        use_eos_feature=False,
+        compile=False,
+        snr_type='lognorm',
+        # denoiser_config,
+        # conditioner_config: Union[None, Dict, ListConfig,
+        #                           OmegaConf] = None,
+        # sampler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        # loss_fn_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         control_model=control_model,
+                         control_key=control_key,
+                         only_mid_control=only_mid_control,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=resume_cldm_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         normalize_clip_encoding=normalize_clip_encoding,
+                         scale_clip_encoding=scale_clip_encoding,
+                         cfg_dropout_prob=cfg_dropout_prob,
+                         cond_key=cond_key,
+                         use_eos_feature=use_eos_feature,
+                         compile=compile,
+                         **kwargs)
+
+        #  ! sgm diffusion pipeline
+        # ! reuse the conditioner
+        self.snr_type = snr_type
+        self.latent_key = 'latent'
+
+        if self.cond_key == 'caption': # ! text pretrain
+            if snr_type == 'stage2-t23d': 
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage2-t23d.yaml')['ldm_configs']
+            elif snr_type == 'stage1-t23d': 
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage1-t23d.yaml')['ldm_configs']
+                self.latent_key = 'normalized-fps-xyz' # learn xyz diff
+            else: # just simple t23d, no xyz condition
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/t23d-clipl-compat-fm.yaml')['ldm_configs']
+        else: # 
+
+            # assert 'lognorm' in snr_type
+            if snr_type == 'lognorm': # by default
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/img23d-clipl-compat-fm-lognorm.yaml')['ldm_configs']
+            # st()
+            # if snr_type == 'lognorm-highres': # by default
+            elif snr_type == 'img-uniform-gvp': # by default
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform.yaml')['ldm_configs']
+                # self.latent_key = 'fps-xyz' # xyz diffusion
+                self.latent_key = 'normalized-fps-xyz' # to std
+
+            elif snr_type == 'img-uniform-gvp-dino': # by default
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly.yaml')['ldm_configs']
+                self.latent_key = 'normalized-fps-xyz' # to std
+
+            # elif snr_type == 'img-uniform-gvp-dino-xl': # by default
+            #     ldm_configs = OmegaConf.load(
+            #         'sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly.yaml')['ldm_configs']
+            #     self.latent_key = 'normalized-fps-xyz' # to std
+
+            elif snr_type == 'img-uniform-gvp-dino-stage2': # by default
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage2-i23d.yaml')['ldm_configs']
+                # self.latent_key = 'normalized-fps-xyz' # to std
+
+            elif snr_type == 'img-uniform-gvp-clay': # contains both text and image condition
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay.yaml')['ldm_configs']
+                # self.latent_key = 'fps-xyz' # xyz diffusion
+                self.latent_key = 'normalized-fps-xyz' # to std
+
+            elif snr_type == 'pcd-cond-tex':
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond.yaml')['ldm_configs']
+                    # 'sgm/configs/img23d-clipl-compat-fm-lognorm-336.yaml')['ldm_configs']
+
+            # ! stage-2 text-xyz conditioned
+            elif snr_type == 'stage2-t23d':
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage2-t23d.yaml')['ldm_configs']
+
+            elif snr_type == 'lognorm-mv':
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/mv23d-clipl-compat-fm-lognorm.yaml')['ldm_configs']
+            
+            # ! mv version
+            elif snr_type == 'lognorm-mv-plucker':
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm-noclip.yaml')['ldm_configs']
+                    # 'sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm.yaml')['ldm_configs']
+
+            elif snr_type == 'stage1-mv-t23dpt':
+                self.latent_key = 'normalized-fps-xyz' # learn xyz diff
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage1-mv23d-t23dpt.yaml')['ldm_configs']
+
+            elif snr_type == 'stage1-mv-i23dpt':
+                self.latent_key = 'normalized-fps-xyz' # learn xyz diff
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage1-mv23d-i23dpt.yaml')['ldm_configs']
+
+            elif snr_type == 'stage1-mv-i23dpt-noi23d':
+                self.latent_key = 'normalized-fps-xyz' # learn xyz diff
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage1-mv23d-i23dpt-noi23d.yaml')['ldm_configs']
+
+            elif snr_type == 'stage2-mv-i23dpt':
+                # self.latent_key = 'normalized-fps-xyz' # learn xyz diff
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/stage2-mv23d-i23dpt.yaml')['ldm_configs']
+
+
+            else:
+                ldm_configs = OmegaConf.load(
+                    'sgm/configs/img23d-clipl-compat-fm.yaml')['ldm_configs']
+
+        self.loss_fn = (
+            instantiate_from_config(ldm_configs.loss_fn_config)
+            # if loss_fn_config is not None
+            # else None
+        )
+
+        # self.denoiser = instantiate_from_config(
+        #     ldm_configs.denoiser_config).to(dist_util.dev())
+
+        self.transport_sampler = Sampler(self.loss_fn.transport, guider_config=ldm_configs.guider_config)
+
+        self.conditioner = instantiate_from_config(
+            default(ldm_configs.conditioner_config,
+                    UNCONDITIONAL_CONFIG)).to(dist_util.dev())
+
+        # ! setup optimizer (with cond embedder params here)
+        self._set_grad_flag()
+        self._setup_opt2()
+        self._load_model2()
+
+
+    def _set_grad_flag(self):    
+        requires_grad(self.ddpm_model, True) # do not change this flag during training.
+
+    def _setup_opt(self):
+        pass # see below
+
+    def _setup_opt2(self):
+        # ! add trainable conditioner parameters
+        # https://github.com/Stability-AI/generative-models/blob/fbdc58cab9f4ee2be7a5e1f2e2787ecd9311942f/sgm/models/diffusion.py#L219
+
+        # params = list(self.ddpm_model.parameters())
+
+        # https://discuss.pytorch.org/t/how-the-pytorch-freeze-network-in-some-layers-only-the-rest-of-the-training/7088/7
+        self.opt = AdamW([{
+            'name': 'ddpm',
+            # 'params': self.ddpm_model.parameters(),
+            'params': filter(lambda p: p.requires_grad, self.ddpm_model.parameters()), # if you want to freeze some layers
+        },
+        ],
+                         lr=self.lr,
+                         weight_decay=self.weight_decay)
+        
+        
+        embedder_params = []
+        for embedder in self.conditioner.embedders:
+            if embedder.is_trainable:
+                embedder_params = embedder_params + list(embedder.parameters())
+
+
+        if len(embedder_params) != 0:
+            self.opt.add_param_group(
+                {
+                    'name': 'embedder',
+                    'params': embedder_params,
+                    'lr': self.lr*0.5, # smaller lr to finetune dino/clip
+                }
+            )
+        
+        print(self.opt)
+
+    def save(self, mp_trainer=None, model_name='ddpm'):
+        # save embedder params also
+        super().save(mp_trainer, model_name)
+
+        # save embedder ckpt
+        if dist_util.get_rank() == 0:
+            for embedder in self.conditioner.embedders:
+                if embedder.is_trainable:
+                    # embedder_params = embedder_params + list(embedder.parameters())
+                    model_name = embedder.__class__.__name__
+                    filename = f"embedder_{model_name}{(self.step+self.resume_step):07d}.pt"
+                    with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                        "wb") as f:
+                        th.save(embedder.state_dict(), f)
+
+        dist_util.synchronize()
+
+    def _load_model2(self):
+
+        # ! load embedder
+        for embedder in self.conditioner.embedders:
+            if embedder.is_trainable:
+                # embedder_params = embedder_params + list(embedder.parameters())
+                model_name = embedder.__class__.__name__
+                filename = f"embedder_{model_name}{(self.step+self.resume_step):07d}.pt"
+                # embedder_FrozenDinov2ImageEmbedderMV2115000.pt
+
+                # with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                #                     "wb") as f:
+                #     th.save(embedder.state_dict(), f)
+
+                split = self.resume_checkpoint.split("model")
+                resume_checkpoint = str(
+                    Path(split[0]) / filename)
+                if os.path.exists(resume_checkpoint):
+                    if dist.get_rank() == 0:
+                        logger.log(
+                            f"loading cond embedder from checkpoint: {resume_checkpoint}...")
+                        # if model is None:
+                        #     model = self.model
+                        embedder.load_state_dict(
+                            dist_util.load_state_dict(
+                                resume_checkpoint,
+                                map_location=dist_util.dev(),
+                            ))
+                else:
+                    logger.log(f'{resume_checkpoint} not found.')
+
+                if dist_util.get_world_size() > 1:
+                    dist_util.sync_params(embedder.parameters())
+
+
+    def instantiate_cond_stage(self, normalize_clip_encoding,
+                               scale_clip_encoding, cfg_dropout_prob,
+                               use_eos_feature):
+        # https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L509C1-L509C46
+        # self.cond_stage_model.train = disabled_train  # type: ignore
+        # if self.cond_key == 'caption':
+        #     self.cond_txt_model = TextEmbedder(dropout_prob=cfg_dropout_prob,
+        #                                        use_eos_feature=use_eos_feature)
+        # elif self.cond_key == 'img':
+        #     self.cond_img_model = FrozenOpenCLIPImagePredictionEmbedder(
+        #         1, 1,
+        #         FrozenOpenCLIPImageEmbedder(freeze=True,
+        #                                     device=dist_util.dev(),
+        #                                     init_device=dist_util.dev()))
+
+        # else:  # zero-shot Text to 3D using normalized clip latent
+        #     self.cond_stage_model = FrozenClipImageEmbedder(
+        #         'ViT-L/14',
+        #         dropout_prob=cfg_dropout_prob,
+        #         normalize_encoding=normalize_clip_encoding,
+        #         scale_clip_encoding=scale_clip_encoding)
+        #     self.cond_stage_model.freeze()
+
+        #     self.cond_txt_model = FrozenCLIPTextEmbedder(
+        #         dropout_prob=cfg_dropout_prob,
+        #         scale_clip_encoding=scale_clip_encoding)
+        #     self.cond_txt_model.freeze()
+        pass # initialized in the self.__init__() using SD api
+
+
+
+    # ! already merged
+    def prepare_ddpm(self, eps, mode='p'):
+        raise NotImplementedError('already implemented in self.denoiser')
+
+    # merged from noD.py
+
+    # use sota denoiser, loss_fn etc.
+    def ldm_train_step(self, batch, behaviour='cano', *args, **kwargs):
+
+        # ! enable the gradient of both models
+        # requires_grad(self.ddpm_model, True)
+        self._set_grad_flag() # more flexible
+
+        self.mp_trainer.zero_grad()  # !!!!
+
+        if 'img' in batch:
+            batch_size = batch['img'].shape[0]
+        else:
+            batch_size = len(batch['caption'])
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v[i:i+self.microbatch]
+                for k, v in batch.items()
+            }
+
+            # move condition to self.dtype
+            # =================================== ae part ===================================
+            # with th.cuda.amp.autocast(dtype=th.bfloat16,
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                      enabled=self.mp_trainer.use_amp):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                assert 'latent' in micro
+                # st() # torchvision.utils.save_image(micro['img'], 'tmp/img.png', normalize=True, value_range=(-1,1))
+                # vae_out = {self.latent_name: micro['latent']}
+                # else:
+                #     vae_out = self.ddp_rec_model(
+                #         img=micro['img_to_encoder'],
+                #         c=micro['c'],
+                #         behaviour='encoder_vae',
+                #     )  # pred: (B, 3, 64, 64)
+
+                # eps = vae_out[self.latent_name] / self.triplane_scaling_divider
+                # ! if training xyz only
+                # eps = vae_out[self.latent_name][..., -3:] / self.triplane_scaling_divider
+
+                # ! if training texture only
+                eps = micro[self.latent_key] / self.triplane_scaling_divider
+
+                if self.cond_key == 'img-c':
+                    micro['img-c'] = {
+                        # 'img': micro['img'].to(self.dtype),
+                        'img': micro['mv_img'].to(self.dtype), # for compat issue
+                        'c': micro['c'].to(self.dtype),
+                    }
+
+                    # log_rec3d_loss_dict({
+                    #     f"mv-alpha/{i}": self.ddpm_model.blocks[i].mv_alpha[0] for i in range(len(self.ddpm_model.blocks))
+                    # })
+
+
+                loss, loss_other_info = self.loss_fn(self.ddp_ddpm_model,
+                                                    #  self.denoiser,
+                                                     self.conditioner, 
+                                                     eps.to(self.dtype),
+                                                     micro)  # type: ignore
+                loss = loss.mean()
+                # log_rec3d_loss_dict({})
+
+                log_rec3d_loss_dict({
+                    # 'eps_mean':
+                    # eps.mean(),
+                    # 'eps_std':
+                    # eps.std([1, 2, 3]).mean(0),
+                    # 'pred_x0_std':
+                    # loss_other_info['model_output'].std([1, 2, 3]).mean(0),
+                    "p_loss":
+                    loss,
+                })
+
+            self.mp_trainer.backward(loss)  # joint gradient descent
+
+        # update ddpm accordingly
+        self.mp_trainer.optimize(self.opt)
+
+        # ! directly eval_cldm() for sampling.
+        # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+        #     self.log_control_images(vae_out, micro, loss_other_info)
+
+    @th.inference_mode()
+    def log_control_images(self, vae_out, micro, ddpm_ret):
+
+        if 'posterior' in vae_out:
+            vae_out.pop('posterior')  # for calculating kl loss
+        vae_out_for_pred = {self.latent_name: vae_out[self.latent_name][0:1].to(self.dtype)}
+
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+            pred = self.ddp_rec_model(latent=vae_out_for_pred,
+                                    c=micro['c'][0:1],
+                                    behaviour=self.render_latent_behaviour)
+
+        assert isinstance(pred, dict)
+
+        pred_img = pred['image_raw']
+        if 'img' in micro:
+            gt_img = micro['img']
+        else:
+            gt_img = th.zeros_like(pred['image_raw'])
+
+        if 'depth' in micro:
+            gt_depth = micro['depth']
+            if gt_depth.ndim == 3:
+                gt_depth = gt_depth.unsqueeze(1)
+            gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                      gt_depth.min())
+        else:
+            gt_depth = th.zeros_like(gt_img[:, 0:1, ...])
+
+        if 'image_depth' in pred:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+
+        gt_img = self.pool_128(gt_img)
+        gt_depth = self.pool_128(gt_depth)
+        # cond = self.get_c_input(micro)
+        # hint = th.cat(cond['c_concat'], 1)
+
+        gt_vis = th.cat(
+            [
+                gt_img,
+                gt_img,
+                gt_img,
+                # self.pool_128(hint),
+                # gt_img,
+                gt_depth.repeat_interleave(3, dim=1)
+            ],
+            dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+        # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+        # self.sampler
+
+        noised_latent, sigmas, x_start = [
+            ddpm_ret[k] for k in ['noised_input', 'sigmas', 'model_output']
+        ]
+
+        noised_latent = {
+            'latent_normalized_2Ddiffusion':
+            noised_latent[0:1].to(self.dtype) * self.triplane_scaling_divider,
+        }
+
+        denoised_latent = {
+            'latent_normalized_2Ddiffusion':
+            x_start[0:1].to(self.dtype) * self.triplane_scaling_divider,
+        }
+         
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+            noised_ae_pred = self.ddp_rec_model(
+                img=None,
+                c=micro['c'][0:1],
+                latent=noised_latent,
+                behaviour=self.render_latent_behaviour)
+
+            # pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+            # eps_t_p, pred_eps_p, logsnr_p)  # for VAE loss, denosied latent
+
+            # pred_xstart_3D
+            denoised_ae_pred = self.ddp_rec_model(
+                img=None,
+                c=micro['c'][0:1],
+                latent=denoised_latent,
+                # latent=pred_x0[0:1] * self.
+                # triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                behaviour=self.render_latent_behaviour)
+
+        pred_vis = th.cat(
+            [
+                self.pool_128(img) for img in (
+                    pred_img[0:1],
+                    noised_ae_pred['image_raw'][0:1],
+                    denoised_ae_pred['image_raw'][0:1],  # controlnet result
+                    pred_depth[0:1].repeat_interleave(3, dim=1))
+            ],
+            dim=-1)  # B, 3, H, W
+
+        if 'img' in micro:
+            vis = th.cat([gt_vis, pred_vis],
+                         dim=-2)[0].permute(1, 2,
+                                            0).cpu()  # ! pred in range[-1, 1]
+        else:
+            vis = pred_vis[0].permute(1, 2, 0).cpu()
+
+        # vis_grid = torchvision.utils.make_grid(vis) # HWC
+        vis = vis.numpy() * 127.5 + 127.5
+        vis = vis.clip(0, 255).astype(np.uint8)
+        img_save_path = f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{sigmas[0].item():3}.jpg'
+        Image.fromarray(vis).save(img_save_path)
+
+        # if self.cond_key == 'caption':
+        #     with open(f'{logger.get_dir()}/{self.step+self.resume_step}caption_{t_p[0].item():3}.txt', 'w') as f:
+        #         f.write(micro['caption'][0])
+
+        print('log denoised vis to: ', img_save_path)
+
+        th.cuda.empty_cache()
+
+    @th.no_grad()
+    def sample(
+        self,
+        cond: Dict,
+        uc: Union[Dict, None] = None,
+        batch_size: int = 16,
+        shape: Union[None, Tuple, List] = None,
+        use_cfg=True,
+        # cfg_scale=4, # default value in SiT
+        # cfg_scale=1.5, # default value in SiT
+        cfg_scale=4.0, # default value in SiT
+        seed=42,
+        **kwargs,
+    ):
+        
+        logger.log(f'cfg_scale: {cfg_scale}, seed: {seed}')
+
+        # self.sampler
+        sample_fn = self.transport_sampler.sample_ode(num_steps=250, cfg=True) # default ode sampling setting.
+
+        th.manual_seed(seed) # to reproduce result
+        zs = th.randn(batch_size, *shape).to(dist_util.dev()).to(self.dtype)
+        # st()
+        assert use_cfg
+        # sample_model_kwargs = {'uc': uc, 'cond': cond}       
+        model_fn = self.ddpm_model.forward_with_cfg # default
+
+        # ! prepare_inputs in VanillaCFG, for compat issue
+        c_out = {}
+        for k in cond:
+            # if k in ["vector", "crossattn", "concat", 'fps-xyz']:
+            c_out[k] = th.cat((cond[k], uc[k]), 0)
+            # else:
+            #     assert cond[k] == uc[k]
+            #     c_out[k] = cond[k]
+        sample_model_kwargs = {'context': c_out, 'cfg_scale': cfg_scale}
+        zs = th.cat([zs, zs], 0)
+
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+
+            samples = sample_fn(zs, model_fn, **sample_model_kwargs)[-1]
+            samples, _ = samples.chunk(2, dim=0)  # Remove null class samples
+
+        # return samples
+        return samples * self.triplane_scaling_divider
+
+    @th.inference_mode()
+    def eval_cldm(
+        self,
+        prompt="",
+        # use_ddim=False,
+        # unconditional_guidance_scale=1.0,
+        unconditional_guidance_scale=4.0,
+        seed=42,
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        num_instances=1,
+        export_mesh=False,
+    ):
+        # ! slightly modified for new API. combined with
+        # /cpfs01/shared/V2V/V2V_hdd/yslan/Repo/generative-models/sgm/models/diffusion.py:249 log_images()
+        # TODO, support batch_size > 1
+
+        self.ddpm_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False))
+
+        model_kwargs = {}
+
+        uc = None
+        log = dict()
+
+        ucg_keys = [self.cond_key] # i23d
+
+        # if self.cond_key == 'caption':
+        if self.cond_key in ['caption', 'img-xyz']:
+            # batch_c = {self.cond_key: prompt}
+            # batch_c = {self.cond_key: prompt}
+            batch_c = next(self.data) # ! use training set to evaluate t23d for now.
+        elif self.cond_key == 'img-caption':
+            batch_c = {'caption': prompt, 'img': batch['img'].to(dist_util.dev()).to(self.dtype)}
+        else: 
+            batch = next(self.data) # random cond here
+            if self.cond_key == 'img-c':
+                batch_c = {
+                    self.cond_key: {
+                        # 'img': batch['img'].to(self.dtype).to(dist_util.dev()),
+                        'img': batch['mv_img'].to(self.dtype).to(dist_util.dev()),
+                        'c': batch['c'].to(self.dtype).to(dist_util.dev()),
+                    },
+                    'img': batch['img'].to(self.dtype).to(dist_util.dev()) # required by clip
+                }
+
+            else:
+                batch_c = {self.cond_key: batch[self.cond_key].to(dist_util.dev()).to(self.dtype)}
+
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+
+            c, uc = self.conditioner.get_unconditional_conditioning(
+                batch_c,
+                force_uc_zero_embeddings=ucg_keys
+                if len(self.conditioner.embedders) > 0 else [],
+            )
+
+        sampling_kwargs = {'seed': seed, 'cfg_scale': unconditional_guidance_scale}
+
+        N = 3  # hard coded, to update
+        z_shape = (
+            N,
+            self.ddpm_model.in_channels if not self.ddpm_model.roll_out else
+            3 * self.ddpm_model.in_channels,  # type: ignore
+            self.diffusion_input_size,
+            self.diffusion_input_size)
+
+        for k in c:
+            if isinstance(c[k], th.Tensor):
+                c[k], uc[k] = map(lambda y: y[k][:N].to(dist_util.dev()),
+                                  (c, uc))
+    
+        samples = self.sample(c,
+                              shape=z_shape[1:],
+                              uc=uc,
+                              batch_size=N,
+                              **sampling_kwargs)
+        # st() # do rendering first
+
+
+        # ! get c
+        if 'img' in self.cond_key:
+            img_save_path = f'{logger.get_dir()}/{self.step+self.resume_step}_imgcond.jpg'
+            if 'c' in self.cond_key:
+                torchvision.utils.save_image(batch_c['img'][0], img_save_path, value_range=(-1,1), normalize=True, padding=0) # torch.Size([24, 6, 3, 256, 256])
+                th.save(batch_c['img-c']['c'][0], f'{logger.get_dir()}/{self.step+self.resume_step}_c.pt')
+            else:
+                torchvision.utils.save_image(batch_c['img'][0:1], img_save_path, value_range=(-1,1), normalize=True, padding=0)
+
+        assert camera is not None
+        batch = {'c': camera.clone()}
+
+        # else:
+        #     if use_train_trajectory:
+        #         batch = next(iter(self.data))
+        #     else:
+        #         try:
+        #             batch = next(self.eval_data)
+        #         except Exception as e:
+        #             self.eval_data = iter(self.eval_data)
+        #             batch = next(self.eval_data)
+
+        #     if camera is not None:
+        #         batch['c'] = camera.clone()
+
+
+        # rendering
+        for i in range(samples.shape[0]):
+            th.cuda.empty_cache()
+
+            # ! render sampled latent
+            name_prefix = f'{self.step + self.resume_step}_{i}'
+
+            if self.cond_key == 'caption':
+                name_prefix = f'{name_prefix}_{prompt}'
+
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                        enabled=self.mp_trainer.use_amp):
+
+                self.render_video_given_triplane(
+                    samples[i:i+1].to(self.dtype), # default version
+                    self.rec_model,  # compatible with join_model
+                    name_prefix=name_prefix,
+                    save_img=save_img,
+                    render_reference=batch,
+                   export_mesh=False)
+
+        self.ddpm_model.train()
+
+
+class FlowMatchingEngine_gs(FlowMatchingEngine):
+
+    def __init__(
+        self,
+        *,
+        rec_model,
+        denoise_model,
+        diffusion,
+        sde_diffusion,
+        control_model,
+        control_key,
+        only_mid_control,
+        loss_class,
+        data,
+        eval_data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        eval_interval,
+        save_interval,
+        resume_checkpoint,
+        resume_cldm_checkpoint=None,
+        use_fp16=False,
+        fp16_scale_growth=0.001,
+        schedule_sampler=None,
+        weight_decay=0,
+        lr_anneal_steps=0,
+        iterations=10001,
+        ignore_resume_opt=False,
+        freeze_ae=False,
+        denoised_ae=True,
+        triplane_scaling_divider=10,
+        use_amp=False,
+        diffusion_input_size=224,
+        normalize_clip_encoding=False,
+        scale_clip_encoding=1,
+        cfg_dropout_prob=0,
+        cond_key='img_sr',
+        use_eos_feature=False,
+        compile=False,
+        snr_type='lognorm',
+        **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         control_model=control_model,
+                         control_key=control_key,
+                         only_mid_control=only_mid_control,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=resume_cldm_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         normalize_clip_encoding=normalize_clip_encoding,
+                         scale_clip_encoding=scale_clip_encoding,
+                         cfg_dropout_prob=cfg_dropout_prob,
+                         cond_key=cond_key,
+                         use_eos_feature=use_eos_feature,
+                         compile=compile,
+                         snr_type=snr_type,
+                         **kwargs)
+
+        self.gs_bg_color=th.tensor([1,1,1], dtype=th.float32, device=dist_util.dev())
+        self.latent_name = 'latent_normalized'  # normalized triplane latent
+        # self.pcd_unnormalize_fn = lambda x: x.clip(-1,1) * 0.45 # [-1,1] -> [-0.45, 0.45] as in g-buffer dataset.
+        # self.pcd_unnormalize_fn = lambda x: (x * 0.1862).clip(-0.45, 0.45) # [-1,1] -> [-0.45, 0.45] as in g-buffer dataset.
+        
+        # /cpfs01/user/lanyushi.p/logs/nips24/LSGM/t23d/FM/9cls/gs/i23d/dit-b/gpu4-batch32-lr1e-4-gs_surf_latent_224-drop0.33-same
+        # self.pcd_unnormalize_fn = lambda x: (x * 0.158).clip(-0.45, 0.45) # [-1,1] -> [-0.45, 0.45] as in g-buffer dataset.
+
+        # self.feat_scale_factor = th.Tensor([0.99227685, 1.014337  , 0.20842505, 0.98727155, 0.3305389 ,
+        #     0.38729668, 1.0155401 , 0.9728264 , 1.0009694 , 0.97328585,
+        #     0.2881106 , 0.1652732 , 0.3482468 , 0.9971449 , 0.99895126,
+        #     0.18491288]).float().reshape(1,1,-1)
+
+        # stat for normalization
+        # self.xyz_mean = torch.Tensor([-0.00053714, 0.08095618, -0.01914407] ).reshape(1, 3).float()
+        # self.xyz_std = th.Tensor([0.14593576, 0.15753542, 0.18873914] ).reshape(1,3).float().to(dist_util.dev())
+        self.xyz_std = 0.164
+
+        # ! for debug
+        self.kl_mean = th.Tensor([ 0.0184,  0.0024,  0.0926,  0.0517,  0.1781,  0.7137, -0.0355,  0.0267,
+         0.0183,  0.0164, -0.5090,  0.2406,  0.2733, -0.0256, -0.0285,  0.0761]).reshape(1,16).float().to(dist_util.dev())
+        self.kl_std = th.Tensor([1.0018, 1.0309, 1.3001, 1.0160, 0.8182, 0.8023, 1.0591, 0.9789, 0.9966,
+        0.9448, 0.8908, 1.4595, 0.7957, 0.9871, 1.0236, 1.2923]).reshape(1,16).float().to(dist_util.dev())
+
+        # ! for surfel-gs rendering
+        self.zfar = 100.0
+        self.znear = 0.01
+
+    def unnormalize_pcd_act(self, x):
+        return x * self.xyz_std
+
+    def unnormalize_kl_feat(self, latent):
+        # return latent / self.feat_scale_factor
+        # return (latent-self.kl_mean) / self.kl_std
+        return (latent * self.kl_std) + self.kl_mean
+    
+    # def unnormalize_kl_feat(self, latent):
+    #     return latent * self.feat_scale_factor
+
+    @th.inference_mode()
+    def eval_cldm(
+        self,
+        prompt="Yellow rubber duck",
+        # use_ddim=False,
+        # unconditional_guidance_scale=1.0,
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        num_instances=1,
+        export_mesh=False,
+    ):
+        self.ddpm_model.eval()
+
+        # args = dnnlib.EasyDict(
+        #     dict(
+        #         batch_size=1,
+        #         image_size=self.diffusion_input_size,
+        #         denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+        #         out_chans,  # type: ignore
+        #         clip_denoised=False,
+        #         class_cond=False))
+
+        model_kwargs = {}
+
+        uc = None
+        log = dict()
+
+        ucg_keys = [self.cond_key] # i23d
+
+        if self.cond_key == 'caption':
+            if prompt == '':
+                batch = next(self.data) # random cond here
+                batch_c = {self.cond_key: prompt,
+                'fps-xyz': batch['fps-xyz'].to(self.dtype).to(dist_util.dev()), 
+                }
+            else:
+                # ! TODO, update the cascaded generation fps-xyz loading. Manual load for now.
+                batch_c = {
+                    self.cond_key: prompt
+                }
+                if self.latent_key == 'latent': # stage 2
+                    # hard-coded path for now
+                    # fps_xyz_output_prefix = '/nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/eval/clip_text/50w-iter/'
+                    # stage1_num_steps = '500000'
+
+                    batch = next(self.data) # random cond here
+                    batch_c[self.cond_key] = batch[self.cond_key][0:1] # colorizing GT xyz
+                    gt_xyz = batch['fps-xyz'][0:1]
+                    gt_kl_latent = batch['latent'][0:1]
+
+                    # cascaded = False
+                    # st()
+                    # if self.step % 1e4 == 0:
+                    #     cascaded = True
+                    # else:
+
+                    cascaded = False
+                    prompt = batch[self.cond_key][0:1] # replace with on-the-fly GT point clouds
+                    
+                    batch_c[self.cond_key] = prompt
+
+                    # ! for logging two-stage cascaded result. change the path to your stage-1 output pcd logdir.
+                    if cascaded: # ! use stage-1 as output
+                        # fps_xyz_output_prefix = '/nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/eval/clip_text/60w-iter/'
+                        fps_xyz_output_prefix = ''
+                        stage1_num_steps = '600000'
+
+                        fps_xyz = torch.from_numpy(pcu.load_mesh_v(f'{fps_xyz_output_prefix}/{stage1_num_steps}_0_{prompt}.ply') ).clip(-0.45,0.45).unsqueeze(0)
+
+                        batch_c.update({
+                            'fps-xyz': fps_xyz.to(self.dtype).to(dist_util.dev())
+                        })
+                    else:
+                        # use gt as condition
+                        # batch_c = {k: v[0:1].to(self.dtype).to(dist_util.dev()) for k, v in batch_c.items() if k in [self.cond_key, 'fps-xyz']}
+                        for k in ['fps-xyz']:
+                            batch_c[k] = batch[k][0:1].to(self.dtype).to(dist_util.dev())
+                        batch_c[self.cond_key] = prompt
+        else: 
+            batch = next(self.data) # random cond here
+
+            #! debugging, get GT xyz and KL latent for disentangled debugging
+
+            if self.cond_key == 'img-c':
+                prompt = batch['caption'][0:1]
+                batch_c = {
+                    self.cond_key: {
+                        'img': batch['mv_img'][0:1].to(self.dtype).to(dist_util.dev()),
+                        'c': batch['c'][0:1].to(self.dtype).to(dist_util.dev()),
+                    },
+                    'img': batch['img'][0:1].to(self.dtype).to(dist_util.dev()),
+                    'caption': prompt,
+                    'fps-xyz': batch['fps-xyz'][0:1].to(self.dtype).to(dist_util.dev())
+                }
+            
+            elif self.cond_key == 'img-caption':
+                batch_c = {'caption': prompt, 'img': batch['img'].to(dist_util.dev()).to(self.dtype)}
+
+            elif self.cond_key == 'img-xyz':
+                # load local xyz here
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/cpfs01/user/lanyushi.p/Repo/diffusion-3d/tmp/sampled-0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/cpfs01/user/lanyushi.p/Repo/diffusion-3d/tmp/sampled-2.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/cpfs01/user/lanyushi.p/Repo/diffusion-3d/tmp/sampled-1.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/cpfs01/user/lanyushi.p/Repo/diffusion-3d/tmp/sampled-3.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output_fullset_stillclip_but448_eval/1725000_0_0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output_fullset_stillclip_but448_eval/1725000_0_0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/eval/dino_img/debug/1875000_0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/eval/dino_img/debug/1875000_0_0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+
+                # ! edit
+                # st()
+                # fps_xyz[..., 2:3] *= 4
+                # fps_xyz[..., 2:3] *= 3
+
+                # fps_xyz = torch.from_numpy(pcu.load_mesh_v('/nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output_fullset_stillclip_but448_eval/1725000_0_1.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                batch_c = {
+                    # 'img': batch['img'][[1,0]].to(self.dtype).to(dist_util.dev()),
+                    'img': batch['img'][0:1].to(self.dtype).to(dist_util.dev()),
+                    'fps-xyz': batch['fps-xyz'][0:1].to(self.dtype).to(dist_util.dev()),
+                    # 'caption': batch['caption']
+                    # 'fps-xyz': fps_xyz.repeat(batch['img'].shape[0],1,1).to(self.dtype).to(dist_util.dev()),
+                }
+
+            else:
+
+                # gt_xyz = batch['fps-xyz'][0:1]
+                # gt_kl_latent = batch['latent'][0:1]
+
+                batch_c = {self.cond_key: batch[self.cond_key][0:1].to(dist_util.dev()).to(self.dtype), }
+
+        # swap for more results, hard-coded here.
+        # if 'img' in batch_c:
+        #     batch_c['img'] = batch_c['img'][[1,0]]
+            # batch_c['fps-xyz'] = batch_c['fps-xyz'][[1,0]]
+
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+
+            c, uc = self.conditioner.get_unconditional_conditioning(
+                batch_c,
+                force_uc_zero_embeddings=ucg_keys
+                if len(self.conditioner.embedders) > 0 else [],
+            )
+
+        sampling_kwargs = {}
+
+        N = num_samples  # hard coded, to update
+        z_shape = (N, 768, self.ddpm_model.in_channels)
+
+        for k in c:
+            if isinstance(c[k], th.Tensor):
+                # c[k], uc[k] = map(lambda y: y[k][:N].to(dist_util.dev()),
+                #                   (c, uc))
+                assert c[k].shape[0] == 1 # ! support batch inference
+                c[k], uc[k] = map(lambda y: y[k].repeat_interleave(N, 0).to(dist_util.dev()),
+                                (c, uc)) # support bs>1 sampling given a condition
+    
+        samples = self.sample(c,
+                              shape=z_shape[1:],
+                              uc=uc,
+                              batch_size=N,
+                              **sampling_kwargs)
+
+        # ! get c
+        if 'img' in self.cond_key:
+            img_save_path = f'{logger.get_dir()}/{self.step+self.resume_step}_imgcond.jpg'
+            if 'c' in self.cond_key:
+                mv_img_save_path = f'{logger.get_dir()}/{self.step+self.resume_step}_mv-imgcond.jpg'
+                torchvision.utils.save_image(batch_c['img-c']['img'][0], mv_img_save_path, value_range=(-1,1), normalize=True, padding=0) # torch.Size([24, 6, 3, 256, 256])
+                torchvision.utils.save_image(batch_c['img'][0], img_save_path, value_range=(-1,1), normalize=True, padding=0) # torch.Size([24, 6, 3, 256, 256])
+            else:
+                torchvision.utils.save_image(batch_c['img'], img_save_path, value_range=(-1,1), normalize=True, padding=0)
+
+        assert camera is not None
+        batch = {'c': camera.clone()}
+
+        # rendering
+        for i in range(samples.shape[0]):
+            th.cuda.empty_cache()
+
+            # ! render sampled latent
+            name_prefix = f'{self.step + self.resume_step}_{i}'
+
+            # if self.cond_key in ['caption', 'img-c']:
+            if self.cond_key in ['caption']:
+                if isinstance(prompt, list):
+                    name_prefix = f'{name_prefix}_{"-".join(prompt[0].split())}'
+                else:
+                    name_prefix = f'{name_prefix}_{"-".join(prompt.split())}'
+
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                        enabled=self.mp_trainer.use_amp):
+
+            #     # ! todo, transform to gs camera
+                if self.latent_key != 'latent': # normalized-xyz
+                    pcu.save_mesh_v( f'{logger.get_dir()}/{name_prefix}.ply', self.unnormalize_pcd_act(samples[i]).detach().cpu().float().numpy())
+                    logger.log(f'point cloud saved to {logger.get_dir()}/{name_prefix}.ply')
+                else:
+                    # ! editing debug
+                    self.render_gs_video_given_latent(
+                        # samples[i:i+1].to(self.dtype), # default version
+                        # th.cat([gt_kl_latent.to(samples), gt_xyz.to(samples)], dim=-1), 
+
+                        # ! xyz-cond kl feature gen:
+                        th.cat([samples[i:i+1], batch_c['fps-xyz'][i:i+1]], dim=-1), # ! debugging xyz diffusion
+
+                        # ! xyz debugging
+                        # th.cat([gt_kl_latent.to(samples), samples[i:i+1]], dim=-1), # ! debugging xyz diffusion
+                        # th.cat([samples[i:i+1], gt_xyz.to(samples), ], dim=-1) # ! debugging kl feature diffusion
+                        self.rec_model,  # compatible with join_model
+                        name_prefix=name_prefix,
+                        save_img=save_img,
+                        render_reference=batch,
+                        export_mesh=False)
+
+                # st()
+                pass
+
+                # for noise_scale in np.linspace(0,0.1, 10):
+                #     per_scale_name_prefix = f'{name_prefix}_{noise_scale}'
+                #     self.render_gs_video_given_latent( th.cat([gt_kl_latent.to(samples), (gt_xyz+noise_scale*th.randn_like(gt_xyz)).to(samples)], dim=-1), self.rec_model,  name_prefix=per_scale_name_prefix, save_img=save_img, render_reference=batch, export_mesh=False)
+                # self.render_gs_video_given_latent( th.cat([gt_kl_latent.to(samples), (batch_c['fps-xyz'][0:1]).to(samples)], dim=-1), self.rec_model,  name_prefix=per_scale_name_prefix, save_img=save_img, render_reference=batch, export_mesh=False)
+
+                # pcu.save_mesh_v( f'{logger.get_dir()}/sampled-4.ply', self.unnormalize_pcd_act(samples[0]).detach().cpu().float().numpy())
+                # st()
+                # pcu.save_mesh_v( f'tmp/sampled-3.ply', self.unnormalize_pcd_act(samples[0]).detach().cpu().float().numpy())
+
+        gc.collect()
+        self.ddpm_model.train()
+
+    @torch.no_grad()
+    def export_mesh_from_2dgs(self, all_rgbs, all_depths, all_alphas, cam_pathes, idx, i):
+        # https://github.com/autonomousvision/LaRa/blob/main/evaluation.py
+        n_thread = 1 # avoid TSDF cpu hanging bug.
+        os.environ["MKL_NUM_THREADS"] = f"{n_thread}" 
+        os.environ["NUMEXPR_NUM_THREADS"] = f"{n_thread}" 
+        os.environ["OMP_NUM_THREADS"] = f"4" 
+        os.environ["VECLIB_MAXIMUM_THREADS"] = f"{n_thread}" 
+        os.environ["OPENBLAS_NUM_THREADS"] = f"{n_thread}" 
+
+        # copied from: https://github.com/hbb1/2d-gaussian-splatting/blob/19eb5f1e091a582e911b4282fe2832bac4c89f0f/render.py#L23
+        logger.log("exporting mesh ...")
+        # os.makedirs(train_dir, exist_ok=True)
+        train_dir = logger.get_dir()
+
+        # for g-objv
+        # aabb = [-0.5,-0.5,-0.5,0.5,0.5,0.5]
+        # aabb = None
+        aabb = [-0.45,-0.45,-0.45,0.45,0.45,0.45]
+        self.aabb = np.array(aabb).reshape(2,3)*1.1
+
+        # center = self.aabb.mean(0)
+        # radius = np.linalg.norm(self.aabb[1] - self.aabb[0]) * 0.5
+        # voxel_size = radius / 256
+        # sdf_trunc = voxel_size * 2
+        # print("using aabb")
+
+        # set the active_sh to 0 to export only diffuse texture
+
+        # gaussExtractor.gaussians.active_sh_degree = 0
+        # gaussExtractor.reconstruction(scene.getTrainCameras())
+
+        # extract the mesh and save
+        # if args.unbounded:
+        #     name = 'fuse_unbounded.ply'
+        #     mesh = gaussExtractor.extract_mesh_unbounded(resolution=args.mesh_res)
+        # else:
+
+        # name = f'{idx}-{i}-fuse.ply'
+        # name = f'mesh.obj'
+        name = f'{idx}/mesh_raw.obj'
+        # st()
+        # depth_trunc = (radius * 2.0) if depth_trunc < 0  else depth_trunc
+        # voxel_size = (depth_trunc / mesh_res) if voxel_size < 0 else voxel_size
+        # sdf_trunc = 5.0 * voxel_size if sdf_trunc < 0 else sdf_trunc
+        # mesh = self.extract_mesh_bounded(all_rgbs, all_depths, all_alphas, cam_pathes, voxel_size=voxel_size, sdf_trunc=sdf_trunc, depth_trunc=depth_trunc, mask_backgrond=False)
+        mesh = self.extract_mesh_bounded(all_rgbs, all_depths, all_alphas, cam_pathes)
+        
+        o3d.io.write_triangle_mesh(os.path.join(train_dir, name), mesh)
+        logger.log("mesh saved at {}".format(os.path.join(train_dir, name)))
+        # post-process the mesh and save, saving the largest N clusters
+        # mesh_post = post_process_mesh(mesh, cluster_to_keep=num_cluster)
+        mesh_post = post_process_mesh(mesh)
+        # o3d.io.write_triangle_mesh(os.path.join(train_dir, name.replace('.obj', '_post.obj')), mesh_post)
+
+        mesh_vertices = np.asarray(mesh_post.vertices)  # Convert vertices to a numpy array
+        rotated_vertices = mesh_vertices @ rotation_matrix_x(-90).T
+        mesh_post.vertices = o3d.utility.Vector3dVector(rotated_vertices)  # Update vertices
+        post_mesh_path = os.path.join(train_dir, name.replace('_raw.obj', '.obj'))
+
+        o3d.io.write_triangle_mesh(post_mesh_path, mesh_post)
+
+        logger.log("mesh post processed saved at {}".format(post_mesh_path))
+        return post_mesh_path
+
+
+    @torch.no_grad()
+    def extract_mesh_bounded(self, rgbmaps, depthmaps, alpha_maps, cam_pathes, voxel_size=0.004, sdf_trunc=0.02, depth_trunc=3, alpha_thres=0.08, mask_backgrond=False):
+        """
+        Perform TSDF fusion given a fixed depth range, used in the paper.
+        
+        voxel_size: the voxel size of the volume
+        sdf_trunc: truncation value
+        depth_trunc: maximum depth range, should depended on the scene's scales
+        mask_backgrond: whether to mask backgroud, only works when the dataset have masks
+
+        return o3d.mesh
+        """
+
+        if self.aabb is not None: # as in lara.
+            center = self.aabb.mean(0)
+            # radius = np.linalg.norm(self.aabb[1] - self.aabb[0]) * 0.5
+            radius = np.linalg.norm(self.aabb[1] - self.aabb[0]) * 0.5
+            # voxel_size = radius / 256
+            # voxel_size = radius / 192 # less holes
+            voxel_size = radius / 160 # smaller
+            # sdf_trunc = voxel_size * 16 # less holes, slower integration
+            sdf_trunc = voxel_size * 12 # 
+            print("using aabb")
+
+        volume = o3d.pipelines.integration.ScalableTSDFVolume(
+            voxel_length= voxel_size,
+            sdf_trunc=sdf_trunc,
+            color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8
+        )
+
+        print("Running tsdf volume integration ...")
+        print(f'voxel_size: {voxel_size}')
+        print(f'sdf_trunc: {sdf_trunc}')
+        print(f'depth_truc: {depth_trunc}')
+
+        # render_reference = th.load('eval_pose.pt', map_location='cpu').numpy()
+
+        # ! use uni_mesh_path, from Lara, Chen et al, ECCV 24'
+
+        # '''
+
+
+        # for i, cam_o3d in tqdm(enumerate(to_cam_open3d(self.viewpoint_stack)), desc="TSDF integration progress"):
+        for i, cam in tqdm(enumerate(cam_pathes), desc="TSDF integration progress"):
+            # rgb = self.rgbmaps[i]
+            # depth = self.depthmaps[i]
+            cam = self.c_to_3dgs_format(cam)
+            cam_o3d = to_cam_open3d_compat(cam)
+
+            rgb = rgbmaps[i][0]
+            depth = depthmaps[i][0]
+            alpha = alpha_maps[i][0]
+
+            # if we have mask provided, use it
+            # if mask_backgrond and (self.viewpoint_stack[i].gt_alpha_mask is not None):
+            #     depth[(self.viewpoint_stack[i].gt_alpha_mask < 0.5)] = 0
+            
+            depth[(alpha < alpha_thres)] = 0
+            if self.aabb is not None:
+                campos = cam['cam_pos'].cpu().numpy()
+                depth_trunc = np.linalg.norm(campos - center, axis=-1) + radius
+
+            # make open3d rgbd
+            rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
+                o3d.geometry.Image(np.asarray(np.clip(rgb.permute(1,2,0).cpu().numpy(), 0.0, 1.0) * 255, order="C", dtype=np.uint8)),
+                o3d.geometry.Image(np.asarray(depth.permute(1,2,0).cpu().numpy(), order="C")),
+                depth_trunc = depth_trunc, 
+                convert_rgb_to_intensity=False,
+                depth_scale = 1.0
+            )
+
+            volume.integrate(rgbd, intrinsic=cam_o3d.intrinsic, extrinsic=cam_o3d.extrinsic)
+
+        mesh = volume.extract_triangle_mesh()
+        return mesh
+
+
+    @th.inference_mode()
+    def render_gs_video_given_latent(self,
+                                    planes,
+                                    rec_model,
+                                    name_prefix='0',
+                                    save_img=False, 
+                                    render_reference=None, 
+                                    export_mesh=False, 
+                                    output_dir=None, 
+                                    for_fid=False,):
+
+        batch_size, L, C = planes.shape
+
+        # ddpm_latent = { self.latent_name: planes[..., :-3] * self.feat_scale_factor.to(planes),  # kl-reg latent
+        #                 'query_pcd_xyz': self.pcd_unnormalize_fn(planes[..., -3:]) }
+
+        # ddpm_latent = { self.latent_name: self.unnormalize_kl_feat(planes[..., :-3]),  # kl-reg latent
+        # ddpm_latent = { self.latent_name: planes[..., :-3],  # kl-reg latent
+        #                     'query_pcd_xyz': self.unnormalize_pcd_act(planes[..., -3:]) }
+
+        ddpm_latent = { self.latent_name: planes[..., :-3],  # kl-reg latent
+                            'query_pcd_xyz': planes[..., -3:]}
+        
+        ddpm_latent.update(rec_model(latent=ddpm_latent, behaviour='decode_gs_after_vae_no_render')) 
+
+        # ! editing debug, raw scaling
+        
+        # for beacon
+        # edited_fps_xyz[..., 2] *= 1.5
+        # edited_fps_xyz[..., :2] *= 0.75
+
+        # z_mask = edited_fps_xyz[..., 2] > 0
+        # edited_fps_xyz[..., 2] *= 1.25 # only apply to upper points
+
+        # z_dim_coord = edited_fps_xyz[..., 2]
+        # edited_fps_xyz[..., 2] = th.where(z_dim_coord>0, z_dim_coord*1.25, z_dim_coord)
+        # edited_fps_xyz[..., :2] *= 0.6
+
+        fine_scale = 'gaussians_upsampled_3'
+        # ddpm_latent[fine_scale][..., :2] *= 1.5
+        # ddpm_latent[fine_scale][..., 2:3] *= 0.75
+
+        # ddpm_latent[fine_scale][..., :2] *= 3
+        # ddpm_latent[fine_scale][..., 2:3] *= 0.75
+
+        # z_dim_coord = ddpm_latent[fine_scale][..., 2]
+        # ddpm_latent[fine_scale][..., 2] = th.where(z_dim_coord>0.24, z_dim_coord+0.1, z_dim_coord)
+
+        # pcu.save_mesh_v(f'{output_dir}/gaussian.ply', ddpm_latent['gaussians_upsampled'][0, ..., :3].cpu().numpy())
+        # fps-downsampling?
+        pred_gaussians_xyz = ddpm_latent['gaussians_upsampled_3'][..., :3]
+        fine_gs = ddpm_latent[fine_scale]
+
+        # K=4096
+        # query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+        #     pred_gaussians_xyz, K=K,
+        #     # random_start_point=False)  # B self.latent_num
+        #     random_start_point=True)  # B self.latent_num
+
+        if output_dir is None:
+            output_dir = logger.get_dir()
+
+        # pcu.save_mesh_v(f'{output_dir}/{name_prefix}-gaussian-{K}.ply', query_pcd_xyz[0].cpu().numpy())
+
+        np.save(f'{output_dir}/{name_prefix}-gaussian.npy', fine_gs.cpu().numpy()[0]) # L, 13
+
+        # also save rgb point cloud
+        fine_gs_numpy = fine_gs.cpu().numpy()
+        rgb_xyz_path = f'{output_dir}/{name_prefix}-gaussian-pcd.glb'
+        # pcu.save_mesh_vc(rgb_xyz_path, v=fine_gs_numpy[..., :3], c=fine_gs_numpy[..., 3:6])
+        vtx = np.transpose(rotation_matrix_x(-90) @ np.transpose(fine_gs_numpy[0, :, :3]))
+        cloud = trimesh.PointCloud(vtx, colors=fine_gs_numpy[0, :, 10:13])
+        # Save the point cloud to an OBJ file
+        _ = cloud.export(rgb_xyz_path)
+
+        # return None, None
+        video_path = f'{output_dir}/{name_prefix}-gs.mp4'
+
+        try:
+            # video_out = imageio.get_writer(
+            #     f'{output_dir}/gs_{name_prefix}.mp4',
+            #     mode='I',
+            #     fps=15,
+            #     codec='libx264')
+
+            video_out = imageio.get_writer(
+                video_path,
+                mode='I',
+                fps=15,
+                codec='libx264')
+
+        except Exception as e:
+            logger.log(e)
+
+            # return # some caption are too tired and cannot be parsed as file name
+
+        # !for FID
+
+        ''' # if for uniform FID rendering. Will not adopt this later.
+        azimuths = []
+        elevations = []
+        frame_number = 10
+
+        for i in range(frame_number): # 1030 * 5 * 10, for FID 50K
+
+            azi, elevation = sample_uniform_cameras_on_sphere()
+            # azi, elevation = azi[0] / np.pi * 180, elevation[0] / np.pi * 180
+            # azi, elevation = azi[0] / np.pi * 180, 0
+            azi, elevation = azi[0] / np.pi * 180, (elevation[0]-np.pi*0.5) / np.pi * 180 # [-0.5 pi, 0.5 pi]
+            azimuths.append(azi)
+            elevations.append(elevation)
+
+        azimuths = np.array(azimuths)
+        elevations = np.array(elevations)
+
+        # azimuths = np.array(list(range(0,360,30))).astype(float)
+        # frame_number = azimuths.shape[0]
+        # elevations = np.array([10]*azimuths.shape[0]).astype(float)
+
+        zero123pp_pose, _ = generate_input_camera(1.8, [[elevations[i], azimuths[i]] for i in range(frame_number)], fov=30)
+        K = th.Tensor([1.3889, 0.0000, 0.5000, 0.0000, 1.3889, 0.5000, 0.0000, 0.0000, 0.0039]).to(zero123pp_pose) # keeps the same
+        render_reference = th.cat([zero123pp_pose.reshape(frame_number,-1), K.unsqueeze(0).repeat(frame_number,1)], dim=-1).cpu().numpy()
+        '''
+
+        assert render_reference is not None
+        # render_reference = th.load('eval_pose.pt', map_location='cpu').numpy()[:24]
+
+        # rand_start_idx = random.randint(0,2)
+        # render_reference = render_reference[rand_start_idx::3] # randomly render 8 views, maintain fixed azimuths
+        # assert len(render_reference)==8
+
+        # assert render_reference is None
+            # render_reference = self.eval_data # compat
+        # else: # use train_traj
+
+        # for key in ['ins', 'bbox', 'caption']:
+        #     if key in render_reference:
+        #         render_reference.pop(key)
+
+        # render_reference = [ { k:v[idx:idx+1] for k, v in render_reference.items() } for idx in range(40) ]
+        all_rgbs, all_depths, all_alphas = [], [], []
+
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+        for i, micro_c in enumerate(tqdm(render_reference)):
+            # micro = {
+            #     k: v.to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+            #     for k, v in batch.items() 
+            # }
+
+            # c = self.eval_data.post_process.c_to_3dgs_format(micro_c)
+            c = self.c_to_3dgs_format(micro_c)
+            for k in c.keys(): # to cuda
+                if isinstance(c[k], th.Tensor) and k != 'tanfov':
+                    c[k] = c[k].unsqueeze(0).unsqueeze(0).to(dist_util.dev()) # actually, could render 40 views together.
+            c['tanfov'] = th.tensor(c['tanfov']).to(dist_util.dev())
+
+            pred = rec_model(
+                img=None,
+                c=c, # TODO, to dict
+                latent=ddpm_latent, # render gs
+                behaviour='triplane_dec',
+                bg_color=self.gs_bg_color,
+                render_all_scale=True, # for better visualization
+                )
+            
+            # ! if visualizing a single scale
+            fine_scale_key = list(pred.keys())[-1]
+
+            all_rgbs.append(einops.rearrange(pred[fine_scale_key]['image'], 'B V ... -> (B V) ...'))
+            all_depths.append(einops.rearrange(pred[fine_scale_key]['depth'], 'B V ... -> (B V) ...'))
+            all_alphas.append(einops.rearrange(pred[fine_scale_key]['alpha'], 'B V ... -> (B V) ...'))
+
+            all_pred_vis = {}
+            # for key in pred.keys():
+            # st()
+            for key in ['gaussians_base', fine_scale_key]: # only show two LoDs
+                pred_scale = pred[key] # only show finest result here
+                for k in pred_scale.keys():
+                    pred_scale[k] = einops.rearrange(pred_scale[k], 'B V ... -> (B V) ...') # merge 
+                
+                pred_vis = self._make_vis_img(pred_scale)
+
+                vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+                vis = vis * 127.5 + 127.5
+                vis = vis.clip(0, 255).astype(np.uint8)
+
+                all_pred_vis[key] = vis
+
+            all_pred_vis_concat = np.concatenate([cv2.resize(all_pred_vis[k][0], (512*3, 512)) for k in all_pred_vis.keys()], axis=0)
+
+            video_out.append_data(all_pred_vis_concat)
+
+        if save_img: # for fid 
+            for idx in range(len(all_rgbs)):
+                sampled_img = Image.fromarray(
+                    (all_rgbs[idx][0].permute(1, 2, 0).cpu().numpy() *
+                        255).clip(0, 255).astype(np.uint8))
+                sampled_img.save(os.path.join(output_dir,f'{name_prefix}-{idx}.jpg'))
+
+
+        # if not save_img:
+        video_out.close()
+        print('logged video to: ',
+            f'{output_dir}/{name_prefix}.mp4')
+
+        del video_out, pred, pred_vis, vis
+        return all_rgbs, all_depths, all_alphas, video_path, rgb_xyz_path
+
+    @th.no_grad()
+    def _make_vis_img(self, pred):
+
+        # if True:
+        pred_depth = pred['image_depth']
+        pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                        pred_depth.min())
+
+        pred_depth = pred_depth.cpu()[0].permute(1, 2, 0).numpy()
+        pred_depth = (plt.cm.viridis(pred_depth[..., 0])[..., :3]) * 2 - 1
+        pred_depth = th.from_numpy(pred_depth).to(
+            pred['image_raw'].device).permute(2, 0, 1).unsqueeze(0)
+
+        gen_img = pred['image_raw']
+        rend_normal = pred['rend_normal']
+
+        pred_vis = th.cat(
+            [
+                gen_img,
+                rend_normal,
+                pred_depth,
+            ],
+            dim=-1)  # B, 3, H, W
+        
+        return pred_vis
+
+
+    def _set_grad_flag(self):    
+        requires_grad(self.ddpm_model, True) # 
+
+    @th.inference_mode()
+    def sample_and_save(self, batch_c, ucg_keys, num_samples, camera, save_img, idx=0, save_dir='', export_mesh=False, stage1_idx=0, cfg_scale=4.0, seed=42):
+
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+
+            c, uc = self.conditioner.get_unconditional_conditioning(
+                batch_c,
+                force_uc_zero_embeddings=ucg_keys
+                if len(self.conditioner.embedders) > 0 else [],
+            )
+
+        sampling_kwargs = {
+            'cfg_scale': cfg_scale, # default value in SiT
+            'seed': seed,
+        }
+
+        N = num_samples  # hard coded, to update
+        z_shape = (N, 768, self.ddpm_model.in_channels)
+
+        for k in c:
+            if isinstance(c[k], th.Tensor):
+                # c[k], uc[k] = map(lambda y: y[k][:N].to(dist_util.dev()),
+                #                   (c, uc))
+                assert c[k].shape[0] == 1 # ! support batch inference
+                c[k], uc[k] = map(lambda y: y[k].repeat_interleave(N, 0).to(dist_util.dev()),
+                                (c, uc)) # support bs>1 sampling given a condition
+    
+        samples = self.sample(c,
+                            shape=z_shape[1:],
+                            uc=uc,
+                            batch_size=N,
+                            **sampling_kwargs)
+
+        # ! get c
+        if save_dir == '':
+            save_dir = logger.get_dir()
+
+        if 'img' in self.cond_key:
+            # img_save_path = f'{save_dir}/{idx}_imgcond.jpg'
+            img_save_path = f'{save_dir}/{idx}/imgcond.jpg'
+            os.makedirs(f'{save_dir}/{idx}', exist_ok=True)
+            if 'c' in self.cond_key:
+                torchvision.utils.save_image(batch_c['img-c']['img'][0], img_save_path, value_range=(-1,1), normalize=True, padding=0) # torch.Size([24, 6, 3, 256, 256])
+            else:
+                torchvision.utils.save_image(batch_c['img'], img_save_path, value_range=(-1,1), normalize=True, padding=0)
+
+        assert camera is not None
+        # batch = {'c': camera.clone()}
+
+        # rendering
+        for i in range(samples.shape[0]):
+            th.cuda.empty_cache()
+
+            if self.cond_key in ['caption']:
+                name_prefix = f'{batch_c["caption"]}_sample-{stage1_idx}-{i}'
+            else:
+                # ! render sampled latent
+                # name_prefix = f'{idx}_sample-{i}'
+                name_prefix = f'{idx}/sample-{stage1_idx}-{i}'
+
+            # if self.cond_key in ['caption', 'img-c']:
+            cam_pathes = uni_mesh_path(10)
+
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                        enabled=self.mp_trainer.use_amp):
+
+            #     # ! todo, transform to gs camera
+                if self.latent_key != 'latent': # normalized-xyz
+
+                    pcd_export_dir = f'{save_dir}/{name_prefix}.glb' # pcu fails on py=3.9
+                    vtx = self.unnormalize_pcd_act(samples[i]).detach().cpu().float().numpy()
+                    cloud = trimesh.PointCloud(vtx @ rotation_matrix_x(-90).T, colors=np.ones_like(vtx)*0.75)
+                    _ = cloud.export(pcd_export_dir) # for gradio display
+                    logger.log(f'stage-1 glb point cloud saved to {pcd_export_dir}')
+
+                    pcd_export_dir_forstage1 = f'{save_dir}/{name_prefix}.ply' # pcu fails on py=3.9
+                    cloud = trimesh.PointCloud(vtx)
+                    _ = cloud.export(pcd_export_dir_forstage1) # for stage-1 cascaded loading.
+
+                    logger.log(f'point cloud saved to {pcd_export_dir_forstage1}')
+                    return pcd_export_dir
+                else:
+                    # ! editing debug
+                    all_rgbs, all_depths, all_alphas, video_path, rgb_xyz_path = self.render_gs_video_given_latent(
+                        th.cat([samples[i:i+1], batch_c['fps-xyz'][0:1]], dim=-1), # ! debugging xyz diffusion
+                        self.rec_model,  # compatible with join_model
+                        name_prefix=name_prefix,
+                        save_img=save_img,
+                        render_reference=cam_pathes,
+                        export_mesh=False,)
+                        # for_fid=False)
+
+                    if export_mesh:
+                        post_mesh_path=self.export_mesh_from_2dgs(all_rgbs, all_depths, all_alphas, cam_pathes, idx, i)
+                    else:
+                        post_mesh_path = ''
+
+                    return video_path, rgb_xyz_path, post_mesh_path
+
+        # mesh = self.extract_mesh_bounded(all_rgbs, all_depths, all_alphas, cam_pathes, voxel_size=voxel_size, sdf_trunc=sdf_trunc, depth_trunc=depth_trunc, mask_backgrond=False)
+
+
+    @th.inference_mode()
+    def eval_and_export(
+        self,
+        prompt="Yellow rubber duck",
+        # use_ddim=False,
+        # unconditional_guidance_scale=1.0,
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        stage_1_output_dir='',
+        num_instances=1,
+        export_mesh=False,
+    ):
+        self.ddpm_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False))
+
+        model_kwargs = {}
+
+        uc = None
+        log = dict()
+
+        ucg_keys = [self.cond_key] # i23d
+
+        def sample_and_save(batch_c, idx=0):
+
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                        enabled=self.mp_trainer.use_amp):
+
+                c, uc = self.conditioner.get_unconditional_conditioning(
+                    batch_c,
+                    force_uc_zero_embeddings=ucg_keys
+                    if len(self.conditioner.embedders) > 0 else [],
+                )
+
+            sampling_kwargs = {}
+
+            N = num_samples  # hard coded, to update
+            z_shape = (N, 768, self.ddpm_model.in_channels)
+
+            for k in c:
+                if isinstance(c[k], th.Tensor):
+                    # c[k], uc[k] = map(lambda y: y[k][:N].to(dist_util.dev()),
+                    #                   (c, uc))
+                    assert c[k].shape[0] == 1 # ! support batch inference
+                    c[k], uc[k] = map(lambda y: y[k].repeat_interleave(N, 0).to(dist_util.dev()),
+                                    (c, uc)) # support bs>1 sampling given a condition
+        
+            samples = self.sample(c,
+                                shape=z_shape[1:],
+                                uc=uc,
+                                batch_size=N,
+                                **sampling_kwargs)
+
+            # ! get c
+            if 'img' in self.cond_key:
+                img_save_path = f'{logger.get_dir()}/{idx}_imgcond.jpg'
+                if 'c' in self.cond_key:
+                    torchvision.utils.save_image(batch_c['img-c']['img'][0], img_save_path, value_range=(-1,1), normalize=True, padding=0) # torch.Size([24, 6, 3, 256, 256])
+                else:
+                    torchvision.utils.save_image(batch_c['img'], img_save_path, value_range=(-1,1), normalize=True, padding=0)
+
+            assert camera is not None
+            batch = {'c': camera.clone()}
+
+            # rendering
+            for i in range(samples.shape[0]):
+                th.cuda.empty_cache()
+
+                if self.cond_key in ['caption']:
+                    name_prefix = f'{batch_c["caption"]}_sample-{idx}-{i}'
+                else:
+                    # ! render sampled latent
+                    name_prefix = f'{idx}_sample-{i}'
+
+                # if self.cond_key in ['caption', 'img-c']:
+
+                with th.cuda.amp.autocast(dtype=self.dtype,
+                                            enabled=self.mp_trainer.use_amp):
+
+                #     # ! todo, transform to gs camera
+                    if self.latent_key != 'latent': # normalized-xyz
+                        stage1_pcd_path = f'{logger.get_dir()}/{name_prefix}.glb'
+                        pcu.save_mesh_v( stage1_pcd_path, self.unnormalize_pcd_act(samples[i]).detach().cpu().float().numpy())
+                        logger.log(f'point cloud saved to {stage1_pcd_path}')
+                    else:
+                        # ! editing debug
+                        all_rgbs, all_depths, all_alphas, _, _ = self.render_gs_video_given_latent(
+                            # samples[i:i+1].to(self.dtype), # default version
+                            # th.cat([gt_kl_latent.to(samples), gt_xyz.to(samples)], dim=-1), 
+
+                            # ! xyz-cond kl feature gen:
+                            # th.cat([samples[i:i+1], batch_c['fps-xyz'][i:i+1]], dim=-1), # ! debugging xyz diffusion
+                            th.cat([samples[i:i+1], batch_c['fps-xyz'][0:1]], dim=-1), # ! debugging xyz diffusion
+
+                            # ! xyz debugging
+                            # th.cat([gt_kl_latent.to(samples), samples[i:i+1]], dim=-1), # ! debugging xyz diffusion
+                            # th.cat([samples[i:i+1], gt_xyz.to(samples), ], dim=-1) # ! debugging kl feature diffusion
+                            self.rec_model,  # compatible with join_model
+                            name_prefix=name_prefix,
+                            save_img=save_img,
+                            render_reference=batch,
+                            export_mesh=False)
+
+                        # if export_mesh:
+                        #     self.export_mesh_from_2dgs(all_rgbs, all_depths, idx, i)
+
+        if self.cond_key == 'caption':
+            assert prompt != ''
+            batch_c = {self.cond_key: prompt}
+
+            if self.latent_key == 'latent': # t23d, stage-2
+                for i in range(2): # 8 * num_samples here
+                    fps_xyz = torch.from_numpy(pcu.load_mesh_v(f'{stage_1_output_dir}/{prompt}_sample-0-{i}.ply') ).clip(-0.45,0.45).unsqueeze(0)
+
+                    # ! if editing, change the latent points accordingly.
+                    # edited_fps_xyz = fps_xyz.clone() # B N 3
+                    # z_dim_coord = edited_fps_xyz[..., 2]
+                    # edited_fps_xyz[..., 2] = th.where(z_dim_coord>0.24, z_dim_coord+0.075, z_dim_coord)
+
+                    batch_c.update({
+                        'fps-xyz': fps_xyz.to(self.dtype).to(dist_util.dev())
+                        # 'fps-xyz': edited_fps_xyz.to(self.dtype).to(dist_util.dev())
+                    })
+
+                    sample_and_save(batch_c, idx=i)
+            else:
+                sample_and_save(batch_c)
+            
+
+
+    @th.inference_mode()
+    def eval_t23d_and_export(
+        self,
+        prompt="Yellow rubber duck",
+        # use_ddim=False,
+        # unconditional_guidance_scale=1.0,
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        stage_1_output_dir='',
+        num_instances=1,
+        export_mesh=False,
+    ):
+        self.ddpm_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False))
+
+        model_kwargs = {}
+
+        uc = None
+        log = dict()
+
+        ucg_keys = [self.cond_key] # i23d
+
+        assert self.cond_key == 'caption' and prompt != ''
+        batch_c = {self.cond_key: prompt}
+
+        if self.latent_key == 'latent': # t23d, stage-2
+            fps_xyz = torch.from_numpy(pcu.load_mesh_v(f'{stage_1_output_dir}/{prompt}_sample-0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+            batch_c.update({
+                'fps-xyz': fps_xyz.to(self.dtype).to(dist_util.dev())
+            })
+
+        self.sample_and_save(batch_c, ucg_keys, num_samples, camera,)
+
+
+    @th.inference_mode()
+    def eval_i23d_and_export(
+        self,
+        prompt="Yellow rubber duck",
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        stage_1_output_dir='',
+        num_instances=1,
+        export_mesh=False,
+    ):
+        self.ddpm_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False))
+
+        model_kwargs = {}
+
+        uc = None
+        log = dict()
+
+        ucg_keys = [self.cond_key] # i23d
+
+
+        for idx, batch in enumerate(tqdm(self.data)):
+
+            ins = batch['ins'][0]
+
+            # obj_folder, _, frame = ins.split('/')
+            ins = ins.split('/')
+            # obj_folder, frame = ins[0], ins[-1] # for gso
+
+            if len(ins) >2:
+                if ins[1] == 'render_mvs_25': # gso
+                    obj_folder, frame = ins[0], int(ins[-1].split('.')[0])
+                    ins_name = f'{obj_folder}/{str(frame)}'
+                else:
+                    obj_folder, frame = os.path.join(ins[1], ins[2]), ins[-1] # for objv
+                    frame = int(frame.split('.')[0])
+                    ins_name = f'{obj_folder}/{str(frame)}'
+            else: # folder of images, e.g., instantmesh
+                ins_name = ins[0].split('.')[0]
+
+
+            # pcd_export_dir = f'{logger.get_dir()}/{ins_name}/sample-0.ply'
+
+            # if os.path.exists(pcd_export_dir):
+            #     continue
+
+            #! debugging, get GT xyz and KL latent for disentangled debugging
+
+            if self.cond_key == 'img-c': # mv23d
+                prompt = batch['caption'][0:1]
+                batch_c = {
+                    self.cond_key: {
+                        'img': batch['mv_img'][0:1].to(self.dtype).to(dist_util.dev()),
+                        'c': batch['c'][0:1].to(self.dtype).to(dist_util.dev()),
+                    },
+                    'img': batch['img'][0:1].to(self.dtype).to(dist_util.dev()),
+                    'caption': prompt,
+                }
+
+                if self.latent_key == 'latent': # stage-2
+                    fps_xyz = torch.from_numpy(pcu.load_mesh_v(f'{stage_1_output_dir}/{idx}_sample-0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                    # fps_xyz = torch.from_numpy(pcu.load_mesh_v(f'{stage_1_output_dir}/sample-0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                    batch_c.update({
+                        'fps-xyz': fps_xyz[0:1].to(self.dtype).to(dist_util.dev()),
+                    })
+            
+            # elif self.cond_key == 'img-caption':
+            #     batch_c = {'caption': prompt, 'img': batch['img'].to(dist_util.dev()).to(self.dtype)}
+
+            elif self.cond_key == 'img-xyz': # stage-2
+
+                for i in range(2):
+
+                    stage1_pcd_output_path = f'{stage_1_output_dir}/{ins_name}/sample-0-{i}.ply'
+
+                    fps_xyz = trimesh.load(stage1_pcd_output_path).vertices # pcu may fail on py=3.9
+                    fps_xyz = torch.from_numpy(fps_xyz).clip(-0.45,0.45).unsqueeze(0)
+
+                    # fps_xyz = None # ! TODO, load from local directory
+                    batch_c = {
+                        'img': batch['img'][0:1].to(self.dtype).to(dist_util.dev()),
+                        'fps-xyz': fps_xyz[0:1].to(self.dtype).to(dist_util.dev()),
+                    }
+                    
+                    self.sample_and_save(batch_c, ucg_keys, num_samples, camera, save_img, idx=ins_name, export_mesh=export_mesh, stage1_idx=i) # type: ignore
+
+            else: # stage-1 data
+                batch_c = {self.cond_key: batch[self.cond_key][0:1].to(dist_util.dev()).to(self.dtype), }
+                if self.cond_key == 'caption' and self.latent_key == 'latent': # t23d, stage-2
+                    fps_xyz = torch.from_numpy(pcu.load_mesh_v(f'{stage_1_output_dir}/{idx}_sample-0.ply') ).clip(-0.45,0.45).unsqueeze(0)
+                    batch_c.update({
+                        'fps-xyz': fps_xyz.to(self.dtype).to(dist_util.dev())
+                    })
+
+
+            # save_dir = f'{logger.get_dir()}/{ins}'
+            # os.mkdir(save_dir, exists_ok=True, parents=True)
+
+            # self.sample_and_save(batch_c, ucg_keys, num_samples, camera, save_img, idx=f'{idx}-{ins}', export_mesh=export_mesh)
+                self.sample_and_save(batch_c, ucg_keys, num_samples, camera, save_img, idx=ins_name, export_mesh=export_mesh) # type: ignore
+
+
+        gc.collect()
+
+    @th.inference_mode()
+    def eval_i23d_and_export_gradio(
+        self,
+        inp_img,
+        seed=42,
+        cfg_scale=4.0, # default value in neural ode
+        save_img=False,
+        **kwargs,
+    ):
+
+        # logger.log(
+        #     unconditional_guidance_scale,
+        #     seed,
+        #     mesh_size,
+        #     mesh_thres,
+        # )
+
+        sampling_kwargs = {
+            'cfg_scale': cfg_scale, # default value in SiT
+            'seed': seed,
+        }
+
+        camera = th.load('assets/objv_eval_pose.pt', map_location=dist_util.dev())[:24]
+        inp_img = th.from_numpy(inp_img).permute(2,0,1).unsqueeze(0) / 127.5 - 1 # to [-1,1]
+
+        num_samples=1
+        export_mesh=True
+
+        # self.ddpm_model.eval()
+
+        # args = dnnlib.EasyDict(
+        #     dict(
+        #         batch_size=1,
+        #         image_size=self.diffusion_input_size,
+        #         denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+        #         out_chans,  # type: ignore
+        #         clip_denoised=False,
+        #         class_cond=False))
+
+        # model_kwargs = {}
+
+        ucg_keys = [self.cond_key] # i23d
+
+        ins_name = 'house2-input' # for debug here
+
+        if self.cond_key == 'img-xyz': # stage-2
+
+            i = 0 # for gradio only
+            # for i in range(1):
+            stage_1_output_dir="./logs/i23d/stage-1/dino_img/"
+            stage1_pcd_output_path = f'{stage_1_output_dir}/{ins_name}/sample-0-{i}.ply'
+
+            fps_xyz = trimesh.load(stage1_pcd_output_path).vertices # pcu may fail on py=3.9
+            fps_xyz = torch.from_numpy(fps_xyz).clip(-0.45,0.45).unsqueeze(0)
+
+            logger.log('loading stage-1 point cloud from: ', stage1_pcd_output_path)
+
+            # fps_xyz = None # ! TODO, load from local directory
+            # batch_c = {
+            #     'img': batch['img'][0:1].to(self.dtype).to(dist_util.dev()),
+            #     'fps-xyz': fps_xyz[0:1].to(self.dtype).to(dist_util.dev()),
+            # }
+            batch_c = {'img': inp_img.to(dist_util.dev()).to(self.dtype),
+                        'fps-xyz': fps_xyz[0:1].to(self.dtype).to(dist_util.dev())}
+            
+            # no need to return here?
+            video_path, rgb_xyz_path, post_mesh_path = self.sample_and_save(batch_c, ucg_keys, num_samples, camera, save_img, idx=ins_name, export_mesh=export_mesh, stage1_idx=i, **sampling_kwargs) # type: ignore
+
+            # video_path = './logs/i23d/stage-2/dino_img/house2-input/sample-0-0-gs.mp4'
+            # rgb_xyz_path = './logs/i23d/stage-2/dino_img/low-poly-model-of-a-green-pine-tree,-also-resembling-a-Christmas-tree.-vc.ply' 
+            assert post_mesh_path != ''
+
+            return video_path, rgb_xyz_path, post_mesh_path
+
+        else: # stage-1 data
+            # batch_c = {self.cond_key: batch[self.cond_key][0:1].to(dist_util.dev()).to(self.dtype), }
+
+            # raise NotImplementedError('stage-2 only')
+            batch_c = {'img': inp_img.to(dist_util.dev()).to(self.dtype)}
+            pcd_export_dir = self.sample_and_save(batch_c, ucg_keys, num_samples, camera, save_img, idx=ins_name, export_mesh=export_mesh, **sampling_kwargs) # type: ignore
+            return pcd_export_dir
+
+
+
+
+    def get_source_cw2wT(self, source_cameras_view_to_world):
+        return matrix_to_quaternion(
+            source_cameras_view_to_world[:3, :3].transpose(0, 1))
+
+    def c_to_3dgs_format(self, pose):
+        # TODO, switch to torch version (batched later)
+
+        c2w = pose[:16].reshape(4, 4)  # 3x4
+
+        # ! load cam
+        w2c = np.linalg.inv(c2w)
+        R = np.transpose(
+            w2c[:3, :3])  # R is stored transposed due to 'glm' in CUDA code
+        T = w2c[:3, 3]
+        fx = pose[16]
+        FovX = focal2fov(fx, 1)
+        FovY = focal2fov(fx, 1)
+
+        tanfovx = math.tan(FovX * 0.5)
+        tanfovy = math.tan(FovY * 0.5)
+
+        assert tanfovx == tanfovy
+
+        trans = np.array([0.0, 0.0, 0.0])
+        scale = 1.0
+
+        world_view_transform = torch.tensor(getWorld2View2(R, T, trans,
+                                                           scale)).transpose(
+                                                               0, 1)
+        projection_matrix = getProjectionMatrix(znear=self.znear,
+                                                zfar=self.zfar,
+                                                fovX=FovX,
+                                                fovY=FovY).transpose(0, 1)
+        full_proj_transform = (world_view_transform.unsqueeze(0).bmm(
+            projection_matrix.unsqueeze(0))).squeeze(0)
+        camera_center = world_view_transform.inverse()[3, :3]
+
+        view_world_transform = torch.tensor(getView2World(R, T, trans,
+                                                          scale)).transpose(
+                                                              0, 1)
+
+        # item.update(viewpoint_cam=[viewpoint_cam])
+        c = {}
+        c["source_cv2wT_quat"] = self.get_source_cw2wT(view_world_transform)
+        c.update(
+            projection_matrix=projection_matrix, # K
+            cam_view=world_view_transform,  # world_view_transform
+            cam_view_proj=full_proj_transform,  # full_proj_transform
+            cam_pos=camera_center,
+            tanfov=tanfovx,  # TODO, fix in the renderer
+            # orig_c2w=c2w,
+            # orig_w2c=w2c,
+            orig_pose=torch.from_numpy(pose),
+            orig_c2w=torch.from_numpy(c2w),
+            orig_w2c=torch.from_numpy(w2c),
+            # tanfovy=tanfovy,
+        )
+
+        return c  # dict for gs rendering
+
+
+
+class FlowMatchingEngine_gs_clay(FlowMatchingEngine_gs):
+
+    def __init__(
+        self,
+        *,
+        rec_model,
+        denoise_model,
+        diffusion,
+        sde_diffusion,
+        control_model,
+        control_key,
+        only_mid_control,
+        loss_class,
+        data,
+        eval_data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        eval_interval,
+        save_interval,
+        resume_checkpoint,
+        resume_cldm_checkpoint=None,
+        use_fp16=False,
+        fp16_scale_growth=0.001,
+        schedule_sampler=None,
+        weight_decay=0,
+        lr_anneal_steps=0,
+        iterations=10001,
+        ignore_resume_opt=False,
+        freeze_ae=False,
+        denoised_ae=True,
+        triplane_scaling_divider=10,
+        use_amp=False,
+        diffusion_input_size=224,
+        normalize_clip_encoding=False,
+        scale_clip_encoding=1,
+        cfg_dropout_prob=0,
+        cond_key='img_sr',
+        use_eos_feature=False,
+        compile=False,
+        snr_type='lognorm',
+        **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         control_model=control_model,
+                         control_key=control_key,
+                         only_mid_control=only_mid_control,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=resume_cldm_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         normalize_clip_encoding=normalize_clip_encoding,
+                         scale_clip_encoding=scale_clip_encoding,
+                         cfg_dropout_prob=cfg_dropout_prob,
+                         cond_key=cond_key,
+                         use_eos_feature=use_eos_feature,
+                         compile=compile,
+                         snr_type=snr_type,
+                         **kwargs)
+
+        # self._init_new_ca_weight() # after ckpt loading
+
+
+    def _set_grad_flag(self):    
+        # unfree CA only
+        requires_grad(self.ddpm_model, True) # 
+        # for k, v in self.ddpm_model.named_parameters():
+        #     # if 'cross_attn_dino' in k:
+        #     if 'mv' in k: # for mv dino
+        #         v.requires_grad_(True)
+        #         if self.step == 0:
+        #             logger.log(k)
+        #     else:
+        #         v.requires_grad_(False)
+                
+    def _init_new_ca_weight(self):
+        blks_to_copy = ['cross_attn_dino', 'prenorm_ca_dino']
+
+        for blk in self.ddpm_model.blocks:
+            for param_name in blks_to_copy:
+                try:
+                    getattr(blk, param_name.replace('dino', 'dino_mv')).load_state_dict(getattr(blk, param_name).state_dict())
+                except Exception as e:
+                    logger.log(e) # some key misalignment
diff --git a/nsr/lsgm/sgm_DiffusionEngine.py b/nsr/lsgm/sgm_DiffusionEngine.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cc04e56b9e3243330ae212df18f5d7ccfbb8578
--- /dev/null
+++ b/nsr/lsgm/sgm_DiffusionEngine.py
@@ -0,0 +1,510 @@
+"""
+https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L30
+"""
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+from click import prompt
+import einops
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+from ldm.modules.encoders.modules import FrozenClipImageEmbedder, TextEmbedder, FrozenCLIPTextEmbedder, FrozenOpenCLIPImagePredictionEmbedder, FrozenOpenCLIPImageEmbedder
+
+import dnnlib
+from dnnlib.util import requires_grad
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+# from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+# from .controlLDM import TrainLoop3DDiffusionLSGM_Control  # joint diffusion and rec class
+from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD  # joint diffusion and rec class
+
+# ! add new schedulers from https://github.com/Stability-AI/generative-models
+
+from .crossattn_cldm import TrainLoop3DDiffusionLSGM_crossattn
+
+# import SD stuffs
+from typing import Any, Dict, List, Optional, Tuple, Union
+from contextlib import contextmanager
+from omegaconf import ListConfig, OmegaConf
+from sgm.modules import UNCONDITIONAL_CONFIG
+
+from sgm.util import (default, disabled_train, get_obj_from_str,
+                      instantiate_from_config, log_txt_as_img)
+
+# from sgm.sampling_utils.demo.streamlit_helpers import init_sampling
+
+
+class DiffusionEngineLSGM(TrainLoop3DDiffusionLSGM_crossattn):
+
+    def __init__(
+        self,
+        *,
+        rec_model,
+        denoise_model,
+        diffusion,
+        sde_diffusion,
+        control_model,
+        control_key,
+        only_mid_control,
+        loss_class,
+        data,
+        eval_data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        eval_interval,
+        save_interval,
+        resume_checkpoint,
+        resume_cldm_checkpoint=None,
+        use_fp16=False,
+        fp16_scale_growth=0.001,
+        schedule_sampler=None,
+        weight_decay=0,
+        lr_anneal_steps=0,
+        iterations=10001,
+        ignore_resume_opt=False,
+        freeze_ae=False,
+        denoised_ae=True,
+        triplane_scaling_divider=10,
+        use_amp=False,
+        diffusion_input_size=224,
+        normalize_clip_encoding=False,
+        scale_clip_encoding=1,
+        cfg_dropout_prob=0,
+        cond_key='img_sr',
+        use_eos_feature=False,
+        compile=False,
+        # denoiser_config,
+        # conditioner_config: Union[None, Dict, ListConfig,
+        #                           OmegaConf] = None,
+        # sampler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        # loss_fn_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         control_model=control_model,
+                         control_key=control_key,
+                         only_mid_control=only_mid_control,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         resume_cldm_checkpoint=resume_cldm_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         normalize_clip_encoding=normalize_clip_encoding,
+                         scale_clip_encoding=scale_clip_encoding,
+                         cfg_dropout_prob=cfg_dropout_prob,
+                         cond_key=cond_key,
+                         use_eos_feature=use_eos_feature,
+                         compile=compile,
+                         **kwargs)
+
+        #  ! sgm diffusion pipeline
+        if self.cond_key == 'caption':
+            ldm_configs = OmegaConf.load(
+                'sgm/configs/txt2img-clipl-compat.yaml')['ldm_configs']
+        else:
+            ldm_configs = OmegaConf.load(
+                'sgm/configs/img23d-clipl-compat.yaml')['ldm_configs']
+
+        self.loss_fn = (
+            instantiate_from_config(ldm_configs.loss_fn_config)
+            # if loss_fn_config is not None
+            # else None
+        )
+        self.denoiser = instantiate_from_config(
+            ldm_configs.denoiser_config).to(dist_util.dev())
+        self.sampler = (instantiate_from_config(ldm_configs.sampler_config))
+
+        self.conditioner = instantiate_from_config(
+            default(ldm_configs.conditioner_config,
+                    UNCONDITIONAL_CONFIG)).to(dist_util.dev())
+
+    # ! already merged
+    def prepare_ddpm(self, eps, mode='p'):
+        raise NotImplementedError('already implemented in self.denoiser')
+
+    # merged from noD.py
+
+    # use sota denoiser, loss_fn etc.
+    def ldm_train_step(self, batch, behaviour='cano', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+
+        # ! enable the gradient of both models
+        requires_grad(self.ddpm_model, True)
+
+        self.mp_trainer.zero_grad()  # !!!!
+
+        if 'img' in batch:
+            batch_size = batch['img'].shape[0]
+        else:
+            batch_size = len(batch['caption'])
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                      enabled=self.mp_trainer.use_amp):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                assert 'latent' in micro
+                vae_out = {self.latent_name: micro['latent'].to(self.dtype)}
+                # else:
+                #     vae_out = self.ddp_rec_model(
+                #         img=micro['img_to_encoder'],
+                #         c=micro['c'],
+                #         behaviour='encoder_vae',
+                #     )  # pred: (B, 3, 64, 64)
+
+                eps = vae_out[self.latent_name] / self.triplane_scaling_divider
+                # eps = vae_out.pop(self.latent_name)
+
+                # if 'bg_plane' in vae_out:
+                #     eps = th.cat((eps, vae_out['bg_plane']),
+                #                  dim=1)  # include background, B 12+4 32 32
+
+                # ! SD loss
+                # cond = self.get_c_input(micro, bs=eps.shape[0])
+                loss, loss_other_info = self.loss_fn(self.ddp_ddpm_model,
+                                                     self.denoiser,
+                                                     self.conditioner, eps,
+                                                     micro)  # type: ignore
+                loss = loss.mean()
+                log_rec3d_loss_dict({})
+
+                log_rec3d_loss_dict({
+                    'eps_mean':
+                    eps.mean(),
+                    'eps_std':
+                    eps.std([1, 2, 3]).mean(0),
+                    'pred_x0_std':
+                    loss_other_info['model_output'].std([1, 2, 3]).mean(0),
+                    "p_loss":
+                    loss,
+                })
+
+            self.mp_trainer.backward(loss)  # joint gradient descent
+
+        # update ddpm accordingly
+        self.mp_trainer.optimize(self.opt)
+
+        if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            self.log_control_images(vae_out, micro, loss_other_info)
+
+    @th.inference_mode()
+    def log_control_images(self, vae_out, micro, ddpm_ret):
+
+        # eps_t_p, t_p, logsnr_p = (p_sample_batch[k] for k in (
+        #     'eps_t_p',
+        #     't_p',
+        #     'logsnr_p',
+        # ))
+        # pred_eps_p = ddpm_ret['pred_eps_p']
+
+        if 'posterior' in vae_out:
+            vae_out.pop('posterior')  # for calculating kl loss
+        # vae_out_for_pred = {
+        #     k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+        #     for k, v in vae_out.items()
+        # }
+        vae_out_for_pred = {self.latent_name: vae_out[self.latent_name][0:1].to(self.dtype)}
+
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+            pred = self.ddp_rec_model(latent=vae_out_for_pred,
+                                    c=micro['c'][0:1],
+                                    behaviour=self.render_latent_behaviour)
+
+        assert isinstance(pred, dict)
+
+        pred_img = pred['image_raw']
+        if 'img' in micro:
+            gt_img = micro['img']
+        else:
+            gt_img = th.zeros_like(pred['image_raw'])
+
+        if 'depth' in micro:
+            gt_depth = micro['depth']
+            if gt_depth.ndim == 3:
+                gt_depth = gt_depth.unsqueeze(1)
+            gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                      gt_depth.min())
+        else:
+            gt_depth = th.zeros_like(gt_img[:, 0:1, ...])
+
+        if 'image_depth' in pred:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+
+        gt_img = self.pool_128(gt_img)
+        gt_depth = self.pool_128(gt_depth)
+        # cond = self.get_c_input(micro)
+        # hint = th.cat(cond['c_concat'], 1)
+
+        gt_vis = th.cat(
+            [
+                gt_img,
+                gt_img,
+                gt_img,
+                # self.pool_128(hint),
+                # gt_img,
+                gt_depth.repeat_interleave(3, dim=1)
+            ],
+            dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+        # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+        noised_latent, sigmas, x_start = [
+            ddpm_ret[k] for k in ['noised_input', 'sigmas', 'model_output']
+        ]
+
+        noised_latent = {
+            'latent_normalized_2Ddiffusion':
+            noised_latent[0:1].to(self.dtype) * self.triplane_scaling_divider,
+        }
+
+        denoised_latent = {
+            'latent_normalized_2Ddiffusion':
+            x_start[0:1].to(self.dtype) * self.triplane_scaling_divider,
+        }
+         
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+            noised_ae_pred = self.ddp_rec_model(
+                img=None,
+                c=micro['c'][0:1],
+                latent=noised_latent,
+                behaviour=self.render_latent_behaviour)
+
+            # pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+            # eps_t_p, pred_eps_p, logsnr_p)  # for VAE loss, denosied latent
+
+            # pred_xstart_3D
+            denoised_ae_pred = self.ddp_rec_model(
+                img=None,
+                c=micro['c'][0:1],
+                latent=denoised_latent,
+                # latent=pred_x0[0:1] * self.
+                # triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                behaviour=self.render_latent_behaviour)
+
+        pred_vis = th.cat(
+            [
+                self.pool_128(img) for img in (
+                    pred_img[0:1],
+                    noised_ae_pred['image_raw'][0:1],
+                    denoised_ae_pred['image_raw'][0:1],  # controlnet result
+                    pred_depth[0:1].repeat_interleave(3, dim=1))
+            ],
+            dim=-1)  # B, 3, H, W
+
+        if 'img' in micro:
+            vis = th.cat([gt_vis, pred_vis],
+                         dim=-2)[0].permute(1, 2,
+                                            0).cpu()  # ! pred in range[-1, 1]
+        else:
+            vis = pred_vis[0].permute(1, 2, 0).cpu()
+
+        # vis_grid = torchvision.utils.make_grid(vis) # HWC
+        vis = vis.numpy() * 127.5 + 127.5
+        vis = vis.clip(0, 255).astype(np.uint8)
+        img_save_path = f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{sigmas[0].item():3}.jpg'
+        Image.fromarray(vis).save(img_save_path)
+
+        # if self.cond_key == 'caption':
+        #     with open(f'{logger.get_dir()}/{self.step+self.resume_step}caption_{t_p[0].item():3}.txt', 'w') as f:
+        #         f.write(micro['caption'][0])
+
+        print('log denoised vis to: ', img_save_path)
+
+        th.cuda.empty_cache()
+
+    @th.no_grad()
+    def sample(
+        self,
+        cond: Dict,
+        uc: Union[Dict, None] = None,
+        batch_size: int = 16,
+        shape: Union[None, Tuple, List] = None,
+        **kwargs,
+    ):
+        
+        with th.cuda.amp.autocast(dtype=self.dtype,
+                                    enabled=self.mp_trainer.use_amp):
+
+            randn = th.randn(batch_size, *shape).to(dist_util.dev()).to(self.dtype)
+
+            denoiser = lambda input, sigma, c: self.denoiser(
+                self.model, input, sigma, c, **kwargs)
+            samples = self.sampler(denoiser, randn, cond, uc=uc)
+
+        return samples
+
+    @th.inference_mode()
+    def eval_cldm(
+        self,
+        prompt="",
+        use_ddim=False,
+        unconditional_guidance_scale=1.0,
+        save_img=False,
+        use_train_trajectory=False,
+        camera=None,
+        num_samples=1,
+        num_instances=1,
+    ):
+        # ! slightly modified for new API. combined with
+        # /cpfs01/shared/V2V/V2V_hdd/yslan/Repo/generative-models/sgm/models/diffusion.py:249 log_images()
+        # TODO, support batch_size > 1
+
+        self.ddpm_model.eval()
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.rec_model.decoder.triplane_decoder.
+                out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False,
+                use_ddim=use_ddim))
+
+        model_kwargs = {}
+
+        uc = None
+        log = dict()
+
+        ucg_keys = [self.cond_key]
+
+        batch_c = {self.cond_key: prompt}
+
+        c, uc = self.conditioner.get_unconditional_conditioning(
+            batch_c,
+            force_uc_zero_embeddings=ucg_keys
+            if len(self.conditioner.embedders) > 0 else [],
+        )
+
+        sampling_kwargs = {}
+
+        N = 1  # hard coded, to update
+        z_shape = (
+            N,
+            self.ddpm_model.in_channels if not self.ddpm_model.roll_out else
+            3 * self.ddpm_model.in_channels,  # type: ignore
+            self.diffusion_input_size,
+            self.diffusion_input_size)
+
+        for k in c:
+            if isinstance(c[k], th.Tensor):
+                c[k], uc[k] = map(lambda y: y[k][:N].to(dist_util.dev()),
+                                  (c, uc))
+
+        samples = self.sample(c,
+                              shape=z_shape[1:],
+                              uc=uc,
+                              batch_size=N,
+                              **sampling_kwargs)
+        # st() # do rendering first
+
+        # ! get c
+
+        if self.cond_key == 'caption':
+            if camera is not None:
+                batch = {'c': camera.clone()}
+        else:
+            if use_train_trajectory:
+                batch = next(iter(self.data))
+            else:
+                try:
+                    batch = next(self.eval_data)
+                except Exception as e:
+                    self.eval_data = iter(self.eval_data)
+                    batch = next(self.eval_data)
+
+            if camera is not None:
+                batch['c'] = camera.clone()
+
+        # rendering
+        for i in range(samples.shape[0]):
+            th.cuda.empty_cache()
+
+            # ! render sampled latent
+
+            with th.cuda.amp.autocast(dtype=self.dtype,
+                                        enabled=self.mp_trainer.use_amp):
+
+                self.render_video_given_triplane(
+                    samples[i:i+1].to(self.dtype),
+                    self.rec_model,  # compatible with join_model
+                    name_prefix=
+                    f'{self.step + self.resume_step}_{i}_{prompt}',
+                    save_img=save_img,
+                    render_reference=batch,
+                    export_mesh=False)
+
+        self.ddpm_model.train()
diff --git a/nsr/lsgm/train_util_diffusion_lsgm.py b/nsr/lsgm/train_util_diffusion_lsgm.py
new file mode 100644
index 0000000000000000000000000000000000000000..661146146b2b67cdaad65fe6f7baefab56f95ab6
--- /dev/null
+++ b/nsr/lsgm/train_util_diffusion_lsgm.py
@@ -0,0 +1,583 @@
+"""
+Modified from:
+https://github.com/NVlabs/LSGM/blob/main/training_obj_joint.py
+"""
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+import dnnlib
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+
+class TrainLoop3DDiffusionLSGM(TrainLoop3DDiffusion,TrainLoop3DcvD_nvsD_canoD):
+    def __init__(self, *, rec_model, denoise_model, diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, schedule_sampler=None, weight_decay=0, lr_anneal_steps=0, iterations=10001, ignore_resume_opt=False, freeze_ae=False, denoised_ae=True, triplane_scaling_divider=10, use_amp=False, diffusion_input_size=224, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, schedule_sampler=schedule_sampler, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, ignore_resume_opt=ignore_resume_opt, freeze_ae=freeze_ae, denoised_ae=denoised_ae, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, **kwargs)
+
+    def run_step(self, batch, step='g_step'):
+
+        if step == 'diffusion_step_rec':
+            self.forward_diffusion(batch, behaviour='diffusion_step_rec')
+            _ = self.mp_trainer_rec.optimize(self.opt_rec) # TODO, update two groups of parameters
+            took_step_ddpm = self.mp_trainer.optimize(self.opt) # TODO, update two groups of parameters
+
+            if took_step_ddpm:
+                self._update_ema()  # g_ema # TODO, ema only needs to track ddpm, remove ema tracking in rec
+
+        elif step == 'd_step_rec':
+            self.forward_D(batch, behaviour='rec')
+            # _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        elif step == 'diffusion_step_nvs':
+            self.forward_diffusion(batch, behaviour='diffusion_step_nvs')
+            _ = self.mp_trainer_rec.optimize(self.opt_rec) # TODO, update two groups of parameters
+            took_step_ddpm = self.mp_trainer.optimize(self.opt) # TODO, update two groups of parameters
+
+            if took_step_ddpm:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step_nvs':
+            self.forward_D(batch, behaviour='nvs')
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            # batch = next(self.data)
+            # self.run_step(batch, 'g_step_rec')
+
+            batch = next(self.data)
+            self.run_step(batch, step='diffusion_step_rec')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_rec')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, step='diffusion_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_nvs')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save(self.mp_trainer, self.mp_trainer.model_name)
+                self.save(self.mp_trainer_rec, self.mp_trainer_rec.model_name)
+                self.save(self.mp_trainer_cvD, 'cvD')
+                self.save(self.mp_trainer_canonical_cvD, 'cano_cvD')
+
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save(self.mp_trainer, self.mp_trainer.model_name)
+                    self.save(self.mp_trainer_rec, self.mp_trainer_rec.model_name)
+                    self.save(self.mp_trainer_cvD, 'cvD')
+                    self.save(self.mp_trainer_canonical_cvD, 'cano_cvD')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def forward_diffusion(self, batch, behaviour='rec', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        self.ddp_model.requires_grad_(True)
+        self.ddp_rec_model.requires_grad_(True)
+
+        # if behaviour != 'diff' and 'rec' in behaviour:
+        # if behaviour != 'diff' and 'rec' in behaviour: # pure diffusion step
+        #     self.ddp_rec_model.requires_grad_(True)
+        for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters( # type: ignore
+        ):  # type: ignore
+            param.requires_grad_(False) # ! disable triplane_decoder grad in each iteration indepenently; 
+        # else:
+
+        self.mp_trainer_rec.zero_grad()
+        self.mp_trainer.zero_grad()
+
+        # ! no 'sds' step now, both add sds grad back to ViT
+
+        # assert behaviour != 'sds'
+        # if behaviour == 'sds':
+        # else:
+        #     self.ddp_ddpm_model.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            vae_nelbo_loss = th.tensor(0.0).to(dist_util.dev())
+            vision_aided_loss = th.tensor(0.0).to(dist_util.dev())
+            denoise_loss = th.tensor(0.0).to(dist_util.dev())
+            d_weight = th.tensor(0.0).to(dist_util.dev())
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp
+                                      and not self.freeze_ae):
+
+                # apply vae
+                vae_out = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='enc_dec_wo_triplane')  # pred: (B, 3, 64, 64)
+                
+
+                if behaviour == 'diffusion_step_rec':
+                    target = micro
+                    pred = self.ddp_rec_model(latent=vae_out,
+                                              c=micro['c'],
+                                              behaviour='triplane_dec')
+
+                    # vae reconstruction loss
+                    if last_batch or not self.use_ddp:
+                        vae_nelbo_loss, loss_dict = self.loss_class(pred,
+                                                             target,
+                                                             test_mode=False)
+                    else:
+                        with self.ddp_model.no_sync():  # type: ignore
+                            vae_nelbo_loss, loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+
+                    last_layer = self.ddp_rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                        -1].weight  # type: ignore
+
+                    if 'image_sr' in pred:
+                        vision_aided_loss = self.ddp_cano_cvD(
+                            0.5 * pred['image_sr'] +
+                            0.5 * th.nn.functional.interpolate(
+                                pred['image_raw'],
+                                size=pred['image_sr'].shape[2:],
+                                mode='bilinear'),
+                            for_G=True).mean()  # [B, 1] shape
+                    else:
+                        vision_aided_loss = self.ddp_cano_cvD(
+                            pred['image_raw'], for_G=True
+                        ).mean(
+                        )   # [B, 1] shape
+
+                    d_weight = calculate_adaptive_weight(
+                        vae_nelbo_loss,
+                        vision_aided_loss,
+                        last_layer,
+                        # disc_weight_max=1) * 1
+                        disc_weight_max=1) * self.loss_class.opt.rec_cvD_lambda
+                    # d_weight = self.loss_class.opt.rec_cvD_lambda # since decoder is fixed here. set to 0.001
+                    
+                    vision_aided_loss *= d_weight
+
+                    # d_weight = self.loss_class.opt.rec_cvD_lambda
+                    loss_dict.update({
+                        'vision_aided_loss/G_rec':
+                        vision_aided_loss,
+                        'd_weight_G_rec':
+                        d_weight,
+                    })
+
+                    log_rec3d_loss_dict(loss_dict)
+
+                elif behaviour == 'diffusion_step_nvs':
+
+                    novel_view_c = th.cat([micro['c'][1:], micro['c'][:1]])
+
+                    pred = self.ddp_rec_model(latent=vae_out,
+                                              c=novel_view_c,
+                                              behaviour='triplane_dec')
+
+                    if 'image_sr' in pred:
+                        vision_aided_loss = self.ddp_nvs_cvD(
+                            # pred_for_rec['image_sr'],
+                            0.5 * pred['image_sr'] +
+                            0.5 * th.nn.functional.interpolate(
+                                pred['image_raw'],
+                                size=pred['image_sr'].shape[2:],
+                                mode='bilinear'),
+                            for_G=True).mean()  # [B, 1] shape
+                    else:
+                        vision_aided_loss = self.ddp_nvs_cvD(
+                            pred['image_raw'], for_G=True
+                        ).mean(
+                        )  # [B, 1] shape
+
+                    d_weight = self.loss_class.opt.nvs_cvD_lambda
+                    vision_aided_loss *= d_weight
+
+                    log_rec3d_loss_dict({
+                        'vision_aided_loss/G_nvs':
+                        vision_aided_loss,
+                    })
+
+                    # ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                # elif behaviour == 'diff':
+                #     self.ddp_rec_model.requires_grad_(False)
+                #     # assert self.ddp_rec_model.module.requires_grad == False, 'freeze ddpm_rec for pure diff step'
+                else:
+                    raise NotImplementedError(behaviour)
+                #     assert behaviour == 'sds'
+
+                # pred = None
+
+                # if behaviour != 'sds': # also train diffusion
+                # assert pred is not None
+
+                # TODO, train diff and sds together, available?
+                eps = vae_out[self.latent_name] 
+
+                # if behaviour != 'sds':
+                # micro_to_denoise.detach_()
+                eps.requires_grad_(True) # single stage diffusion
+
+                t, weights = self.schedule_sampler.sample(
+                    eps.shape[0], dist_util.dev())
+                noise = th.randn(size=vae_out.size(), device='cuda')  # note that this noise value is currently shared!
+
+                model_kwargs = {}
+
+                # ? 
+                # or directly use SSD NeRF version?
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+
+                # ! handle the sampling 
+
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+                t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                    diffusion.iw_quantities(args.batch_size, args.time_eps, args.iw_sample_p, args.iw_subvp_like_vp_sde)
+                eps_t_p = diffusion.sample_q(vae_out, noise, var_t_p, m_t_p)
+
+                # in case we want to train q (vae) with another batch using a different sampling scheme for times t
+                if args.iw_sample_q in ['ll_uniform', 'll_iw']:
+                    t_q, var_t_q, m_t_q, obj_weight_t_q, _, g2_t_q = \
+                        diffusion.iw_quantities(args.batch_size, args.time_eps, args.iw_sample_q, args.iw_subvp_like_vp_sde)
+                    eps_t_q = diffusion.sample_q(vae_out, noise, var_t_q, m_t_q)
+
+                    eps_t_p = eps_t_p.detach().requires_grad_(True)
+                    eps_t = th.cat([eps_t_p, eps_t_q], dim=0)
+                    var_t = th.cat([var_t_p, var_t_q], dim=0)
+                    t = th.cat([t_p, t_q], dim=0)
+                    noise = th.cat([noise, noise], dim=0)
+                else:
+                    eps_t, m_t, var_t, t, g2_t = eps_t_p, m_t_p, var_t_p, t_p, g2_t_p
+                
+                # run the diffusion
+
+                # mixing normal trick
+                # TODO, create a new partial training_losses function 
+                mixing_component = diffusion.mixing_component(eps_t, var_t, t, enabled=dae.mixed_prediction) # TODO, which should I use?
+                params = utils.get_mixed_prediction(dae.mixed_prediction, pred_params, dae.mixing_logit, mixing_component)
+
+                # nelbo loss with kl balancing
+
+
+
+
+                # ! remainign parts of cross entropy in likelihook training
+
+                cross_entropy_per_var += diffusion.cross_entropy_const(args.time_eps)
+                cross_entropy = th.sum(cross_entropy_per_var, dim=[1, 2, 3])
+                cross_entropy += remaining_neg_log_p_total  # for remaining scales if there is any
+                all_neg_log_p = vae.decompose_eps(cross_entropy_per_var)
+                all_neg_log_p.extend(remaining_neg_log_p_per_ver)  # add the remaining neg_log_p
+                kl_all_list, kl_vals_per_group, kl_diag_list = utils.kl_per_group_vada(all_log_q, all_neg_log_p)
+
+
+                kl_coeff = 1.0
+
+                # ! calculate p/q loss; 
+                # ? no spectral regularizer here
+                # ? try adding grid_clip and sn later on.
+                q_loss = th.mean(nelbo_loss) 
+                p_loss = th.mean(p_objective) 
+
+                # backpropagate q_loss for vae and update vae params, if trained
+                if args.train_vae:
+                    grad_scalar.scale(q_loss).backward(retain_graph=utils.different_p_q_objectives(args.iw_sample_p, args.iw_sample_q))
+                    utils.average_gradients(vae.parameters(), args.distributed)
+                    if args.grad_clip_max_norm > 0.:  # apply gradient clipping
+                        grad_scalar.unscale_(vae_optimizer)
+                        th.nn.utils.clip_grad_norm_(vae.parameters(), max_norm=args.grad_clip_max_norm)
+                    grad_scalar.step(vae_optimizer)
+
+                # if we use different p and q objectives or are not training the vae, discard gradients and backpropagate p_loss
+                if utils.different_p_q_objectives(args.iw_sample_p, args.iw_sample_q) or not args.train_vae:
+                    if args.train_vae:
+                        # discard current gradients computed by weighted loss for VAE
+                        dae_optimizer.zero_grad()
+
+                    # compute gradients with unweighted loss
+                    grad_scalar.scale(p_loss).backward()
+
+                # update dae parameters
+                utils.average_gradients(dae.parameters(), args.distributed)
+                if args.grad_clip_max_norm > 0.:         # apply gradient clipping
+                    grad_scalar.unscale_(dae_optimizer)
+                    th.nn.utils.clip_grad_norm_(dae.parameters(), max_norm=args.grad_clip_max_norm)
+                grad_scalar.step(dae_optimizer)
+
+
+                # unpack separate objectives, in case we want to train q (vae) using a different sampling scheme for times t
+                if args.iw_sample_q in ['ll_uniform', 'll_iw']:
+                    l2_term_p, l2_term_q = th.chunk(l2_term, chunks=2, dim=0)
+                    p_objective = th.sum(obj_weight_t_p * l2_term_p, dim=[1, 2, 3])
+                    # cross_entropy_per_var = obj_weight_t_q * l2_term_q
+                else:
+                    p_objective = th.sum(obj_weight_t_p * l2_term, dim=[1, 2, 3])
+                    # cross_entropy_per_var = obj_weight_t_q * l2_term
+
+                # print(micro_to_denoise.min(), micro_to_denoise.max())
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    eps,  # x_start
+                    t,
+                    model_kwargs=model_kwargs,
+                    return_detail=True)
+
+                # ! DDPM step
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                    # denoised_out = denoised_fn()
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        losses = compute_losses()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                denoise_loss = (losses["loss"] * weights).mean()
+
+                x_t = losses.pop('x_t')
+                model_output = losses.pop('model_output')
+                diffusion_target = losses.pop('diffusion_target')
+                alpha_bar = losses.pop('alpha_bar')
+
+                log_loss_dict(self.diffusion, t,
+                              {k: v * weights
+                               for k, v in losses.items()})
+
+                # if behaviour == 'sds':
+                # ! calculate sds grad, and add to the grad of
+
+                # if 'rec' in behaviour and self.loss_class.opt.sds_lamdba > 0:  # only enable sds along with rec step
+                #     w = (
+                #         1 - alpha_bar**2
+                #     ) / self.triplane_scaling_divider * self.loss_class.opt.sds_lamdba  # https://github.com/ashawkey/stable-dreamfusion/issues/106
+                #     sds_grad = denoise_loss.clone().detach(
+                #     ) * w  # * https://pytorch.org/docs/stable/generated/th.Tensor.detach.html. detach() returned Tensor share the same storage with previous one. add clone() here.
+
+                #     # ae_loss = AddGradient.apply(latent[self.latent_name], sds_grad) # add sds_grad during backward
+
+                #     def sds_hook(grad_to_add):
+
+                #         def modify_grad(grad):
+                #             return grad + grad_to_add  # add the sds grad to the original grad for BP
+
+                #         return modify_grad
+
+                #     eps[self.latent_name].register_hook(
+                #         sds_hook(sds_grad))  # merge sds grad with rec/nvs ae step
+
+                loss = vae_nelbo_loss + denoise_loss + vision_aided_loss  # caluclate loss within AMP
+
+            # ! cvD loss
+
+            # exit AMP before backward
+            self.mp_trainer_rec.backward(loss)
+            self.mp_trainer.backward(loss)
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0 and behaviour != 'diff':
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    # st()
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    # if 'image_sr' in pred:  # TODO
+                    #     pred_img = th.cat(
+                    #         [self.pool_512(pred_img), pred['image_sr']],
+                    #         dim=-1)
+                    #     gt_img = th.cat(
+                    #         [self.pool_512(micro['img']), micro['img_sr']],
+                    #         dim=-1)
+                    #     pred_depth = self.pool_512(pred_depth)
+                    #     gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'], micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    noised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=x_t[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically
+                        behaviour=self.render_latent_behaviour)
+
+                    # if denoised_out is None:
+                    # if not self.denoised_ae:
+                    # denoised_out = denoised_fn()
+
+                    if self.diffusion.model_mean_type == ModelMeanType.START_X:
+                        pred_xstart = model_output
+                    else:  # * used here
+                        pred_xstart = self.diffusion._predict_xstart_from_eps(
+                            x_t=x_t, t=t, eps=model_output)
+
+                    denoised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=pred_xstart[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                        behaviour=self.render_latent_behaviour)
+
+                    # denoised_out = denoised_ae_pred
+
+                    # if not self.denoised_ae:
+                    #     denoised_ae_pred = self.ddp_rec_model(
+                    #         img=None,
+                    #         c=micro['c'][0:1],
+                    #         latent=denoised_out['pred_xstart'][0:1] * self.
+                    #         triplane_scaling_divider,  # TODO, how to define the scale automatically
+                    #         behaviour=self.render_latent_behaviour)
+                    # else:
+                    #     assert denoised_ae_pred is not None
+                    #     denoised_ae_pred['image_raw'] = denoised_ae_pred[
+                    #         'image_raw'][0:1]
+
+                    # print(pred_img.shape)
+                    # print('denoised_ae:', self.denoised_ae)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], noised_ae_pred['image_raw'][0:1],
+                        denoised_ae_pred['image_raw'][0:1],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+                    # s
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis = th.cat([
+                    #     self.pool_128(micro['img']), x_t[:, :3, ...],
+                    #     denoised_out['pred_xstart'][:, :3, ...]
+                    # ],
+                    #              dim=-1)[0].permute(
+                    #                  1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                    )
+
+                    th.cuda.empty_cache()
diff --git a/nsr/lsgm/train_util_diffusion_lsgm_cvD_joint.py b/nsr/lsgm/train_util_diffusion_lsgm_cvD_joint.py
new file mode 100644
index 0000000000000000000000000000000000000000..24d33e66d442d48d89b0b43b1e014564b468a171
--- /dev/null
+++ b/nsr/lsgm/train_util_diffusion_lsgm_cvD_joint.py
@@ -0,0 +1,1953 @@
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+
+import vision_aided_loss
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+from dnnlib.util import requires_grad
+from guided_diffusion.nn import update_ema
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+
+from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD
+
+from nsr.losses.builder import kl_coeff
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+class TrainLoop3DDiffusionLSGM_cvD(TrainLoop3DDiffusionLSGMJointnoD):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 sde_diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 triplane_scaling_divider=1,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 init_cvD=True,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         **kwargs)
+
+        #     self.setup_cvD()
+        # def setup_cvD(self):
+        device = dist_util.dev()
+
+        # TODO copied from nvs_canoD, could be merged
+        # * create vision aided model
+        # TODO, load model api
+
+        # nvs D
+        if init_cvD:
+            self.nvs_cvD = vision_aided_loss.Discriminator(
+                cv_type='clip', loss_type='multilevel_sigmoid_s',
+                device=device).to(device)
+            self.nvs_cvD.cv_ensemble.requires_grad_(
+                False)  # Freeze feature extractor
+            self._load_and_sync_parameters(model=self.nvs_cvD, model_name='cvD')
+
+            self.mp_trainer_nvs_cvD = MixedPrecisionTrainer(
+                model=self.nvs_cvD,
+                use_fp16=self.use_fp16,
+                fp16_scale_growth=fp16_scale_growth,
+                model_name='cvD',
+                use_amp=use_amp,
+                # use_amp=
+                # False,  # assert len(optimizer_state["found_inf_per_device"]) > 0, "No inf checks were recorded for this optimizer."
+                model_params=list(self.nvs_cvD.decoder.parameters()))
+            cvD_lr = 2e-4 * (lr / 1e-5) * self.loss_class.opt.nvs_D_lr_mul
+            # cvD_lr = 1e-5*(lr/1e-5)
+            self.opt_cvD = AdamW(self.mp_trainer_nvs_cvD.master_params,
+                                lr=cvD_lr,
+                                betas=(0, 0.999),
+                                eps=1e-8)  # dlr in biggan cfg
+
+            logger.log(f'cpt_cvD lr: {cvD_lr}')
+
+            if self.use_ddp:
+                self.ddp_nvs_cvD = DDP(
+                    self.nvs_cvD,
+                    device_ids=[dist_util.dev()],
+                    output_device=dist_util.dev(),
+                    broadcast_buffers=False,
+                    bucket_cap_mb=128,
+                    find_unused_parameters=False,
+                )
+            else:
+                self.ddp_nvs_cvD = self.nvs_cvD
+
+            # cano d
+            self.cano_cvD = vision_aided_loss.Discriminator(
+                cv_type='clip', loss_type='multilevel_sigmoid_s',
+                device=device).to(device)
+            self.cano_cvD.cv_ensemble.requires_grad_(
+                False)  # Freeze feature extractor
+            # self.cano_cvD.train()
+
+            self._load_and_sync_parameters(model=self.cano_cvD,
+                                        model_name='cano_cvD')
+
+            self.mp_trainer_cano_cvD = MixedPrecisionTrainer(
+                model=self.cano_cvD,
+                use_fp16=self.use_fp16,
+                fp16_scale_growth=fp16_scale_growth,
+                model_name='canonical_cvD',
+                use_amp=use_amp,
+                model_params=list(self.cano_cvD.decoder.parameters()))
+
+            cano_lr = 2e-4 * (
+                lr / 1e-5)  # D_lr=2e-4 in cvD by default. 1e-4 still overfitting
+            self.opt_cano_cvD = AdamW(
+                self.mp_trainer_cano_cvD.master_params,
+                lr=cano_lr,  # same as the G
+                betas=(0, 0.999),
+                eps=1e-8)  # dlr in biggan cfg
+
+            logger.log(f'cpt_cano_cvD lr: {cano_lr}')
+
+            self.ddp_cano_cvD = DDP(
+                self.cano_cvD,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+
+        # Fix decoder
+        requires_grad(self.rec_model.decoder, False)
+
+    def _post_run_step(self):
+        if self.step % self.log_interval == 0 and dist_util.get_rank() == 0 and self.step != 0:
+            out = logger.dumpkvs()
+            # * log to tensorboard
+            for k, v in out.items():
+                self.writer.add_scalar(f'Loss/{k}', v,
+                                       self.step + self.resume_step)
+
+        if self.step % self.eval_interval == 0 and self.step != 0:
+        # if self.step % self.eval_interval == 0:
+            if dist_util.get_rank() == 0:
+                self.eval_ddpm_sample(self.rec_model)
+                if self.sde_diffusion.args.train_vae:
+                    self.eval_loop(self.rec_model)
+
+        if self.step % self.save_interval == 0 and self.step != 0:
+            self.save(self.mp_trainer, self.mp_trainer.model_name)
+
+        self.step += 1
+
+        if self.step > self.iterations:
+            print('reached maximum iterations, exiting')
+
+            # Save the last checkpoint if it wasn't already saved.
+            if (self.step - 1) % self.save_interval != 0:
+                self.save(self.mp_trainer, self.mp_trainer.model_name)
+            exit()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            # dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, 'cano_ddpm_only')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'cano_ddpm_step')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'd_step_rec')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'nvs_ddpm_step')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'd_step_nvs')
+
+            self._post_run_step()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+        if step == 'ce_ddpm_step':
+            self.ce_ddpm_step(batch)
+
+        elif step in ['ce', 'ddpm', 'cano_ddpm_only']:
+            self.joint_rec_ddpm(batch, step)
+
+        elif step == 'cano_ddpm_step':
+            self.joint_rec_ddpm(batch, 'cano')
+
+        elif step == 'd_step_rec':
+            self.forward_D(batch, behaviour='rec')
+
+        elif step == 'nvs_ddpm_step':
+            self.joint_rec_ddpm(batch, 'nvs')
+
+        elif step == 'd_step_nvs':
+            self.forward_D(batch, behaviour='nvs')
+
+        self._anneal_lr()
+        self.log_step()
+
+    def flip_encoder_grad(self, mode=True):
+        requires_grad(self.rec_model.encoder, mode)
+
+    def forward_D(self, batch, behaviour):  # update D
+
+        self.flip_encoder_grad(False)
+        self.rec_model.eval()
+        # self.ddp_model.requires_grad_(False)
+
+        # update two D
+        if behaviour == 'nvs':
+            self.mp_trainer_nvs_cvD.zero_grad()
+            self.ddp_nvs_cvD.requires_grad_(True)
+            self.ddp_nvs_cvD.train()
+            self.ddp_cano_cvD.requires_grad_(False)
+            self.ddp_cano_cvD.eval()
+        else:  # update rec canonical D
+            self.mp_trainer_cano_cvD.zero_grad()
+            self.ddp_nvs_cvD.requires_grad_(False)
+            self.ddp_nvs_cvD.eval()
+            self.ddp_cano_cvD.requires_grad_(True)
+            self.ddp_cano_cvD.train()
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_cano_cvD.use_amp):
+
+                latent = self.ddp_rec_model(img=micro['img_to_encoder'],
+                                            behaviour='enc_dec_wo_triplane')
+
+                cano_pred = self.ddp_rec_model(latent=latent,
+                                               c=micro['c'],
+                                               behaviour='triplane_dec')
+
+                # TODO, optimize with one encoder, and two triplane decoder
+                # FIXME quit autocast to runbackward
+                if behaviour == 'rec':
+                    if 'image_sr' in cano_pred:
+                        # d_loss_cano = self.run_D_Diter(
+                        #     # real=micro['img_sr'],
+                        #     # fake=cano_pred['image_sr'],
+                        #     real=0.5 * micro['img_sr'] + 0.5 * th.nn.functional.interpolate(micro['img'], size=micro['img_sr'].shape[2:], mode='bilinear'),
+                        #     fake=0.5 * cano_pred['image_sr'] + 0.5 * th.nn.functional.interpolate(cano_pred['image_raw'], size=cano_pred['image_sr'].shape[2:], mode='bilinear'),
+                        #     D=self.ddp_canonical_cvD)  # ! failed, color bias
+
+                        # try concat them in batch
+                        d_loss = self.run_D_Diter(
+                            real=th.cat([
+                                th.nn.functional.interpolate(
+                                    micro['img'],
+                                    size=micro['img_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                micro['img_sr'],
+                            ],
+                                        dim=1),
+                            fake=th.cat([
+                                th.nn.functional.interpolate(
+                                    cano_pred['image_raw'],
+                                    size=cano_pred['image_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                cano_pred['image_sr'],
+                            ],
+                                        dim=1),
+                            D=self.ddp_cano_cvD)  # TODO, add SR for FFHQ
+                    else:
+                        d_loss = self.run_D_Diter(real=micro['img'],
+                                                  fake=cano_pred['image_raw'],
+                                                  D=self.ddp_cano_cvD)
+
+                    log_rec3d_loss_dict({'vision_aided_loss/D_cano': d_loss})
+                    # self.mp_trainer_canonical_cvD.backward(d_loss_cano)
+                else:
+                    assert behaviour == 'nvs'
+                    novel_view_c = th.roll(micro['c'], 1, 0)
+
+                    nvs_pred = self.ddp_rec_model(latent=latent,
+                                                  c=novel_view_c,
+                                                  behaviour='triplane_dec')
+
+                    if 'image_sr' in nvs_pred:
+
+                        d_loss = self.run_D_Diter(
+                            real=th.cat([
+                                th.nn.functional.interpolate(
+                                    cano_pred['image_raw'],
+                                    size=cano_pred['image_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                cano_pred['image_sr'],
+                            ],
+                                        dim=1),
+                            fake=th.cat([
+                                th.nn.functional.interpolate(
+                                    nvs_pred['image_raw'],
+                                    size=nvs_pred['image_sr'].shape[2:],
+                                    mode='bilinear',
+                                    align_corners=False,
+                                    antialias=True),
+                                nvs_pred['image_sr'],
+                            ],
+                                        dim=1),
+                            D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                    else:
+                        d_loss = self.run_D_Diter(
+                            real=cano_pred['image_raw'],
+                            fake=nvs_pred['image_raw'],
+                            D=self.ddp_nvs_cvD)  # TODO, add SR for FFHQ
+
+                    log_rec3d_loss_dict({'vision_aided_loss/D_nvs': d_loss})
+                    # self.mp_trainer_cvD.backward(d_loss_nvs)
+            # quit autocast to run backward()
+            if behaviour == 'rec':
+                self.mp_trainer_cano_cvD.backward(d_loss)
+                # assert len(optimizer_state["found_inf_per_device"]) > 0, "No inf checks were recorded for this optimizer."
+                _ = self.mp_trainer_cano_cvD.optimize(self.opt_cano_cvD)
+            else:
+                assert behaviour == 'nvs'
+                self.mp_trainer_nvs_cvD.backward(d_loss)
+                _ = self.mp_trainer_nvs_cvD.optimize(self.opt_cvD)
+
+        self.flip_encoder_grad(True)
+        self.rec_model.train()
+
+    # def forward_ddpm(self, eps):
+    #     args = self.sde_diffusion.args
+
+    #     # sample noise
+    #     noise = th.randn(size=eps.size(), device=eps.device
+    #                      )  # note that this noise value is currently shared!
+
+    #     # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+    #     t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+    #         self.sde_diffusion.iw_quantities(args.iw_sample_p)
+    #     eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p, m_t_p)
+    #     # logsnr_p = self.sde_diffusion.log_snr(m_t_p,
+    #     #                                         var_t_p)  # for p only
+
+    #     pred_eps_p, pred_x0_p, logsnr_p = self.ddpm_step(
+    #         eps_t_p, t_p, m_t_p, var_t_p)
+
+    #     # ! batchify for mixing_component
+    #     # mixing normal trick
+    #     mixing_component = self.sde_diffusion.mixing_component(
+    #         eps_t_p, var_t_p, t_p, enabled=True)  # TODO, which should I use?
+    #     pred_eps_p = get_mixed_prediction(
+    #         True, pred_eps_p,
+    #         self.ddp_ddpm_model(x=None,
+    #                             timesteps=None,
+    #                             get_attr='mixing_logit'), mixing_component)
+
+    #     # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+    #     with self.ddp_ddpm_model.no_sync():  # type: ignore
+    #         l2_term_p = th.square(pred_eps_p - noise)  # ? weights
+
+    #     p_eps_objective = th.mean(obj_weight_t_p * l2_term_p)
+
+    #     log_rec3d_loss_dict(
+    #         dict(mixing_logit=self.ddp_ddpm_model(
+    #             x=None, timesteps=None, get_attr='mixing_logit').detach(), ))
+
+    #     return {
+    #         'pred_eps_p': pred_eps_p,
+    #         'eps_t_p': eps_t_p,
+    #         'p_eps_objective': p_eps_objective,
+    #         'pred_x0_p': pred_x0_p,
+    #         'logsnr_p': logsnr_p
+    #     }
+
+    # ddpm + rec loss
+    def joint_rec_ddpm(self, batch, behaviour='cano', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+
+        # ! enable the gradient of both models
+        # requires_grad(self.rec_model, True)
+        self.flip_encoder_grad(True)
+        self.rec_model.train()
+
+        requires_grad(self.ddpm_model, True)
+        self.ddpm_model.train()
+
+        requires_grad(self.ddp_cano_cvD, False)
+        requires_grad(self.ddp_nvs_cvD, False)
+        self.ddp_cano_cvD.eval()
+        self.ddp_nvs_cvD.eval()
+
+        self.mp_trainer.zero_grad()
+
+        # if args.train_vae:
+        #     for param in self.rec_model.decoder.triplane_decoder.parameters(  # type: ignore
+        #     ):  # type: ignore
+        #         param.requires_grad_(
+        #             False
+        #         )  # ! disable triplane_decoder grad in each iteration indepenently;
+
+        assert args.train_vae
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+                #   and args.train_vae):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+                vision_aided_loss = th.tensor(0.).to(dist_util.dev())
+
+                vae_out = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='encoder_vae',
+                )  # pred: (B, 3, 64, 64)
+                eps = vae_out[self.latent_name]
+
+                if 'bg_plane' in vae_out:
+                    eps = th.cat((eps, vae_out['bg_plane']), dim=1) # include background, B 12+4 32 32
+
+                # eps = pred[self.latent_name]
+                # eps = vae_out.pop(self.latent_name)
+
+                # ! running diffusion forward
+                p_sample_batch = self.prepare_ddpm(eps)
+                # ddpm_ret = self.forward_ddpm(eps)
+                ddpm_ret = self.apply_model(p_sample_batch)
+                # p_loss = ddpm_ret['p_eps_objective']
+                loss += ddpm_ret['p_eps_objective'].mean()
+
+                # =====================================================================
+                # ! reconstruction loss + gan loss
+                if behaviour != 'cano_ddpm_only':
+                    if behaviour == 'cano':
+                        cano_pred = self.ddp_rec_model(
+                            latent=vae_out,
+                            c=micro['c'],
+                            behaviour=self.render_latent_behaviour)
+
+                        with self.ddp_model.no_sync():  # type: ignore
+                            q_vae_recon_loss, loss_dict = self.loss_class(
+                                cano_pred, micro, test_mode=False)
+                        loss += q_vae_recon_loss
+
+                        # add gan loss
+                        vision_aided_loss = self.ddp_cano_cvD(
+                            cano_pred['image_raw'], for_G=True
+                        ).mean(
+                        ) * self.loss_class.opt.rec_cvD_lambda  # [B, 1] shape
+
+                        loss_dict.update({
+                            'vision_aided_loss/G_rec':
+                            vision_aided_loss.detach(),
+                        })
+                        log_rec3d_loss_dict(loss_dict)
+
+                        if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                            self.cano_ddpm_log(cano_pred, micro, ddpm_ret)
+
+                    else:
+                        assert behaviour == 'nvs'
+
+                        nvs_pred = self.ddp_rec_model(
+                            img=micro['img_to_encoder'],
+                            c=th.roll(micro['c'], 1, 0),
+                        )  # ! render novel views only for D loss
+
+                        vision_aided_loss = self.ddp_nvs_cvD(
+                            nvs_pred['image_raw'], for_G=True
+                        ).mean(
+                        ) * self.loss_class.opt.nvs_cvD_lambda  # [B, 1] shape
+
+                        log_rec3d_loss_dict(
+                            {'vision_aided_loss/G_nvs': vision_aided_loss})
+
+                        if dist_util.get_rank() == 0 and self.step % 500 == 1:
+                            self.nvs_log(nvs_pred, micro)
+
+                else:
+                    cano_pred = self.ddp_rec_model(
+                        latent=vae_out,
+                        c=micro['c'],
+                        behaviour=self.render_latent_behaviour)
+
+                    with self.ddp_model.no_sync():  # type: ignore
+                        q_vae_recon_loss, loss_dict = self.loss_class(
+                            {
+                                **vae_out,  # include latent here.
+                                **cano_pred,
+                            },
+                            micro,
+                            test_mode=False)
+                        # pred,
+                        # micro,
+                        # test_mode=False)
+                    log_rec3d_loss_dict(loss_dict)
+                    loss += q_vae_recon_loss
+
+                loss += vision_aided_loss
+
+            self.mp_trainer.backward(loss)
+
+        # quit for loop
+        _ = self.mp_trainer.optimize(self.opt, clip_grad=self.loss_class.opt.grad_clip)
+
+    @th.inference_mode()
+    def cano_ddpm_log(self, cano_pred, micro, ddpm_ret):
+        assert isinstance(cano_pred, dict)
+        behaviour = 'cano'
+
+        gt_depth = micro['depth']
+        if gt_depth.ndim == 3:
+            gt_depth = gt_depth.unsqueeze(1)
+        gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                  gt_depth.min())
+
+        if 'image_depth' in cano_pred:
+            pred_depth = cano_pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+
+        pred_img = cano_pred['image_raw']
+        gt_img = micro['img']
+
+        if 'image_sr' in cano_pred:
+            if cano_pred['image_sr'].shape[-1] == 512:
+                pred_img = th.cat(
+                    [self.pool_512(pred_img), cano_pred['image_sr']], dim=-1)
+                gt_img = th.cat([self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                pred_depth = self.pool_512(pred_depth)
+                gt_depth = self.pool_512(gt_depth)
+
+            elif cano_pred['image_sr'].shape[-1] == 256:
+                pred_img = th.cat(
+                    [self.pool_256(pred_img), cano_pred['image_sr']], dim=-1)
+                gt_img = th.cat([self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                pred_depth = self.pool_256(pred_depth)
+                gt_depth = self.pool_256(gt_depth)
+
+            else:
+                pred_img = th.cat(
+                    [self.pool_128(pred_img), cano_pred['image_sr']], dim=-1)
+                gt_img = th.cat([self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                gt_depth = self.pool_128(gt_depth)
+                pred_depth = self.pool_128(pred_depth)
+        else:
+            gt_img = self.pool_64(gt_img)
+            gt_depth = self.pool_64(gt_depth)
+
+        gt_vis = th.cat([
+            gt_img, micro['img'], micro['img'],
+            gt_depth.repeat_interleave(3, dim=1)
+        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+        # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+        eps_t_p, pred_eps_p, logsnr_p = (ddpm_ret[k]
+                                         for k in ('eps_t_p', 'pred_eps_p',
+                                                   'logsnr_p'))
+
+        if 'bg_plane' in cano_pred:
+            noised_latent = {
+                'latent_normalized_2Ddiffusion': eps_t_p[0:1, :12] * self.triplane_scaling_divider,  
+                'bg_plane': eps_t_p[0:1, 12:16] * self.triplane_scaling_divider,  
+            }
+        else:
+            noised_latent = {
+                'latent_normalized_2Ddiffusion': eps_t_p[0:1] * self.triplane_scaling_divider,
+            }
+
+        # st() # split bg_plane here
+        noised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=noised_latent,
+            behaviour=self.render_latent_behaviour)
+
+        pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+            eps_t_p, pred_eps_p, logsnr_p)  # for VAE loss, denosied latent
+
+        if 'bg_plane' in cano_pred:
+            denoised_latent = {
+                'latent_normalized_2Ddiffusion': pred_x0[0:1, :12] * self.triplane_scaling_divider,  
+                'bg_plane': pred_x0[0:1, 12:16] * self.triplane_scaling_divider,  
+            }
+        else:
+            denoised_latent = {
+                'latent_normalized_2Ddiffusion': pred_x0[0:1] * self.triplane_scaling_divider,
+            }
+
+        # pred_xstart_3D
+        denoised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=denoised_latent,
+            behaviour=self.render_latent_behaviour)
+
+        pred_vis = th.cat([
+            pred_img[0:1], noised_ae_pred['image_raw'][0:1],
+            denoised_ae_pred['image_raw'][0:1],
+            pred_depth[0:1].repeat_interleave(3, dim=1)
+        ],
+                          dim=-1)  # B, 3, H, W
+
+        vis = th.cat([gt_vis, pred_vis],
+                     dim=-2)[0].permute(1, 2,
+                                        0).cpu()  # ! pred in range[-1, 1]
+
+        # vis_grid = torchvision.utils.make_grid(vis) # HWC
+        vis = vis.numpy() * 127.5 + 127.5
+        vis = vis.clip(0, 255).astype(np.uint8)
+        Image.fromarray(vis).save(
+            # f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+            f'{logger.get_dir()}/{self.step+self.resume_step}_{behaviour}.jpg')
+        print(
+            'log denoised vis to: ',
+            f'{logger.get_dir()}/{self.step+self.resume_step}_{behaviour}.jpg')
+        del vis, pred_vis, pred_x0, pred_eps_p, micro
+
+        th.cuda.empty_cache()
+
+    @th.inference_mode()
+    def nvs_log(self, nvs_pred, micro):
+        behaviour = 'nvs'
+
+        if dist_util.get_rank() == 0 and self.step % 500 == 1:
+            # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+            gt_depth = micro['depth']
+            if gt_depth.ndim == 3:
+                gt_depth = gt_depth.unsqueeze(1)
+            gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                      gt_depth.min())
+            # if True:
+            pred_depth = nvs_pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            pred_img = nvs_pred['image_raw']
+            gt_img = micro['img']
+
+            if 'image_sr' in nvs_pred:
+
+                if nvs_pred['image_sr'].shape[-1] == 512:
+                    pred_img = th.cat(
+                        [self.pool_512(pred_img), nvs_pred['image_sr']],
+                        dim=-1)
+                    gt_img = th.cat(
+                        [self.pool_512(micro['img']), micro['img_sr']], dim=-1)
+                    pred_depth = self.pool_512(pred_depth)
+                    gt_depth = self.pool_512(gt_depth)
+
+                elif nvs_pred['image_sr'].shape[-1] == 256:
+                    pred_img = th.cat(
+                        [self.pool_256(pred_img), nvs_pred['image_sr']],
+                        dim=-1)
+                    gt_img = th.cat(
+                        [self.pool_256(micro['img']), micro['img_sr']], dim=-1)
+                    pred_depth = self.pool_256(pred_depth)
+                    gt_depth = self.pool_256(gt_depth)
+
+                else:
+                    pred_img = th.cat(
+                        [self.pool_128(pred_img), nvs_pred['image_sr']],
+                        dim=-1)
+                    gt_img = th.cat(
+                        [self.pool_128(micro['img']), micro['img_sr']], dim=-1)
+                    gt_depth = self.pool_128(gt_depth)
+                    pred_depth = self.pool_128(pred_depth)
+
+            else:
+                gt_img = self.pool_64(gt_img)
+                gt_depth = self.pool_64(gt_depth)
+
+            gt_vis = th.cat(
+                [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+            pred_vis = th.cat(
+                [pred_img, pred_depth.repeat_interleave(3, dim=1)],
+                dim=-1)  # B, 3, H, W
+
+            # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+            #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+            vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+            vis = torchvision.utils.make_grid(
+                vis, normalize=True, scale_each=True,
+                value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+            vis = vis.numpy() * 255
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            Image.fromarray(vis).save(
+                f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg')
+            print('log vis to: ',
+                  f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg')
+
+    # ! all copied from train_util_cvD.py; should merge later.
+    def run_D_Diter(self, real, fake, D=None):
+        # Dmain: Minimize logits for generated images and maximize logits for real images.
+        if D is None:
+            D = self.ddp_nvs_cvD
+
+        lossD = D(real, for_real=True).mean() + D(fake, for_real=False).mean()
+        return lossD
+
+    def save(self, mp_trainer=None, model_name='rec'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, mp_trainer.master_params)
+
+        if model_name == 'ddpm':
+            for rate, params in zip(self.ema_rate, self.ema_params):
+                save_checkpoint(rate, params)
+
+        dist.barrier()
+
+    def _load_and_sync_parameters(self, model=None, model_name='rec'):
+        resume_checkpoint, self.resume_step = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.ddp_rec_model  # default model in the parent class
+
+        logger.log(resume_checkpoint)
+
+        if resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                logger.log(f'mark {model_name} loading ', )
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                logger.log(f'mark {model_name} loading finished', )
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+
+                    # elif 'IN' in k and model_name == 'rec' and getattr(model.decoder, 'decomposed_IN', False):
+                    #     model_state_dict[k.replace('IN', 'superresolution.norm.norm_layer')] = v # decomposed IN
+                    elif 'attn.wk' in k or 'attn.wv' in k:  # old qkv
+                        logger.log('ignore ', k)
+
+                    elif 'decoder.vit_decoder.blocks' in k:
+                        # st()
+                        # load from 2D ViT pre-trained into 3D ViT blocks.
+                        assert len(model.decoder.vit_decoder.blocks[0].vit_blks
+                                   ) == 2  # assert depth=2 here.
+                        fusion_ca_depth = len(
+                            model.decoder.vit_decoder.blocks[0].vit_blks)
+                        vit_subblk_index = int(k.split('.')[3])
+                        vit_blk_keyname = ('.').join(k.split('.')[4:])
+                        fusion_blk_index = vit_subblk_index // fusion_ca_depth
+                        fusion_blk_subindex = vit_subblk_index % fusion_ca_depth
+                        model_state_dict[
+                            f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'] = v
+                        # logger.log('load 2D ViT weight: {}'.format(f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'))
+
+                    elif 'IN' in k:
+                        logger.log('ignore ', k)
+
+                    elif 'quant_conv' in k:
+                        logger.log('ignore ', k)
+
+                    else:
+                        logger.log(
+                            '!!!! ignore key: ',
+                            k,
+                            ": ",
+                            v.size(),
+                        )
+                        if k in model_state_dict:
+                            logger.log('shape in model: ',
+                                       model_state_dict[k].size())
+                        else:
+                            logger.log(k, 'not in model_state_dict')
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            logger.log(f'synced {model_name} params')
+
+
+class TrainLoop3DDiffusionLSGM_cvD_scaling(TrainLoop3DDiffusionLSGM_cvD):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 sde_diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 triplane_scaling_divider=1,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 init_cvD=True,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         sde_diffusion=sde_diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         init_cvD=init_cvD,
+                         **kwargs)
+
+    def _update_latent_stat_ema(self, latent: th.Tensor):
+        # update the miu/var of ema_latent
+        for rate, params in zip(self.ema_rate,
+                                [self.ddpm_model.ema_latent_mean]):
+            update_ema(params, latent.mean(0, keepdim=True), rate=rate)
+        for rate, params in zip(self.ema_rate,
+                                [self.ddpm_model.ema_latent_std]):
+            update_ema(params, latent.std([1,2,3]).mean(0, keepdim=True), rate=rate)
+
+        log_rec3d_loss_dict({'ema_latent_std': self.ddpm_model.ema_latent_std.mean()})
+        log_rec3d_loss_dict({'ema_latent_mean': self.ddpm_model.ema_latent_mean.mean()})
+
+    # def _init_optim_groups(self, rec_model, freeze_decoder=True):
+    #     # unfreeze decoder when scaling is enabled
+    #     return super()._init_optim_groups(rec_model, freeze_decoder=False)
+
+    def _standarize(self, eps):
+        # scaled_eps = (eps - self.ddpm_model.ema_latent_mean
+        #         ) / self.ddpm_model.ema_latent_std
+        # scaled_eps = eps - self.ddpm_model.ema_latent_mean
+        # scaled_eps = eps.div(self.ddpm_model.ema_latent_std)
+        # scaled_eps = eps + self.ddpm_model.ema_latent_std
+        scaled_eps = eps.add(-self.ddpm_model.ema_latent_mean).mul(1/self.ddpm_model.ema_latent_std)
+        return scaled_eps
+
+    def _unstandarize(self, scaled_eps):
+        return scaled_eps.mul(self.ddpm_model.ema_latent_std).add(self.ddpm_model.ema_latent_mean)
+
+
+class TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm(TrainLoop3DDiffusionLSGM_cvD_scaling):
+    def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224,init_cvD=False, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size,
+                         init_cvD=init_cvD, **kwargs)
+
+    def _setup_opt(self):
+        # TODO, two optims groups.
+        self.opt = AdamW([{
+            'name': 'ddpm',
+            'params': self.ddpm_model.parameters(),
+        }],
+                         lr=self.lr,
+                         weight_decay=self.weight_decay)
+
+        for rec_param_group in self._init_optim_groups(self.rec_model, True): # freeze D
+            self.opt.add_param_group(rec_param_group)
+        logger.log(self.opt)
+
+
+    def next_n_batch(self, n=1):
+        '''sample n batch at the same time.
+        '''
+        all_batch_list = [next(self.data) for _ in range(n)]
+        return {
+            k: th.cat([batch[k] for batch in all_batch_list], 0)
+            for k in all_batch_list[0].keys()
+        }
+        # pass
+
+    def subset_batch(self, batch=None, micro_batchsize=4, big_endian=False):
+        '''sample a batch subset
+        '''
+        if batch is None:
+            batch = next(self.data)
+        if big_endian:
+            return {
+                k: v[-micro_batchsize:]
+                for k, v in batch.items()
+            }
+        else:
+            return {
+                k: v[:micro_batchsize]
+                for k, v in batch.items()
+            }
+        # pass
+
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            # dist_util.synchronize()
+
+            # batch = self.next_n_batch(n=4)
+            batch = self.next_n_batch(n=6) # effective BS=72
+            self.run_step(batch, 'ddpm') # ddpm fixed
+
+            batch = next(self.data)
+            self.run_step(batch, 'ce')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'cano_ddpm_step')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'd_step_rec')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'nvs_ddpm_step')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'd_step_nvs')
+
+            self._post_run_step()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    # def _init_optim_groups(self, rec_model, freeze_decoder=True):
+    #     # unfreeze decoder when scaling is enabled
+    #     # return super()._init_optim_groups(rec_model, freeze_decoder=False)
+    #     return super()._init_optim_groups(rec_model, freeze_decoder=True)
+
+    def entropy_weight(self, normal_entropy=None):
+        return self.loss_class.opt.negative_entropy_lambda 
+
+    # ddpm + rec loss
+    def joint_rec_ddpm(self, batch, behaviour='ddpm', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+
+        # ! enable the gradient of both models
+        # requires_grad(self.rec_model, True)
+        # if behaviour == 'ce': # ll sampling? later. train encoder.
+        if 'ce' in behaviour: # ll sampling? later. train encoder.
+            ##############################################
+            ###### Update the VAE encoder/decoder ########
+            ##############################################
+            requires_grad(self.ddpm_model, False)
+            self.ddpm_model.eval()
+            ce_flag = True
+
+            if behaviour == 'ce_E': # unfreeze E and freeze D
+                requires_grad(self.rec_model.encoder, True)
+                self.rec_model.encoder.train()
+                requires_grad(self.rec_model.decoder, False)
+                self.rec_model.decoder.eval()
+
+            else: # train all
+                requires_grad(self.rec_model, True)
+                self.rec_model.train()
+
+        else: # train ddpm.
+            ce_flag = False
+            # self.flip_encoder_grad(False)
+            requires_grad(self.rec_model, False)
+            self.rec_model.eval()
+            requires_grad(self.ddpm_model, True)
+            self.ddpm_model.train()
+
+        self.mp_trainer.zero_grad()
+
+        # assert args.train_vae
+
+        batch_size = batch['img'].shape[0]
+
+        # for i in range(0, batch_size, self.microbatch):
+        for i in range(0, batch_size, batch_size):
+
+            micro = {
+                k:
+                v[i:i + batch_size].to(dist_util.dev()) if isinstance(
+                # v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                    #   enabled=self.mp_trainer.use_amp):
+                                      enabled=False):
+                #   and args.train_vae):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                # with th.cuda.amp.autocast(dtype=th.float16,
+                #                         enabled=False):
+                # quit amp in encoder, avoid nan.
+                vae_out = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='encoder_vae',
+                )  # pred: (B, 3, 64, 64)
+                eps = vae_out[self.latent_name]
+
+                # ! prepare for diffusion
+                if 'bg_plane' in vae_out:
+                    eps = th.cat((eps, vae_out['bg_plane']), dim=1) # include background, B 12+4 32 32
+
+                if ce_flag:
+                    p_sample_batch = self.prepare_ddpm(eps, 'q')
+                else: # sgm prior
+                    eps.requires_grad_(True)
+                    p_sample_batch = self.prepare_ddpm(eps, 'p')
+
+                # ! running diffusion forward
+                ddpm_ret = self.apply_model(p_sample_batch)
+                # p_loss = ddpm_ret['p_eps_objective']
+                p_loss = ddpm_ret['p_eps_objective'].mean()
+                if ce_flag:
+                    cross_entropy = p_loss  # why collapse?
+                    normal_entropy = vae_out['posterior'].normal_entropy()
+                    negative_entropy = -normal_entropy * self.entropy_weight(normal_entropy)
+                    ce_loss = (cross_entropy + negative_entropy.mean())
+
+                    if self.diffusion_ce_anneal: # gradually add ce lambda 
+                        raise NotImplementedError()
+                        diffusion_ce_lambda = kl_coeff(
+                            step=self.step + self.resume_step,
+                            constant_step=5e3,
+                            total_step=20e3,  
+                            min_kl_coeff=1e-2,
+                            max_kl_coeff=self.loss_class.opt.negative_entropy_lambda)
+                        ce_loss *= diffusion_ce_lambda
+
+                        log_rec3d_loss_dict({
+                            'diffusion_ce_lambda': diffusion_ce_lambda,
+                        })
+
+                    loss += ce_loss
+                else:
+                    loss += p_loss  # p loss
+
+                if ce_flag and 'D' in behaviour: # ce only on E
+                    # =====================================================================
+                    # ! reconstruction loss + gan loss
+
+                    with th.cuda.amp.autocast(dtype=th.float16,
+                                            enabled=False):
+
+                        # 24GB memory use till now.
+                        cano_pred = self.ddp_rec_model( 
+                            latent=vae_out,
+                            c=micro['c'],
+                            behaviour=self.render_latent_behaviour)
+
+                        with self.ddp_model.no_sync():  # type: ignore
+                            q_vae_recon_loss, loss_dict = self.loss_class(
+                                {
+                                    **vae_out,  # include latent here.
+                                    **cano_pred,
+                                },
+                                micro,
+                                test_mode=False)
+                        
+                        log_rec3d_loss_dict({
+                            **loss_dict,
+                            'negative_entropy': negative_entropy.mean(),
+                        })
+                        loss += q_vae_recon_loss
+
+                        # save image log
+                        if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                            self.cano_ddpm_log(cano_pred, micro, ddpm_ret)
+
+            self.mp_trainer.backward(loss) # grad accumulation
+
+        # quit micro
+        _ = self.mp_trainer.optimize(self.opt, clip_grad=self.loss_class.opt.grad_clip)
+
+class TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD(TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm):
+    def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, init_cvD=False, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, init_cvD=init_cvD, **kwargs)
+
+    def _setup_opt(self):
+        # TODO, two optims groups.
+        self.opt = AdamW([{
+            'name': 'ddpm',
+            'params': self.ddpm_model.parameters(),
+        }],
+                         lr=self.lr,
+                         weight_decay=self.weight_decay)
+
+        for rec_param_group in self._init_optim_groups(self.rec_model, freeze_decoder=False):
+            self.opt.add_param_group(rec_param_group)
+        logger.log(self.opt)
+
+class TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_weightingv0(TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD):
+    '''
+    1. weight CE with ema(var(eps)), since ce decreases, sigma decreases.
+    2. clip entorpy (log sigma) with 0; avoid it form increasing too much
+    3. add eps scaling back with ema_rate=0.9999, make sure the std=1.
+    4. add grad clipping by default
+    '''
+    def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, init_cvD=False, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, init_cvD=init_cvD, **kwargs)
+        
+        # for dynamic entropy penalize
+        self.entropy_const = 0.5 * (np.log(2 * np.pi) + 1)
+        # self._load_and_sync_parameters
+    
+
+    # def _load_model(self):
+    #     # TODO, for currently compatability
+    #     self._load_and_sync_parameters(model=self.model) # load to joint class
+    
+    # def save(self):
+    #     return super().save()
+
+    def prepare_ddpm(self, eps, mode='p'):
+
+        log_rec3d_loss_dict(
+            {
+                f'unscaled_eps_mean': eps.mean(),
+                f'unscaled_eps_std': eps.std([1,2,3]).mean(0),
+            }
+        )
+
+        scaled_eps = self._standarize(eps)
+        p_sample_batch = super().prepare_ddpm(scaled_eps, mode)
+
+        # update ema; this will not affect the diffusion computation of this batch.
+        self._update_latent_stat_ema(eps)
+
+        return p_sample_batch
+
+    def ce_weight(self):
+        return self.loss_class.opt.ce_lambda * (self.ddpm_model.ema_latent_std.mean().detach())
+
+    # def ce_weight(self):
+    #     return self.loss_class.opt.ce_lambda
+
+    def entropy_weight(self, normal_entropy=None):
+        '''if log(sigma) > 0; stop penalty.
+        '''
+        # basically L1
+        negative_entroy_lambda = self.loss_class.opt.negative_entropy_lambda
+        # return th.where(normal_entropy>self.entropy_const, -negative_entroy_lambda, negative_entroy_lambda) # if log(sigma) > 0, weight = 0.
+        # return negative_entroy_lambda * (1/self.ddpm_model.ema_latent_std.mean().detach()**2) # if log(sigma) > 0, weight = 0.
+        return negative_entroy_lambda * (1/self.ddpm_model.ema_latent_std.mean().detach()) # if log(sigma) > 0, weight = 0.
+
+class TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED(TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_weightingv0):
+    def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, init_cvD=False, diffusion_ce_anneal=False, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, init_cvD=init_cvD, **kwargs)
+        self.diffusion_ce_anneal = diffusion_ce_anneal
+
+    def run_step(self, batch, step='g_step'):
+
+        assert step in ['ce', 'ddpm', 'cano_ddpm_only', 'ce_ED', 'ce_E', 'ce_D', 'D', 'ED']
+        self.joint_rec_ddpm(batch, step)
+
+        self._anneal_lr()
+        self.log_step()
+
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            batch = self.next_n_batch(n=12) # effective BS=48
+            self.run_step(batch, 'ddpm') # ddpm fixed AE
+
+            batch = self.next_n_batch(n=3) # effective BS=12
+            self.run_step(batch, 'ce_ED')
+
+            self._post_run_step()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+
+
+    @th.inference_mode()
+    def log_diffusion_images(self, vae_out, p_sample_batch, micro, ddpm_ret):
+
+        eps_t_p, t_p, logsnr_p = (p_sample_batch[k] for k in (
+            'eps_t_p',
+            't_p',
+            'logsnr_p',
+        ))
+        pred_eps_p = ddpm_ret['pred_eps_p']
+
+        vae_out.pop('posterior')  # for calculating kl loss
+        vae_out_for_pred = {
+            k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+            for k, v in vae_out.items()
+        }
+
+        pred = self.ddp_rec_model(latent=vae_out_for_pred,
+                                  c=micro['c'][0:1],
+                                  behaviour=self.render_latent_behaviour)
+        assert isinstance(pred, dict)
+
+        pred_img = pred['image_raw']
+        gt_img = micro['img']
+
+        if 'depth' in micro:
+            gt_depth = micro['depth']
+            if gt_depth.ndim == 3:
+                gt_depth = gt_depth.unsqueeze(1)
+            gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                      gt_depth.min())
+        else:
+            gt_depth = th.zeros_like(gt_img[:, 0:1, ...])
+
+        if 'image_depth' in pred:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+
+        gt_img = self.pool_128(gt_img)
+        gt_depth = self.pool_128(gt_depth)
+        # cond = self.get_c_input(micro)
+        # hint = th.cat(cond['c_concat'], 1)
+
+        gt_vis = th.cat(
+            [
+                gt_img,
+                gt_img,
+                # self.pool_128(hint),
+                gt_img,
+                gt_depth.repeat_interleave(3, dim=1)
+            ],
+            dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+        # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+        if 'bg_plane' in vae_out:
+            noised_latent = {
+                'latent_normalized_2Ddiffusion':
+                eps_t_p[0:1, :12] * self.triplane_scaling_divider,
+                'bg_plane':
+                eps_t_p[0:1, 12:16] * self.triplane_scaling_divider,
+            }
+        else:
+            noised_latent = {
+                'latent_normalized_2Ddiffusion':
+                eps_t_p[0:1] * self.triplane_scaling_divider,
+            }
+
+        noised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=noised_latent,
+            # latent=eps_t_p[0:1] * self.
+            # triplane_scaling_divider,  # TODO, how to define the scale automatically
+            behaviour=self.render_latent_behaviour)
+
+        pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+            eps_t_p, pred_eps_p, logsnr_p)  # for VAE loss, denosied latent
+
+        if 'bg_plane' in vae_out:
+            denoised_latent = {
+                'latent_normalized_2Ddiffusion':
+                pred_x0[0:1, :12] * self.triplane_scaling_divider,
+                'bg_plane':
+                pred_x0[0:1, 12:16] * self.triplane_scaling_divider,
+            }
+        else:
+            denoised_latent = {
+                'latent_normalized_2Ddiffusion':
+                pred_x0[0:1] * self.triplane_scaling_divider,
+            }
+
+        # pred_xstart_3D
+        denoised_ae_pred = self.ddp_rec_model(
+            img=None,
+            c=micro['c'][0:1],
+            latent=denoised_latent,
+            # latent=pred_x0[0:1] * self.
+            # triplane_scaling_divider,  # TODO, how to define the scale automatically?
+            behaviour=self.render_latent_behaviour)
+
+        pred_vis = th.cat(
+            [
+                self.pool_128(img) for img in (
+                    pred_img[0:1],
+                    noised_ae_pred['image_raw'][0:1],
+                    denoised_ae_pred['image_raw'][0:1],  # controlnet result
+                    pred_depth[0:1].repeat_interleave(3, dim=1))
+            ],
+            dim=-1)  # B, 3, H, W
+
+        vis = th.cat([gt_vis, pred_vis],
+                     dim=-2)[0].permute(1, 2,
+                                        0).cpu()  # ! pred in range[-1, 1]
+
+        # vis_grid = torchvision.utils.make_grid(vis) # HWC
+        vis = vis.numpy() * 127.5 + 127.5
+        vis = vis.clip(0, 255).astype(np.uint8)
+        Image.fromarray(vis).save(
+            f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg'
+        )
+        print(
+            'log denoised vis to: ',
+            f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg'
+        )
+
+        th.cuda.empty_cache()
+
+
+    @th.inference_mode()
+    def log_patch_img(self, micro, pred, pred_cano):
+        # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+        def norm_depth(pred_depth):  # to [-1,1]
+            # pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            return -(pred_depth * 2 - 1)
+
+        pred_img = pred['image_raw']
+        gt_img = micro['img']
+
+        # infer novel view also
+        # if self.loss_class.opt.symmetry_loss:
+        #     pred_nv_img = nvs_pred
+        # else:
+        # ! replace with novel view prediction
+
+        # ! log another novel-view prediction
+        # pred_nv_img = self.rec_model(
+        #     img=micro['img_to_encoder'],
+        #     c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+        # if 'depth' in micro:
+        gt_depth = micro['depth']
+        if gt_depth.ndim == 3:
+            gt_depth = gt_depth.unsqueeze(1)
+        gt_depth = norm_depth(gt_depth)
+        # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+        #                                           gt_depth.min())
+        # if True:
+        fg_mask = pred['image_mask'] * 2 - 1  # 0-1
+        input_fg_mask = pred_cano['image_mask'] * 2 - 1  # 0-1
+        if 'image_depth' in pred:
+            pred_depth = norm_depth(pred['image_depth'])
+            pred_nv_depth = norm_depth(pred_cano['image_depth'])
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+            pred_nv_depth = th.zeros_like(gt_depth)
+
+        # if 'image_sr' in pred:
+        #     if pred['image_sr'].shape[-1] == 512:
+        #         pred_img = th.cat([self.pool_512(pred_img), pred['image_sr']],
+        #                           dim=-1)
+        #         gt_img = th.cat([self.pool_512(micro['img']), micro['img_sr']],
+        #                         dim=-1)
+        #         pred_depth = self.pool_512(pred_depth)
+        #         gt_depth = self.pool_512(gt_depth)
+
+        #     elif pred['image_sr'].shape[-1] == 256:
+        #         pred_img = th.cat([self.pool_256(pred_img), pred['image_sr']],
+        #                           dim=-1)
+        #         gt_img = th.cat([self.pool_256(micro['img']), micro['img_sr']],
+        #                         dim=-1)
+        #         pred_depth = self.pool_256(pred_depth)
+        #         gt_depth = self.pool_256(gt_depth)
+
+        #     else:
+        #         pred_img = th.cat([self.pool_128(pred_img), pred['image_sr']],
+        #                           dim=-1)
+        #         gt_img = th.cat([self.pool_128(micro['img']), micro['img_sr']],
+        #                         dim=-1)
+        #         gt_depth = self.pool_128(gt_depth)
+        #         pred_depth = self.pool_128(pred_depth)
+        # else:
+        #     gt_img = self.pool_64(gt_img)
+        #     gt_depth = self.pool_64(gt_depth)
+
+        pred_vis = th.cat([
+            pred_img,
+            pred_depth.repeat_interleave(3, dim=1),
+            fg_mask.repeat_interleave(3, dim=1),
+        ],
+                          dim=-1)  # B, 3, H, W
+
+        pred_vis_nv = th.cat([
+            pred_cano['image_raw'],
+            pred_nv_depth.repeat_interleave(3, dim=1),
+            input_fg_mask.repeat_interleave(3, dim=1),
+        ],
+                             dim=-1)  # B, 3, H, W
+
+        pred_vis = th.cat([pred_vis, pred_vis_nv], dim=-2)  # cat in H dim
+
+        gt_vis = th.cat([
+            gt_img,
+            gt_depth.repeat_interleave(3, dim=1),
+            th.zeros_like(gt_img)
+        ],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+        # if 'conf_sigma' in pred:
+        #     gt_vis = th.cat([gt_vis, fg_mask], dim=-1)  # placeholder
+
+        # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+        # st()
+        vis = th.cat([gt_vis, pred_vis], dim=-2)
+        # .permute(
+        #     0, 2, 3, 1).cpu()
+        vis_tensor = torchvision.utils.make_grid(vis, nrow=vis.shape[-1] //
+                                                 64)  # HWC
+        torchvision.utils.save_image(
+            vis_tensor,
+            f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+            value_range=(-1, 1),
+            normalize=True)
+
+        logger.log('log vis to: ',
+                   f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+        # self.writer.add_image(f'images',
+        #                       vis,
+        #                       self.step + self.resume_step,
+        #                       dataformats='HWC')
+
+
+
+class TrainLoop3D_LDM(TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED):
+    def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, init_cvD=False, diffusion_ce_anneal=False, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, init_cvD=init_cvD, diffusion_ce_anneal=diffusion_ce_anneal, **kwargs)
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            batch = self.next_n_batch(n=2) # effective BS=64, micro=4, 30.7gib
+            self.run_step(batch, 'ddpm') # ddpm fixed AE
+
+            # batch = self.next_n_batch(n=1) # 
+            # self.run_step(batch, 'ce_ED')
+
+            self._post_run_step()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+
+class TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED_nv(TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED):
+    # reconstruction function from train_nv_util.py
+    def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, init_cvD=False, diffusion_ce_anneal=False, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, init_cvD=init_cvD, diffusion_ce_anneal=diffusion_ce_anneal, **kwargs)
+
+        # ! for rendering
+        self.eg3d_model = self.rec_model.decoder.triplane_decoder  # type: ignore
+        self.renderdiff_loss = False # whether to render denoised latent for reconstruction loss
+
+        # self.inner_loop_k = 2
+        # self.ce_d_loop_k = 6
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            batch = self.next_n_batch(n=2) # effective BS=2*8
+            self.run_step(batch, 'ddpm')
+
+            # if self.step % self.inner_loop_k == 1: # train E per 2 steps
+            batch = next(self.data) # sample a new batch for rec training
+            # self.run_step(self.subset_batch(batch, micro_batchsize=6, big_endian=False), 'ce_ED') # freeze D, train E with diffusion prior
+            # self.run_step(batch, 'ce_ED') # 
+            self.run_step(batch, 'ce_E') # 
+
+                # if self.step % self.ce_d_loop_k == 1: # train D per 4 steps
+                #     batch = next(self.data) # sample a new batch for rec training
+                #     self.run_step(self.subset_batch(batch, micro_batchsize=4, big_endian=True), 'ED') # freeze E, train D
+
+            self._post_run_step()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+
+    # ddpm + rec loss
+    def joint_rec_ddpm(self, batch, behaviour='ddpm', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+
+        # ! enable the gradient of both models
+        # requires_grad(self.rec_model, True)
+        # if behaviour == 'ce': # ll sampling? later. train encoder.
+        ce_flag = False
+        diffusion_flag = True
+        if 'ce' in behaviour: # ll sampling? later. train encoder.
+            ##############################################
+            ###### Update the VAE encoder/decoder ########
+            ##############################################
+            requires_grad(self.ddpm_model, False)
+            self.ddpm_model.eval()
+            ce_flag = True
+
+            if behaviour == 'ce_E': # unfreeze E and freeze D
+                requires_grad(self.rec_model.encoder, True)
+                self.rec_model.encoder.train()
+                requires_grad(self.rec_model.decoder, False)
+                self.rec_model.decoder.eval()
+
+            elif behaviour == 'ce_D': # unfreeze E and freeze D
+                requires_grad(self.rec_model.encoder, False)
+                self.rec_model.encoder.eval()
+                requires_grad(self.rec_model.decoder, True)
+                self.rec_model.decoder.train()
+
+            else: # train all, may oom
+                requires_grad(self.rec_model, True)
+                self.rec_model.train()
+
+        elif behaviour == 'ED': # just train E and D
+            diffusion_flag = False
+            requires_grad(self.ddpm_model, False)
+            self.ddpm_model.eval()
+            requires_grad(self.rec_model, True)
+            self.rec_model.train()
+
+        elif behaviour == 'D':
+            diffusion_flag = False
+            requires_grad(self.rec_model.encoder, False)
+            self.rec_model.encoder.eval()
+            requires_grad(self.rec_model.decoder, True)
+            self.rec_model.decoder.train()
+
+        else: # train ddpm.
+            # self.flip_encoder_grad(False)
+            requires_grad(self.rec_model, False)
+            self.rec_model.eval()
+            requires_grad(self.ddpm_model, True)
+            self.ddpm_model.train()
+
+        self.mp_trainer.zero_grad()
+
+        assert args.train_vae
+
+        batch_size = batch['img'].shape[0]
+
+        # for i in range(0, batch_size, self.microbatch):
+        for i in range(0, batch_size, batch_size):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            # ! sample rendering patch
+            target = {
+                **self.eg3d_model(
+                    c=micro['nv_c'],  # type: ignore
+                    ws=None,
+                    planes=None,
+                    sample_ray_only=True,
+                    fg_bbox=micro['nv_bbox']),  # rays o / dir
+            }
+
+            patch_rendering_resolution = self.eg3d_model.rendering_kwargs[
+                'patch_rendering_resolution']  # type: ignore
+            cropped_target = {
+                k: th.empty_like(v)
+                [..., :patch_rendering_resolution, :patch_rendering_resolution]
+                if k not in [
+                    'ins_idx', 'img_to_encoder', 'img_sr', 'nv_img_to_encoder',
+                    'nv_img_sr', 'c'
+                ] else v
+                for k, v in micro.items()
+            }
+
+            # crop according to uv sampling
+            for j in range(micro['img'].shape[0]):
+                top, left, height, width = target['ray_bboxes'][
+                    j]  # list of tuple
+                # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+                for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+                    # target[key][i:i+1] = torchvision.transforms.functional.crop(
+                    # cropped_target[key][
+                    #     j:j + 1] = torchvision.transforms.functional.crop(
+                    #         micro[key][j:j + 1], top, left, height, width)
+
+                    cropped_target[f'{key}'][  # ! no nv_ here
+                        j:j + 1] = torchvision.transforms.functional.crop(
+                            micro[f'nv_{key}'][j:j + 1], top, left, height,
+                            width)
+
+            # ! cano view loss
+            cano_target = {
+                **self.eg3d_model(
+                    c=micro['c'],  # type: ignore
+                    ws=None,
+                    planes=None,
+                    sample_ray_only=True,
+                    fg_bbox=micro['bbox']),  # rays o / dir
+            }
+
+            cano_cropped_target = {
+                k: th.empty_like(v)
+                for k, v in cropped_target.items()
+            }
+
+            for j in range(micro['img'].shape[0]):
+                top, left, height, width = cano_target['ray_bboxes'][
+                    j]  # list of tuple
+                # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+                for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+                    # target[key][i:i+1] = torchvision.transforms.functional.crop(
+                    cano_cropped_target[key][
+                        j:j + 1] = torchvision.transforms.functional.crop(
+                            micro[key][j:j + 1], top, left, height, width)
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                    #   enabled=self.mp_trainer.use_amp):
+                                      enabled=False):
+                #   and args.train_vae):
+
+                loss = th.tensor(0.).to(dist_util.dev())
+
+                # with th.cuda.amp.autocast(dtype=th.float16,
+                #                         enabled=False):
+                # quit amp in encoder, avoid nan.
+                vae_out = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='encoder_vae',
+                )  # pred: (B, 3, 64, 64)
+
+
+                if diffusion_flag:
+
+                    eps = vae_out[self.latent_name] # 12542mib, bs=4
+
+                    # '''
+                    # ! prepare for diffusion
+                    if 'bg_plane' in vae_out:
+                        eps = th.cat((eps, vae_out['bg_plane']), dim=1) # include background, B 12+4 32 32
+
+                    if ce_flag:
+                        p_sample_batch = self.prepare_ddpm(eps, 'q')
+                    else:
+                        eps.requires_grad_(True)
+                        p_sample_batch = self.prepare_ddpm(eps, 'p')
+
+                    # ! running diffusion forward
+                    ddpm_ret = self.apply_model(p_sample_batch)
+                    # p_loss = ddpm_ret['p_eps_objective']
+                    p_loss = ddpm_ret['p_eps_objective'].mean()
+                    # st() # 12890mib
+
+                    if ce_flag:
+                        cross_entropy = p_loss  # why collapse?
+                        normal_entropy = vae_out['posterior'].normal_entropy()
+                        entropy_weight = self.entropy_weight(normal_entropy)
+                        negative_entropy = -normal_entropy * entropy_weight
+                        ce_loss = (cross_entropy + negative_entropy.mean())
+
+                        # if self.diffusion_ce_anneal: # gradually add ce lambda 
+                        #     diffusion_ce_lambda = kl_coeff(
+                        #         step=self.step + self.resume_step,
+                        #         constant_step=5e3+self.resume_step,
+                        #         total_step=25e3,  
+                        #         min_kl_coeff=1e-5,
+                        #         max_kl_coeff=self.loss_class.opt.negative_entropy_lambda)
+                        #     # diffusion_ce_lambda = th.tensor(1e-5).to(dist_util.dev())
+                        #     ce_loss *= diffusion_ce_lambda
+
+                        log_rec3d_loss_dict({
+                            # 'diffusion_ce_lambda': diffusion_ce_lambda,
+                            'negative_entropy': negative_entropy.mean(),
+                            'entropy_weight': entropy_weight,
+                            'ce_loss': ce_loss
+                        })
+
+                        loss += ce_loss
+                    else:
+                        loss += p_loss  # p loss
+
+                
+                # ! do reconstruction supervision 
+
+                # '''
+
+                if ce_flag or not diffusion_flag:  # vae part
+                    latent_to_decode = vae_out
+                else:
+                    latent_to_decode =  { # diffusion part
+                        self.latent_name: ddpm_ret['pred_x0_p']
+                    } # render denoised latent
+
+                # with th.cuda.amp.autocast(dtype=th.float16,
+                #                         enabled=False):
+                # st()
+                if ce_flag or self.renderdiff_loss or not diffusion_flag:
+                    # ! do vae latent -> triplane decode
+                    latent_to_decode.update(self.ddp_rec_model(latent=latent_to_decode, behaviour='decode_after_vae_no_render'))  # triplane, 19mib bs=4
+                    
+                    # ! do render
+                    # st()
+                    pred_nv_cano = self.ddp_rec_model( # 24gb, bs=4
+                        # latent=latent.expand(2,),
+                        latent={
+                            'latent_after_vit': # ! triplane for rendering
+                            latent_to_decode['latent_after_vit'].repeat(2, 1, 1, 1)
+                        },
+                        c=th.cat([micro['nv_c'],
+                                micro['c']]),  # predict novel view here
+                        behaviour='triplane_dec',
+                        # ray_origins=target['ray_origins'],
+                        # ray_directions=target['ray_directions'],
+                        ray_origins=th.cat(
+                            [target['ray_origins'], cano_target['ray_origins']],
+                            0),
+                        ray_directions=th.cat([
+                            target['ray_directions'], cano_target['ray_directions']
+                        ]),
+                    )
+
+                    pred_nv_cano.update({ # for kld
+                        'posterior': vae_out['posterior'],
+                        'latent_normalized_2Ddiffusion': vae_out['latent_normalized_2Ddiffusion']
+                    })
+                    
+                    # ! 2D loss
+
+                    with self.ddp_model.no_sync():  # type: ignore
+                        loss_rec, loss_rec_dict, _ = self.loss_class(
+                            pred_nv_cano,
+                            {
+                                k: th.cat([v, cano_cropped_target[k]], 0)
+                                for k, v in cropped_target.items()
+                            },  # prepare merged data
+                            step=self.step + self.resume_step,
+                            test_mode=False,
+                            return_fg_mask=True,
+                            conf_sigma_l1=None,
+                            conf_sigma_percl=None)
+
+                        if diffusion_flag and not ce_flag:
+                            prefix = 'denoised_'
+                        else:
+                            prefix = ''
+
+                        log_rec3d_loss_dict({
+                            f'{prefix}{k}': v for k, v in loss_rec_dict.items()
+                        })
+
+                    loss += loss_rec # l2, LPIPS, Alpha loss
+
+                    # save image log
+                    # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                    #     self.cano_ddpm_log(cano_pred, micro, ddpm_ret)
+
+            self.mp_trainer.backward(loss) # grad accumulation, 27gib
+
+            # st()
+
+            # for name, p in self.model.named_parameters():
+            #     if p.grad is None:
+            #         logger.log(f"found rec unused param: {name}")
+
+        # _ = self.mp_trainer.optimize(self.opt, clip_grad=self.loss_class.opt.grad_clip)
+        _ = self.mp_trainer.optimize(self.opt, clip_grad=True)
+
+        if dist_util.get_rank() == 0:
+            if  self.step % 500 == 0: # log diffusion
+                self.log_diffusion_images(vae_out, p_sample_batch, micro, ddpm_ret)
+            elif self.step % 500 == 1 and ce_flag: # log reconstruction
+                # st()
+                micro_bs = micro['img_to_encoder'].shape[0]
+                self.log_patch_img(
+                    cropped_target,
+                    {
+                        k: pred_nv_cano[k][:micro_bs]
+                        for k in ['image_raw', 'image_depth', 'image_mask']
+                    },
+                    {
+                        k: pred_nv_cano[k][micro_bs:]
+                        for k in ['image_raw', 'image_depth', 'image_mask']
+                    },
+                )
+
+    def _init_optim_groups(self, rec_model, freeze_decoder=False):
+        # unfreeze decoder when scaling is enabled
+        return super()._init_optim_groups(rec_model, freeze_decoder=True)
+
+# class TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED_nv_noCE(TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED_nv):
+#     """no sepatate CE schedule, use single schedule for joint ddpm/nv-rec training with entropy regularization
+#     """
+#     def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, init_cvD=False, diffusion_ce_anneal=False, **kwargs):
+#         super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, init_cvD=init_cvD, diffusion_ce_anneal=diffusion_ce_anneal, **kwargs)
+
+#     def run_loop(self):
+#         while (not self.lr_anneal_steps
+#                or self.step + self.resume_step < self.lr_anneal_steps):
+
+#             batch = self.next_n_batch(n=2) # effective BS=2*8
+#             self.run_step(batch, 'ddpm')
+
+#             # if self.step % self.inner_loop_k == 1: # train E per 2 steps
+#             batch = next(self.data) # sample a new batch for rec training
+#             self.run_step(self.subset_batch(batch, micro_batchsize=6, big_endian=False), 'ce_ED') # freeze D, train E with diffusion prior
+
+#             self._post_run_step()
+
+#         # Save the last checkpoint if it wasn't already saved.
+#         if (self.step - 1) % self.save_interval != 0:
+#             self.save()
\ No newline at end of file
diff --git a/nsr/lsgm/train_util_diffusion_lsgm_noD.py b/nsr/lsgm/train_util_diffusion_lsgm_noD.py
new file mode 100644
index 0000000000000000000000000000000000000000..ced343581dc0fb809557bb2722b88f4cab90c3d0
--- /dev/null
+++ b/nsr/lsgm/train_util_diffusion_lsgm_noD.py
@@ -0,0 +1,936 @@
+"""
+Modified from:
+https://github.com/NVlabs/LSGM/blob/main/training_obj_joint.py
+"""
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+import dnnlib
+from dnnlib.util import requires_grad
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+# import utils as lsgm_utils
+
+
+class TrainLoop3DDiffusionLSGM_noD(TrainLoop3DDiffusion):
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 sde_diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 schedule_sampler=None,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 ignore_resume_opt=False,
+                 freeze_ae=False,
+                 denoised_ae=True,
+                 triplane_scaling_divider=10,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 **kwargs):
+        super().__init__(
+            rec_model=rec_model,
+            denoise_model=denoise_model,
+            diffusion=diffusion,
+            loss_class=loss_class,
+            data=data,
+            eval_data=eval_data,
+            batch_size=batch_size,
+            microbatch=microbatch,
+            lr=lr,
+            ema_rate=ema_rate,
+            log_interval=log_interval,
+            eval_interval=eval_interval,
+            save_interval=save_interval,
+            resume_checkpoint=resume_checkpoint,
+            use_fp16=use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            schedule_sampler=schedule_sampler,
+            weight_decay=weight_decay,
+            lr_anneal_steps=lr_anneal_steps,
+            iterations=iterations,
+            ignore_resume_opt=ignore_resume_opt,
+            #  freeze_ae=freeze_ae,
+            freeze_ae=not sde_diffusion.args.train_vae,
+            denoised_ae=denoised_ae,
+            triplane_scaling_divider=triplane_scaling_divider,
+            use_amp=use_amp,
+            diffusion_input_size=diffusion_input_size,
+            **kwargs)
+
+        assert sde_diffusion is not None
+        sde_diffusion.args.batch_size = batch_size
+        self.sde_diffusion = sde_diffusion
+        self.latent_name = 'latent_normalized_2Ddiffusion'  # normalized triplane latent
+        self.render_latent_behaviour = 'decode_after_vae'  # directly render using triplane operations
+
+        self.pool_512 = th.nn.AdaptiveAvgPool2d((512, 512))
+        self.pool_256 = th.nn.AdaptiveAvgPool2d((256, 256))
+        self.pool_128 = th.nn.AdaptiveAvgPool2d((128, 128))
+        self.pool_64 = th.nn.AdaptiveAvgPool2d((64, 64))
+
+        self.ddp_ddpm_model = self.ddp_model
+
+        # if sde_diffusion.args.joint_train:
+        # assert sde_diffusion.args.train_vae
+
+    def run_step(self, batch, step='diffusion_step_rec'):
+
+        # if step == 'diffusion_step_rec':
+
+        self.forward_diffusion(batch, behaviour='diffusion_step_rec')
+
+        # if took_step_ddpm:
+        self._update_ema()
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, step='diffusion_step_rec')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_ddpm_sample()
+                    if self.sde_diffusion.args.train_vae:
+                        self.eval_loop()
+
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save(self.mp_trainer, self.mp_trainer.model_name)
+                if self.sde_diffusion.args.train_vae:
+                    self.save(self.mp_trainer_rec,
+                              self.mp_trainer_rec.model_name)
+
+                # dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save(self.mp_trainer, self.mp_trainer.model_name)
+                    if self.sde_diffusion.args.train_vae:
+                        self.save(self.mp_trainer_rec,
+                                  self.mp_trainer_rec.model_name)
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            # self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    # ! duplicated code, needs refactor later
+    def ddpm_step(self, eps, t, logsnr, model_kwargs={}):
+        """helper function for ddpm predictions; returns predicted eps, x0 and logsnr
+        """
+        args = self.sde_diffusion.args
+        pred_params = self.ddp_ddpm_model(eps, t, **model_kwargs)
+        # pred_params = self.ddp_model(eps, t, **model_kwargs)
+        if args.pred_type == 'eps':
+            pred_eps = pred_params
+            pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+                eps, pred_params, logsnr)  # for VAE loss, denosied latent
+        elif args.pred_type == 'x0':
+            # ! transform to pred_eps format for mixing_component
+            pred_x0 = pred_params
+            pred_eps = self.sde_diffusion._predict_eps_from_x0(
+                eps, pred_params, logsnr)
+        else:
+            raise NotImplementedError(f'{args.pred_type} not implemented.')
+
+        return pred_eps, pred_x0, logsnr
+
+    # def apply_model(self, p_sample_batch, model_kwargs={}):
+    #     # args = self.sde_diffusion.args
+    #     noise, eps_t_p, t_p, logsnr_p, obj_weight_t_p, var_t_p = (
+    #         p_sample_batch[k] for k in ('noise', 'eps_t_p', 't_p', 'logsnr_p',
+    #                                     'obj_weight_t_p', 'var_t_p'))
+
+    #     pred_eps_p, pred_x0_p, logsnr_p = self.ddpm_step(
+    #         eps_t_p, t_p, logsnr_p, model_kwargs)
+
+    #     # ! batchify for mixing_component
+    #     # mixing normal trick
+    #     mixing_component = self.sde_diffusion.mixing_component(
+    #         eps_t_p, var_t_p, t_p, enabled=True)  # TODO, which should I use?
+    #     pred_eps_p = get_mixed_prediction(
+    #         True, pred_eps_p,
+    #         self.ddp_ddpm_model(x=None,
+    #                             timesteps=None,
+    #                             get_attr='mixing_logit'), mixing_component)
+
+    #     # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+    #     with self.ddp_ddpm_model.no_sync():  # type: ignore
+    #         l2_term_p = th.square(pred_eps_p - noise)  # ? weights
+
+    #     p_eps_objective = th.mean(obj_weight_t_p * l2_term_p)
+
+    #     log_rec3d_loss_dict(
+    #         dict(mixing_logit=self.ddp_ddpm_model(
+    #             x=None, timesteps=None, get_attr='mixing_logit').detach(), ))
+
+    #     return {
+    #         'pred_eps_p': pred_eps_p,
+    #         'eps_t_p': eps_t_p,
+    #         'p_eps_objective': p_eps_objective,
+    #         'pred_x0_p': pred_x0_p,
+    #         'logsnr_p': logsnr_p
+    #     }
+
+    def forward_diffusion(self, batch, behaviour='rec', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+
+        # self.ddp_ddpm_model.requires_grad_(True)
+        requires_grad(self.ddp_rec_model.module, args.train_vae)
+        # self.ddp_rec_model.requires_grad_(args.train_vae)
+
+        if args.train_vae:
+            for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters(  # type: ignore
+            ):  # type: ignore
+                param.requires_grad_(
+                    False
+                )  # ! disable triplane_decoder grad in each iteration indepenently;
+
+        self.mp_trainer_rec.zero_grad()
+        self.mp_trainer.zero_grad()
+
+        batch_size = batch['img'].shape[0]
+
+        # # update ddpm params
+        # took_step_ddpm = self.mp_trainer_ddpm.optimize(
+        #     self.opt_ddpm)  # TODO, update two groups of parameters
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            q_vae_recon_loss = th.tensor(0.0).to(dist_util.dev())
+            # vision_aided_loss = th.tensor(0.0).to(dist_util.dev())
+            # denoise_loss = th.tensor(0.0).to(dist_util.dev())
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+                #   and args.train_vae):
+
+                assert behaviour == 'diffusion_step_rec'
+
+                # ! train vae with CE; ddpm fixed
+                requires_grad(self.ddp_model.module, False)
+                # if args.train_vae:
+                #     assert args.add_rendering_loss
+                with th.set_grad_enabled(args.train_vae):
+                    vae_out = self.ddp_rec_model(
+                        img=micro['img_to_encoder'],
+                        c=micro['c'],
+                        # behaviour='enc_dec_wo_triplane'
+                        behaviour='encoder_vae',
+                    )  # pred: (B, 3, 64, 64)
+                    # TODO, no need to render if not SSD; no need to do ViT decoder if only the latent is needed. update later
+
+                # TODO, train diff and sds together, available?
+                all_log_q = [vae_out['log_q_2Ddiffusion']]
+                eps = vae_out[self.latent_name]
+                eps.requires_grad_(True)  # single stage diffusion
+
+                # t, weights = self.schedule_sampler.sample(
+                #     eps.shape[0], dist_util.dev())
+
+                noise = th.randn(
+                    size=eps.size(), device=eps.device
+                )  # note that this noise value is currently shared!
+                model_kwargs = {}
+
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+                t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                    self.sde_diffusion.iw_quantities(args.iw_sample_p)
+                eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p,
+                                                      m_t_p)
+                logsnr_p = self.sde_diffusion.log_snr(m_t_p,
+                                                      var_t_p)  # for p only
+
+                # in case we want to train q (vae) with another batch using a different sampling scheme for times t
+                if args.iw_sample_q in ['ll_uniform', 'll_iw']:
+                    t_q, var_t_q, m_t_q, obj_weight_t_q, _, g2_t_q = \
+                        self.sde_diffusion.iw_quantities(args.iw_sample_q)
+                    eps_t_q = self.sde_diffusion.sample_q(
+                        eps, noise, var_t_q, m_t_q)
+
+                    eps_t_p = eps_t_p.detach().requires_grad_(
+                        True)  # ! p just not updated here
+                    eps_t = th.cat([eps_t_p, eps_t_q], dim=0)
+                    var_t = th.cat([var_t_p, var_t_q], dim=0)
+                    t = th.cat([t_p, t_q], dim=0)
+                    noise = th.cat([noise, noise], dim=0)
+                    # logsnr = self.sde_diffusion.log_snr(m_t_q, var_t_p)
+                else:
+                    eps_t, m_t, var_t, t, g2_t = eps_t_p, m_t_p, var_t_p, t_p, g2_t_p
+
+                # run the diffusion model
+                eps_t.requires_grad_(True)  # 2*BS, 12, 16, 16
+                pred_params = self.ddp_model(eps_t, t, **model_kwargs)
+
+                if args.pred_type == 'eps':
+                    pred_eps = pred_params
+                elif args.pred_type == 'x0':
+                    # ! transform to pred_eps format for mixing_component
+                    pred_eps = self.sde_diffusion._predict_eps_from_x0(
+                        eps_t, pred_params, logsnr_p)
+                else:
+                    raise NotImplementedError(
+                        f'{args.pred_type} not implemented.')
+
+                # mixing normal trick
+                mixing_component = self.sde_diffusion.mixing_component(
+                    eps_t, var_t, t, enabled=True)  # TODO, which should I use?
+                pred_eps = get_mixed_prediction(
+                    # True, pred_params,
+                    True,
+                    pred_eps,
+                    self.ddp_model(x=None,
+                                   timesteps=None,
+                                   get_attr='mixing_logit'),
+                    mixing_component)
+
+                # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+                if last_batch or not self.use_ddp:
+                    l2_term = th.square(pred_eps - noise)
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        l2_term = th.square(pred_eps - noise)  # ? weights
+
+                # nelbo loss with kl balancing
+                # ! remainign parts of cross entropy in likelihook training
+                # unpack separate objectives, in case we want to train q (vae) using a different sampling scheme for times t
+                if args.iw_sample_q in ['ll_uniform',
+                                        'll_iw']:  # ll_iw by default
+                    l2_term_p, l2_term_q = th.chunk(l2_term, chunks=2, dim=0)
+                    p_objective = th.mean(obj_weight_t_p * l2_term_p,
+                                          dim=[1, 2, 3])
+                    cross_entropy_per_var = obj_weight_t_q * l2_term_q
+                else:
+                    p_objective = th.mean(obj_weight_t_p * l2_term,
+                                          dim=[1, 2, 3])
+                    cross_entropy_per_var = obj_weight_t_q * l2_term
+
+                cross_entropy_per_var += self.sde_diffusion.cross_entropy_const(
+                    args.sde_time_eps)
+                all_neg_log_p = [cross_entropy_per_var
+                                 ]  # since only one vae group
+
+                kl_all_list, kl_vals_per_group, kl_diag_list = kl_per_group_vada(
+                    all_log_q, all_neg_log_p)  # return the mean of two terms
+
+                # nelbo loss with kl balancing
+                balanced_kl, kl_coeffs, kl_vals = kl_balancer(kl_all_list,
+                                                              kl_coeff=1.0,
+                                                              kl_balance=False,
+                                                              alpha_i=None)
+
+                # ! update vae for CE
+                # ! single stage diffusion for rec side 1: bind vae prior and diffusion prior
+                if args.train_vae:
+                    # if args.add_rendering_loss:
+                    # if args.joint_train:
+                    with th.set_grad_enabled(args.train_vae):
+                        target = micro
+                        pred = self.ddp_rec_model(
+                            latent=vae_out,
+                            # latent={
+                            #     **vae_out, self.latent_name: pred_x0,
+                            #     'latent_name': self.latent_name
+                            # },
+                            c=micro['c'],
+                            behaviour=self.render_latent_behaviour)
+
+                        # vae reconstruction loss
+                        if last_batch or not self.use_ddp:
+                            q_vae_recon_loss, loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+                        else:
+                            with self.ddp_model.no_sync():  # type: ignore
+                                q_vae_recon_loss, loss_dict = self.loss_class(
+                                    pred, target, test_mode=False)
+
+                        log_rec3d_loss_dict(loss_dict)
+
+                # ! calculate p/q loss;
+                nelbo_loss = balanced_kl + q_vae_recon_loss
+                q_loss = th.mean(nelbo_loss)
+                p_loss = th.mean(p_objective)
+
+                log_rec3d_loss_dict(
+                    dict(
+                        q_vae_recon_loss=q_vae_recon_loss,
+                        p_loss=p_loss,
+                        balanced_kl=balanced_kl,
+                        mixing_logit=self.ddp_model(
+                            x=None, timesteps=None,
+                            get_attr='mixing_logit').detach(),
+                    ))
+
+                # ! single stage diffusion for rec side 2: generative feature
+                if args.p_rendering_loss:
+                    with th.set_grad_enabled(args.train_vae):
+
+                        # ! transform fro pred_eps format back to pred_x0, for p only.
+                        pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+                            eps_t_p, pred_eps[:eps_t_p.shape[0]],
+                            logsnr_p)  # for VAE loss, denosied latent
+
+                        target = micro
+                        pred = self.ddp_rec_model(
+                            # latent=vae_out,
+                            latent={
+                                **vae_out, self.latent_name: pred_x0,
+                                'latent_name': self.latent_name
+                            },
+                            c=micro['c'],
+                            behaviour=self.render_latent_behaviour)
+
+                        # vae reconstruction loss
+                        if last_batch or not self.use_ddp:
+                            p_vae_recon_loss, loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+                        else:
+                            with self.ddp_model.no_sync():  # type: ignore
+                                p_vae_recon_loss, loss_dict = self.loss_class(
+                                    pred, target, test_mode=False)
+                        log_rec3d_loss_dict(
+                            dict(p_vae_recon_loss=p_vae_recon_loss, ))
+
+            # ! backpropagate q_loss for vae and update vae params, if trained
+            if args.train_vae:
+                self.mp_trainer_rec.backward(
+                    q_loss,
+                    retain_graph=different_p_q_objectives(
+                        args.iw_sample_p, args.iw_sample_q))
+
+            # if we use different p and q objectives or are not training the vae, discard gradients and backpropagate p_loss
+            if different_p_q_objectives(
+                    args.iw_sample_p, args.iw_sample_q) or not args.train_vae:
+                if args.train_vae:
+                    # discard current gradients computed by weighted loss for VAE
+                    self.mp_trainer_rec.zero_grad()
+
+                self.mp_trainer.backward(p_loss)
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            if dist_util.get_rank(
+            ) == 0 and self.step % 500 == 0 and behaviour != 'diff':
+
+                with th.no_grad():
+
+                    if not args.train_vae:
+                        vae_out.pop('posterior')  # for calculating kl loss
+                        vae_out_for_pred = {
+                            k: v[0:1].to(dist_util.dev()) if isinstance(
+                                v, th.Tensor) else v
+                            for k, v in vae_out.items()
+                        }
+
+                        pred = self.ddp_rec_model(
+                            latent=vae_out_for_pred,
+                            c=micro['c'][0:1],
+                            behaviour=self.render_latent_behaviour)
+                    assert isinstance(pred, dict)
+                    assert pred is not None
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+
+                    # pred_depth = pred['image_depth']
+                    # pred_depth = (pred_depth - pred_depth.min()) / (
+                    #     pred_depth.max() - pred_depth.min())
+
+                    if 'image_depth' in pred:
+                        pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_img = self.pool_64(gt_img)
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'], micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+                    noised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=eps_t_p[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically
+                        behaviour=self.render_latent_behaviour)
+
+                    # ! test time, use discrete diffusion model
+                    params_p, _ = th.chunk(pred_eps, chunks=2,
+                                           dim=0)  # get predicted noise
+
+                    # TODO, implement for SDE difusion?
+                    # ! two values isclose(rtol=1e-03, atol=1e-04)
+                    # pred_xstart = self.diffusion._predict_xstart_from_eps(
+                    #     x_t=eps_t_p,
+                    #     t=th.tensor(t_p.detach() *
+                    #                 self.diffusion.num_timesteps).long(),
+                    #     eps=params_p)
+
+                    pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+                        eps_t_p, params_p,
+                        logsnr_p)  # for VAE loss, denosied latent
+
+                    # pred_xstart_3D
+                    denoised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=pred_x0[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                        behaviour=self.render_latent_behaviour)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], noised_ae_pred['image_raw'][0:1],
+                        denoised_ae_pred['image_raw'][0:1],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                    )
+                    del vis, pred_vis, pred_x0, pred_eps, micro, vae_out
+
+                    th.cuda.empty_cache()
+
+    # ! copied from train_util.py
+    # TODO, needs to lint the class inheritance chain later.
+    @th.inference_mode()
+    def eval_novelview_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        all_loss_dict = []
+        novel_view_micro = {}
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i == 0:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            pred = self.rec_model(img=novel_view_micro['img_to_encoder'],
+                                  c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+            # targe
+
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            if 'image_sr' in pred:
+
+                if pred['image_sr'].shape[-1] == 512:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), pred['image_sr'],
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                elif pred['image_sr'].shape[-1] == 256:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_256(pred['image_raw']), pred['image_sr'],
+                        self.pool_256(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                else:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred['image_sr']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+            else:
+                pred_vis = th.cat([
+                    self.pool_64(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+                  'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+                                   self.step + self.resume_step)
+        del video_out, vis, pred_vis, pred, micro
+        th.cuda.empty_cache()
+
+    # @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    @th.inference_mode()
+    def eval_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+        all_loss_dict = []
+        self.rec_model.eval()
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            pred = self.rec_model(img=micro['img_to_encoder'],
+                                  c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+
+            # if last_batch or not self.use_ddp:
+            #     loss, loss_dict = self.loss_class(pred, target)
+            # else:
+            #     with self.ddp_model.no_sync():  # type: ignore
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+            # gt_vis = th.cat([micro['img'], micro['img']], dim=-1) # TODO, fail to load depth. range [0, 1]
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+            # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(1,2,0).cpu().numpy() # ! pred in range[-1, 1]
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+
+                if pred['image_sr'].shape[-1] == 512:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), pred['image_sr'],
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                elif pred['image_sr'].shape[-1] == 256:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_256(pred['image_raw']), pred['image_sr'],
+                        self.pool_256(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                else:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred['image_sr']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+            else:
+                pred_vis = th.cat([
+                    self.pool_64(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores.json'), 'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/Rec/{k}', v,
+                                   self.step + self.resume_step)
+
+        del video_out, vis, pred_vis, pred, micro
+        th.cuda.empty_cache()
+        self.eval_novelview_loop()
+        self.rec_model.train()
+
+    # for compatablity with p_sample, to lint
+    def apply_model_inference(self, x_noisy, t, c=None, model_kwargs={}):
+        # control = self.ddp_control_model(x=x_noisy,
+        #                                  hint=th.cat(c['c_concat'], 1),
+        #                                  timesteps=t,
+        #                                  context=None)
+        # control = [c * scale for c, scale in zip(control, self.control_scales)]
+        pred_params = self.ddp_ddpm_model(x_noisy, t, 
+                                    **model_kwargs
+                                    )
+
+
+        assert args.pred_type == 'eps'
+        # mixing normal trick
+        mixing_component = self.sde_diffusion.mixing_component(
+            eps, var_t, t, enabled=True)  # TODO, which should I use?
+        pred_eps = get_mixed_prediction(
+            True, pred_eps,
+            self.ddp_ddpm_model(x=None, timesteps=None, get_attr='mixing_logit'), mixing_component)
+
+        return pred_params
+
+    @th.inference_mode()
+    def eval_ddpm_sample(self):
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=self.diffusion_input_size,
+                denoise_in_channels=self.ddp_rec_model.module.decoder.
+                triplane_decoder.out_chans,  # type: ignore
+                clip_denoised=False,
+                class_cond=False,
+                use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        for i in range(1):
+            triplane_sample = sample_fn(
+                # self.ddp_model,
+                self,
+                (
+                    args.batch_size,
+                    self.ddp_rec_model.module.decoder.ldm_z_channels *
+                    3,  # type: ignore
+                    self.diffusion_input_size,
+                    self.diffusion_input_size),
+                clip_denoised=args.clip_denoised,
+                model_kwargs=model_kwargs,
+                mixing_normal=True,  # !
+            )
+            th.cuda.empty_cache()
+
+            self.render_video_given_triplane(
+                triplane_sample,
+                name_prefix=f'{self.step + self.resume_step}_{i}')
+            # st()
+            del triplane_sample
+            th.cuda.empty_cache()
diff --git a/nsr/lsgm/train_util_diffusion_lsgm_noD_joint.py b/nsr/lsgm/train_util_diffusion_lsgm_noD_joint.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed0d1b77ac908488a7b0af068bdb798be83a2b75
--- /dev/null
+++ b/nsr/lsgm/train_util_diffusion_lsgm_noD_joint.py
@@ -0,0 +1,1376 @@
+"""
+Modified from:
+https://github.com/NVlabs/LSGM/blob/main/training_obj_joint.py
+"""
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+from dnnlib.util import requires_grad
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion, TrainLoopDiffusionWithRec
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer
+# import utils as lsgm_utils
+
+
+class JointDenoiseRecModel(th.nn.Module):
+
+    def __init__(self, ddpm_model, rec_model, diffusion_input_size) -> None:
+        super().__init__()
+        # del ddpm_model
+        # th.cuda.empty_cache()
+        # self.ddpm_model = th.nn.Identity()
+        self.ddpm_model = ddpm_model
+        self.rec_model = rec_model
+
+        self._setup_latent_stat(diffusion_input_size)
+
+    def _setup_latent_stat(self, diffusion_input_size): # for dynamic EMA tracking.
+        latent_size = (
+            1,
+            self.ddpm_model.in_channels,  # type: ignore
+            diffusion_input_size,
+            diffusion_input_size),
+
+        self.ddpm_model.register_buffer(
+            'ema_latent_std',
+            th.ones(*latent_size).to(dist_util.dev()), persistent=True)
+        self.ddpm_model.register_buffer(
+            'ema_latent_mean',
+            th.zeros(*latent_size).to(dist_util.dev()), persistent=True)
+
+    # TODO, lint api.
+    def forward(
+        self,
+        *args,
+        model_name='ddpm',
+        **kwargs,
+    ):
+        if model_name == 'ddpm':
+            return self.ddpm_model(*args, **kwargs)
+        elif model_name == 'rec':
+            return self.rec_model(*args, **kwargs)
+        else:
+            raise NotImplementedError(model_name)
+
+
+# TODO, merge with train_util_diffusion.py later
+class SDETrainLoopJoint(TrainLoopDiffusionWithRec):
+    """A dataclass with some required attribtues; copied from guided_diffusion TrainLoop
+    """
+
+    def __init__(
+        self,
+        rec_model,
+        denoise_model,
+        diffusion,  # not used
+        sde_diffusion,
+        loss_class,
+        data,
+        eval_data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        eval_interval,
+        save_interval,
+        resume_checkpoint,
+        use_fp16=False,
+        fp16_scale_growth=0.001,
+        weight_decay=0,
+        lr_anneal_steps=0,
+        iterations=10001,
+        triplane_scaling_divider=1,
+        use_amp=False,
+        diffusion_input_size=224,
+        train_vae=False,
+        **kwargs,
+    ) -> None:
+
+        joint_model = JointDenoiseRecModel(denoise_model, rec_model, diffusion_input_size)
+        super().__init__(
+            model=joint_model,
+            diffusion=diffusion,  # just for sampling
+            loss_class=loss_class,
+            data=data,
+            eval_data=eval_data,
+            eval_interval=eval_interval,
+            batch_size=batch_size,
+            microbatch=microbatch,
+            lr=lr,
+            ema_rate=ema_rate,
+            log_interval=log_interval,
+            save_interval=save_interval,
+            resume_checkpoint=resume_checkpoint,
+            use_fp16=use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            weight_decay=weight_decay,
+            lr_anneal_steps=lr_anneal_steps,
+            use_amp=use_amp,
+            model_name='joint_denoise_rec_model',
+            iterations=iterations,
+            triplane_scaling_divider=triplane_scaling_divider,
+            diffusion_input_size=diffusion_input_size,
+            train_vae=train_vae,
+            **kwargs)
+        self.sde_diffusion = sde_diffusion
+        # setup latent scaling factor
+
+    # ! integrate the init_params_group for rec model
+    def _setup_model(self):
+
+        super()._setup_model()
+        self.ddp_rec_model = functools.partial(self.model, model_name='rec')
+        self.ddp_ddpm_model = functools.partial(self.model, model_name='ddpm')
+
+        self.rec_model = self.ddp_model.module.rec_model
+        self.ddpm_model = self.ddp_model.module.ddpm_model  # compatability
+
+        # TODO, required?
+        # for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters(  # type: ignore
+        # ):  # type: ignore
+        #     param.requires_grad_(
+        #         False
+        #     )  # ! disable triplane_decoder grad in each iteration indepenently;
+
+    def _load_model(self):
+        # TODO, for currently compatability
+        if 'joint' in self.resume_checkpoint: # load joint directly
+            self._load_and_sync_parameters(model=self.model, model_name=self.model_name)
+        else: # from scratch
+            self._load_and_sync_parameters(model=self.rec_model, model_name='rec')
+            self._load_and_sync_parameters(model=self.ddpm_model,
+                                        model_name='ddpm')
+
+    def _setup_opt(self):
+        # TODO, two optims groups.
+        self.opt = AdamW([{
+            'name': 'ddpm',
+            'params': self.ddpm_model.parameters(),
+        }],
+                         lr=self.lr,
+                         weight_decay=self.weight_decay)
+
+        if self.train_vae:
+            for rec_param_group in self._init_optim_groups(self.rec_model):
+                self.opt.add_param_group(rec_param_group)
+
+        print(self.opt)
+
+
+class TrainLoop3DDiffusionLSGMJointnoD(SDETrainLoopJoint):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 sde_diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 resume_cldm_checkpoint=None,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 triplane_scaling_divider=1,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 diffusion_ce_anneal=False,
+                #  compile=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         sde_diffusion=sde_diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         **kwargs)
+
+        if sde_diffusion is not None:
+            sde_diffusion.args.batch_size = batch_size
+        self.latent_name = 'latent_normalized_2Ddiffusion'  # normalized triplane latent
+        self.render_latent_behaviour = 'decode_after_vae'  # directly render using triplane operations
+        self.diffusion_ce_anneal = diffusion_ce_anneal
+        # assert sde_diffusion.args.train_vae
+
+    def prepare_ddpm(self, eps, mode='p'):
+
+        log_rec3d_loss_dict({
+            f'eps_mean': eps.mean(),
+            f'eps_std': eps.std([1,2,3]).mean(0),
+            f'eps_max': eps.max()
+        })
+
+        args = self.sde_diffusion.args
+        # sample noise
+        noise = th.randn(size=eps.size(), device=eps.device
+                         )  # note that this noise value is currently shared!
+
+        # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+        if mode == 'p':
+            t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                self.sde_diffusion.iw_quantities(args.iw_sample_p, noise.shape[0]) # TODO, q not used, fall back to original ddpm implementation
+        else:
+            assert mode == 'q'
+            # assert args.iw_sample_q in ['ll_uniform', 'll_iw']
+            t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                self.sde_diffusion.iw_quantities(args.iw_sample_q, noise.shape[0]) # TODO, q not used, fall back to original ddpm implementation
+        eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p, m_t_p)
+        # ! important
+        # eps_t_p = eps_t_p.detach().requires_grad_(True)
+        # logsnr_p = self.sde_diffusion.log_snr(m_t_p,
+        #                                         var_t_p)  # for p only
+        logsnr_p = self.sde_diffusion.log_snr(m_t_p, var_t_p)  # for p only
+
+        return {
+            'noise': noise,
+            't_p': t_p,
+            'eps_t_p': eps_t_p,
+            'logsnr_p': logsnr_p,
+            'obj_weight_t_p': obj_weight_t_p,
+            'var_t_p': var_t_p,
+            'm_t_p': m_t_p,
+            'eps': eps,
+            'mode': mode
+        }
+
+    # merged from noD.py
+
+    def ce_weight(self):
+        return self.loss_class.opt.ce_lambda
+
+    def apply_model(self, p_sample_batch, **model_kwargs):
+        args = self.sde_diffusion.args
+        # args = self.sde_diffusion.args
+        noise, eps_t_p, t_p, logsnr_p, obj_weight_t_p, var_t_p, m_t_p = (
+            p_sample_batch[k] for k in ('noise', 'eps_t_p', 't_p', 'logsnr_p',
+                                        'obj_weight_t_p', 'var_t_p', 'm_t_p'))
+
+        pred_eps_p, pred_x0_p = self.ddpm_step(eps_t_p, t_p, logsnr_p, var_t_p, m_t_p,
+                                               **model_kwargs)
+
+        # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+        with self.ddp_model.no_sync():  # type: ignore
+            if args.loss_type == 'eps':
+                l2_term_p = th.square(pred_eps_p - noise)  # ? weights
+            elif args.loss_type == 'x0':
+                # l2_term_p = th.square(pred_eps_p - p_sample_batch['eps'])  # ? weights
+                l2_term_p = th.square(
+                    pred_x0_p - p_sample_batch['eps'].detach())  # ? weights
+                # if args.loss_weight == 'snr':
+                # obj_weight_t_p = th.sigmoid(th.exp(logsnr_p))
+            else:
+                raise NotImplementedError(args.loss_type)
+
+        # p_eps_objective = th.mean(obj_weight_t_p * l2_term_p)
+        p_eps_objective = obj_weight_t_p * l2_term_p
+
+        if p_sample_batch['mode'] == 'q':
+            ce_weight = self.ce_weight()
+            p_eps_objective = p_eps_objective * ce_weight
+
+            log_rec3d_loss_dict({
+                'ce_weight': ce_weight,
+            })
+
+
+        log_rec3d_loss_dict({
+            f"{p_sample_batch['mode']}_loss":
+            p_eps_objective.mean(),
+        })
+        if self.ddpm_model.mixed_prediction:
+            log_rec3d_loss_dict({
+                'mixing_logit':
+                self.ddp_ddpm_model(x=None,
+                                    timesteps=None,
+                                    get_attr='mixing_logit').detach(),})
+
+        return {
+            'pred_eps_p': pred_eps_p,
+            'eps_t_p': eps_t_p,
+            'p_eps_objective': p_eps_objective,
+            'pred_x0_p': pred_x0_p,
+            'logsnr_p': logsnr_p
+        }
+
+    def ddpm_step(self, eps_t, t, logsnr, var_t, m_t, **model_kwargs):
+        """helper function for ddpm predictions; returns predicted eps, x0 and logsnr. 
+
+        args notes: 
+            eps_t is x_noisy
+        """
+        args = self.sde_diffusion.args
+        pred_params = self.ddp_ddpm_model(x=eps_t, timesteps=t, **model_kwargs)
+        # logsnr = self.sde_diffusion.log_snr(m_t, var_t)  # for p only
+        if args.pred_type in ['eps', 'v']:
+            if args.pred_type == 'v':
+                pred_eps = self.sde_diffusion._predict_eps_from_z_and_v(
+                    pred_params, var_t, eps_t, m_t
+                )
+                # pred_x0 = self.sde_diffusion._predict_x0_from_z_and_v(
+                #     pred_params, var_t, eps_t, m_t)  # ! verified
+            else:
+                pred_eps = pred_params
+
+            # mixing normal trick
+
+            if self.ddpm_model.mixed_prediction:
+                mixing_component = self.sde_diffusion.mixing_component(
+                    eps_t, var_t, t, enabled=True)  # z_t * sigma_t
+                pred_eps = get_mixed_prediction(
+                    True, pred_eps,
+                    self.ddp_ddpm_model(x=None,
+                                        timesteps=None,
+                                        get_attr='mixing_logit'), mixing_component)
+
+            pred_x0 = self.sde_diffusion._predict_x0_from_eps( eps_t, pred_eps, logsnr)  # for VAE loss, denosied latent
+            # eps, pred_params, logsnr)  # for VAE loss, denosied latent
+        elif args.pred_type == 'x0':
+            # ! pred_x0_mixed = alpha * pred_x0 + (1-alpha) * z_t * alpha_t
+            pred_x0 = pred_params  # how to mix?
+
+            # mixing normal trick
+            mixing_component = self.sde_diffusion.mixing_component_x0(
+                eps_t, var_t, t, enabled=True)  # z_t * alpha_t
+            pred_x0 = get_mixed_prediction(
+                True, pred_x0,
+                self.ddp_ddpm_model(x=None,
+                                    timesteps=None,
+                                    get_attr='mixing_logit'), mixing_component)
+
+            pred_eps = self.sde_diffusion._predict_eps_from_x0(
+                eps_t, pred_x0, logsnr)
+        else:
+            raise NotImplementedError(f'{args.pred_type} not implemented.')
+
+        log_rec3d_loss_dict({
+            f'pred_x0_mean': pred_x0.mean(),
+            f'pred_x0_std': pred_x0.std([1,2,3]).mean(0),
+            f'pred_x0_max': pred_x0.max(),
+        })
+
+        return pred_eps, pred_x0
+
+    def ddpm_loss(self, noise, pred_eps, last_batch):
+
+        # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+        if last_batch or not self.use_ddp:
+            l2_term = th.square(pred_eps - noise)
+        else:
+            with self.ddp_model.no_sync():  # type: ignore
+                l2_term = th.square(pred_eps - noise)  # ? weights
+        return l2_term
+
+    def run_step(self, batch, step='diffusion_step_rec'):
+
+        if step == 'ce_ddpm_step':
+            self.ce_ddpm_step(batch)
+        elif step == 'p_rendering_step':
+            self.p_rendering_step(batch)
+
+        elif step == 'eps_step':
+            self.eps_step(batch)
+
+        # ! both took ddpm step
+        self._update_ema()
+
+        self._anneal_lr()
+        self.log_step()
+
+    @th.inference_mode()
+    def _post_run_loop(self):
+
+        # if self.step % self.eval_interval == 0 and self.step != 0:
+        # if self.step % self.eval_interval == 0:
+        #     if dist_util.get_rank() == 0:
+        #         self.eval_ddpm_sample(
+        #             self.rec_model,
+        #             # self.ddpm_model
+        #         )  # ! only support single GPU inference now.
+        #         if self.sde_diffusion.args.train_vae:
+        #             self.eval_loop(self.ddp_rec_model)
+
+        if self.step % self.log_interval == 0 and dist_util.get_rank() == 0:
+            out = logger.dumpkvs()
+            # * log to tensorboard
+            for k, v in out.items():
+                self.writer.add_scalar(f'Loss/{k}', v,
+                                       self.step + self.resume_step)
+
+        # if self.step % self.eval_interval == 0 and self.step != 0:
+        if self.step % self.eval_interval == 0:
+            if dist_util.get_rank() == 0:
+                self.eval_ddpm_sample(self.ddp_rec_model)
+                if self.sde_diffusion.args.train_vae:
+                    self.eval_loop(self.ddp_rec_model)
+
+        if self.step % self.save_interval == 0:
+            self.save(self.mp_trainer, self.mp_trainer.model_name)
+
+        self.step += 1
+
+        if self.step > self.iterations:
+            print('reached maximum iterations, exiting')
+
+            # Save the last checkpoint if it wasn't already saved.
+            if (self.step - 1) % self.save_interval != 0:
+                self.save(self.mp_trainer, self.mp_trainer.model_name)
+            exit()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            batch = next(self.data)
+            self.run_step(batch, step='ce_ddpm_step')
+
+            self._post_run_loop()
+
+            # batch = next(self.data)
+            # self.run_step(batch, step='p_rendering_step')
+
+    def ce_ddpm_step(self, batch, behaviour='rec', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+        assert args.train_vae
+
+        requires_grad(self.rec_model, args.train_vae)
+        requires_grad(self.ddpm_model, True)
+
+        # TODO merge?
+        self.mp_trainer.zero_grad()
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            q_vae_recon_loss = th.tensor(0.0).to(dist_util.dev())
+            # vision_aided_loss = th.tensor(0.0).to(dist_util.dev())
+            # denoise_loss = th.tensor(0.0).to(dist_util.dev())
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+
+                # ! part 1: train vae with CE; ddpm fixed
+                # ! TODO, add KL_all_list? vae.decompose
+                with th.set_grad_enabled(args.train_vae):
+                    # vae_out = self.ddp_rec_model(
+                    #     img=micro['img_to_encoder'],
+                    #     c=micro['c'],
+                    #     behaviour='encoder_vae',
+                    # )  # pred: (B, 3, 64, 64)
+                    # TODO, no need to render if not SSD; no need to do ViT decoder if only the latent is needed. update later
+                    # if args.train_vae:
+                    # if args.add_rendering_loss:
+                    # if args.joint_train:
+                    # with th.set_grad_enabled(args.train_vae):
+                    pred = self.ddp_rec_model(
+                        # latent=vae_out,
+                        img=micro['img_to_encoder'],
+                        c=micro['c'],
+                    )
+                    # behaviour=self.render_latent_behaviour)
+
+                    # vae reconstruction loss
+                    if last_batch or not self.use_ddp:
+                        q_vae_recon_loss, loss_dict = self.loss_class(
+                            pred, micro, test_mode=False)
+                    else:
+                        with self.ddp_model.no_sync():  # type: ignore
+                            q_vae_recon_loss, loss_dict = self.loss_class(
+                                pred, micro, test_mode=False)
+
+                    log_rec3d_loss_dict(loss_dict)
+                # '''
+
+                # ! calculate p/q loss;
+                # nelbo_loss = balanced_kl * self.loss_class.opt.ce_balanced_kl + q_vae_recon_loss
+                nelbo_loss = q_vae_recon_loss
+                q_loss = th.mean(nelbo_loss)
+
+                # st()
+
+                # all_log_q = [vae_out['log_q_2Ddiffusion']]
+                # eps = vae_out[self.latent_name]
+                # all_log_q = [pred['log_q_2Ddiffusion']]
+                eps = pred[self.latent_name]
+
+                if not args.train_vae:
+                    eps.requires_grad_(True)  # single stage diffusion
+
+                # sample noise
+                noise = th.randn(
+                    size=eps.size(), device=eps.device
+                )  # note that this noise value is currently shared!
+
+                # in case we want to train q (vae) with another batch using a different sampling scheme for times t
+                '''
+                assert args.iw_sample_q in ['ll_uniform', 'll_iw']
+                t_q, var_t_q, m_t_q, obj_weight_t_q, _, g2_t_q = \
+                    self.sde_diffusion.iw_quantities(args.iw_sample_q)
+                eps_t_q = self.sde_diffusion.sample_q(eps, noise, var_t_q,
+                                                      m_t_q)
+
+                # eps_t = th.cat([eps_t_p, eps_t_q], dim=0)
+                # var_t = th.cat([var_t_p, var_t_q], dim=0)
+                # t = th.cat([t_p, t_q], dim=0)
+                # noise = th.cat([noise, noise], dim=0)
+
+                # run the diffusion model
+                if not args.train_vae:
+                    eps_t_q.requires_grad_(True)  # 2*BS, 12, 16, 16
+
+                # ! For CE guidance.
+                requires_grad(self.ddpm_model_module, False)
+                pred_eps_q, _, _ = self.ddpm_step(eps_t_q, t_q, m_t_q, var_t_q)
+
+                l2_term_q = self.ddpm_loss(noise, pred_eps_q, last_batch)
+
+                # pred_eps = th.cat([pred_eps_p, pred_eps_q], dim=0)  # p then q
+
+                # ÇE: nelbo loss with kl balancing
+                assert args.iw_sample_q in ['ll_uniform', 'll_iw']
+                # l2_term_p, l2_term_q = th.chunk(l2_term, chunks=2, dim=0)
+                cross_entropy_per_var = obj_weight_t_q * l2_term_q
+
+                cross_entropy_per_var += self.sde_diffusion.cross_entropy_const(
+                    args.sde_time_eps)
+                all_neg_log_p = [cross_entropy_per_var
+                                 ]  # since only one vae group
+
+                kl_all_list, kl_vals_per_group, kl_diag_list = kl_per_group_vada(
+                    all_log_q, all_neg_log_p)  # return the mean of two terms
+
+                # nelbo loss with kl balancing
+                balanced_kl, kl_coeffs, kl_vals = kl_balancer(kl_all_list,
+                                                              kl_coeff=1.0,
+                                                              kl_balance=False,
+                                                              alpha_i=None)
+                # st()
+
+                log_rec3d_loss_dict(
+                    dict(
+                        balanced_kl=balanced_kl,
+                        l2_term_q=l2_term_q,
+                        cross_entropy_per_var=cross_entropy_per_var.mean(),
+                        all_log_q=all_log_q[0].mean(),
+                    ))
+
+
+                '''
+                # ! update vae for CE
+                # ! single stage diffusion for rec side 1: bind vae prior and diffusion prior
+
+            # ! BP for CE and VAE; quit the AMP context.
+            # if args.train_vae:
+            #     self.mp_trainer.backward(q_loss)
+            #     _ = self.mp_trainer.optimize(self.opt)
+            #  retain_graph=different_p_q_objectives(
+            #      args.iw_sample_p,
+            #      args.iw_sample_q))
+
+            log_rec3d_loss_dict(
+                dict(q_vae_recon_loss=q_vae_recon_loss,
+                     # all_log_q=all_log_q[0].mean(),
+                     ))
+
+            # ! adding p loss; enable ddpm gradient
+            # self.mp_trainer.zero_grad()
+            # requires_grad(self.rec_model_module,
+            #               False)  # could be removed since eps_t_p.detach()
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+
+                # first get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+                t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                    self.sde_diffusion.iw_quantities(args.iw_sample_p)
+                eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p,
+                                                      m_t_p)
+                eps_t_p = eps_t_p.detach(
+                )  # .requires_grad_(True)  # ! update ddpm not rec module
+
+                pred_eps_p, _, = self.ddpm_step(eps_t_p, t_p, m_t_p, var_t_p)
+                l2_term_p = self.ddpm_loss(noise, pred_eps_p, last_batch)
+                p_loss = th.mean(obj_weight_t_p * l2_term_p)
+
+            # ! update ddpm
+            self.mp_trainer.backward(p_loss +
+                                     q_loss)  # just backward for p_loss
+            _ = self.mp_trainer.optimize(self.opt)
+            # requires_grad(self.rec_model_module, True)
+
+            log_rec3d_loss_dict(
+                dict(
+                    p_loss=p_loss,
+                    mixing_logit=self.ddp_ddpm_model(
+                        x=None, timesteps=None,
+                        get_attr='mixing_logit').detach(),
+                ))
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            # ! todo, wrap in a single function
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+
+                with th.no_grad():
+
+                    if not args.train_vae:
+                        vae_out.pop('posterior')  # for calculating kl loss
+                        vae_out_for_pred = {
+                            k:
+                            v[0:1].to(dist_util.dev()) if isinstance(
+                                v, th.Tensor) else v
+                            for k, v in vae_out.items()
+                        }
+
+                        pred = self.ddp_rec_model(
+                            latent=vae_out_for_pred,
+                            c=micro['c'][0:1],
+                            behaviour=self.render_latent_behaviour)
+                    assert isinstance(pred, dict)
+                    assert pred is not None
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+
+                    if 'image_depth' in pred:
+                        pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_img = self.pool_64(gt_img)
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img,
+                            # micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], pred_depth[0:1].repeat_interleave(3,
+                                                                         dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        # f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_{behaviour}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_{behaviour}.jpg'
+                    )
+
+                    th.cuda.empty_cache()
+
+    def eps_step(self, batch, behaviour='rec', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+
+        requires_grad(self.ddpm_model_module, True)
+        requires_grad(self.rec_model_module, False)
+
+        # TODO?
+        # if args.train_vae:
+        #     for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters(  # type: ignore
+        #     ):  # type: ignore
+        #         param.requires_grad_(
+        #             False
+        #         )  # ! disable triplane_decoder grad in each iteration indepenently;
+
+        self.mp_trainer.zero_grad()
+
+        # assert args.train_vae
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+                #   and args.train_vae):
+
+                # ! part 1: train vae with CE; ddpm fixed
+                # ! TODO, add KL_all_list? vae.decompose
+
+                with th.set_grad_enabled(args.train_vae):
+                    vae_out = self.ddp_rec_model(
+                        img=micro['img_to_encoder'],
+                        c=micro['c'],
+                        behaviour='encoder_vae',
+                    )  # pred: (B, 3, 64, 64)
+                eps = vae_out[self.latent_name]
+
+                # sample noise
+                noise = th.randn(
+                    size=eps.size(), device=eps.device
+                )  # note that this noise value is currently shared!
+
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+                t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                    self.sde_diffusion.iw_quantities(args.iw_sample_p)
+                eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p,
+                                                      m_t_p)
+                logsnr_p = self.sde_diffusion.log_snr(m_t_p,
+                                                      var_t_p)  # for p only
+
+                pred_eps_p, pred_x0_p, logsnr_p = self.ddpm_step(
+                    eps_t_p, t_p, m_t_p, var_t_p)
+
+                # ! batchify for mixing_component
+                # mixing normal trick
+                mixing_component = self.sde_diffusion.mixing_component(
+                    eps_t_p, var_t_p, t_p,
+                    enabled=True)  # TODO, which should I use?
+                pred_eps_p = get_mixed_prediction(
+                    True, pred_eps_p,
+                    self.ddp_ddpm_model(x=None,
+                                        timesteps=None,
+                                        get_attr='mixing_logit'),
+                    mixing_component)
+
+                # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+                if last_batch or not self.use_ddp:
+                    l2_term_p = th.square(pred_eps_p - noise)
+                else:
+                    with self.ddp_ddpm_model.no_sync():  # type: ignore
+                        l2_term_p = th.square(pred_eps_p - noise)  # ? weights
+
+                p_eps_objective = th.mean(
+                    obj_weight_t_p *
+                    l2_term_p) * self.loss_class.opt.p_eps_lambda
+
+                log_rec3d_loss_dict(
+                    dict(mixing_logit=self.ddp_ddpm_model(
+                        x=None, timesteps=None,
+                        get_attr='mixing_logit').detach(), ))
+
+                # =====================================================================
+                # ! single stage diffusion for rec side 2: generative feature
+                # if args.p_rendering_loss:
+                #     target = micro
+                #     pred = self.ddp_rec_model(
+                #         # latent=vae_out,
+                #         latent={
+                #             **vae_out, self.latent_name: pred_x0_p,
+                #             'latent_name': self.latent_name
+                #         },
+                #         c=micro['c'],
+                #         behaviour=self.render_latent_behaviour)
+
+                #     # vae reconstruction loss
+                #     if last_batch or not self.use_ddp:
+                #         p_vae_recon_loss, _ = self.loss_class(pred,
+                #                                               target,
+                #                                               test_mode=False)
+                #     else:
+                #         with self.ddp_model.no_sync():  # type: ignore
+                #             p_vae_recon_loss, _ = self.loss_class(
+                #                 pred, target, test_mode=False)
+                #     log_rec3d_loss_dict(
+                #         dict(p_vae_recon_loss=p_vae_recon_loss, ))
+                #     p_loss = p_eps_objective + p_vae_recon_loss
+                # else:
+                p_loss = p_eps_objective
+
+                log_rec3d_loss_dict(
+                    dict(p_loss=p_loss, p_eps_objective=p_eps_objective))
+
+                # ! to arrange: update vae params
+
+            self.mp_trainer.backward(p_loss)
+
+            # update ddpm accordingly
+            _ = self.mp_trainer.optimize(
+                self.opt)  # TODO, update two groups of parameters
+
+            # TODO, merge visualization with original AE
+            # ! todo, merge required
+            # =================================== denoised AE log part ===================================
+            if dist_util.get_rank(
+            ) == 0 and self.step % 500 == 0 and behaviour != 'diff':
+
+                with th.no_grad():
+
+                    vae_out.pop('posterior')  # for calculating kl loss
+                    vae_out_for_pred = {
+                        k:
+                        v[0:1].to(dist_util.dev())
+                        if isinstance(v, th.Tensor) else v
+                        for k, v in vae_out.items()
+                    }
+
+                    pred = self.ddp_rec_model(
+                        latent=vae_out_for_pred,
+                        c=micro['c'][0:1],
+                        behaviour=self.render_latent_behaviour)
+                    assert isinstance(pred, dict)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+
+                    if 'image_depth' in pred:
+                        pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_img = self.pool_64(gt_img)
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'], micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+                    noised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=eps_t_p[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically
+                        behaviour=self.render_latent_behaviour)
+
+                    pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+                        eps_t_p, pred_eps_p,
+                        logsnr_p)  # for VAE loss, denosied latent
+
+                    # pred_xstart_3D
+                    denoised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=pred_x0[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                        behaviour=self.render_latent_behaviour)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], noised_ae_pred['image_raw'][0:1],
+                        denoised_ae_pred['image_raw'][0:1],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg'
+                    )
+                    del vis, pred_vis, pred_x0, pred_eps_p, micro, vae_out
+
+                    th.cuda.empty_cache()
+
+    def p_rendering_step(self, batch, behaviour='rec', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+        args = self.sde_diffusion.args
+
+        requires_grad(self.ddpm_model, True)
+        requires_grad(self.rec_model, args.train_vae)
+
+        # TODO?
+        # if args.train_vae:
+        #     for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters(  # type: ignore
+        #     ):  # type: ignore
+        #         param.requires_grad_(
+        #             False
+        #         )  # ! disable triplane_decoder grad in each iteration indepenently;
+
+        self.mp_trainer.zero_grad()
+
+        assert args.train_vae
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+                #   and args.train_vae):
+
+                # ! part 1: train vae with CE; ddpm fixed
+                # ! TODO, add KL_all_list? vae.decompose
+
+                with th.set_grad_enabled(args.train_vae):
+                    vae_out = self.ddp_rec_model(
+                        img=micro['img_to_encoder'],
+                        c=micro['c'],
+                        behaviour='encoder_vae',
+                    )  # pred: (B, 3, 64, 64)
+                eps = vae_out[self.latent_name]
+
+                # sample noise
+                noise = th.randn(
+                    size=eps.size(), device=eps.device
+                )  # note that this noise value is currently shared!
+
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+                t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                    self.sde_diffusion.iw_quantities(args.iw_sample_p)
+                eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p,
+                                                      m_t_p)
+                logsnr_p = self.sde_diffusion.log_snr(m_t_p,
+                                                      var_t_p)  # for p only
+
+                # pred_eps_p, pred_x0_p, logsnr_p = self.ddpm_step(
+                pred_eps_p, pred_x0_p = self.ddpm_step(eps_t_p, t_p, logsnr_p,
+                                                       var_t_p)
+                # eps_t_p, t_p, m_t_p, var_t_p)
+
+                # ! batchify for mixing_component
+                # mixing normal trick
+                # mixing_component = self.sde_diffusion.mixing_component(
+                #     eps_t_p, var_t_p, t_p,
+                #     enabled=True)  # TODO, which should I use?
+                # pred_eps_p = get_mixed_prediction(
+                #     True, pred_eps_p,
+                #     self.ddp_ddpm_model(x=None,
+                #                         timesteps=None,
+                #                         get_attr='mixing_logit'),
+                #     mixing_component)
+
+                # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation"
+                if last_batch or not self.use_ddp:
+                    l2_term_p = th.square(pred_eps_p - noise)
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        l2_term_p = th.square(pred_eps_p - noise)  # ? weights
+
+                p_eps_objective = th.mean(obj_weight_t_p * l2_term_p)
+                # st()
+
+                log_rec3d_loss_dict(
+                    dict(mixing_logit=self.ddp_ddpm_model(
+                        x=None, timesteps=None,
+                        get_attr='mixing_logit').detach(), ))
+
+                # =====================================================================
+                # ! single stage diffusion for rec side 2: generative feature
+                if args.p_rendering_loss:
+                    target = micro
+                    pred = self.ddp_rec_model( # re-render 
+                        latent={
+                            **vae_out, self.latent_name: pred_x0_p,
+                            'latent_name': self.latent_name
+                        },
+                        c=micro['c'],
+                        behaviour=self.render_latent_behaviour)
+
+                    # vae reconstruction loss
+                    if last_batch or not self.use_ddp:
+                        pred[self.latent_name] = vae_out[self.latent_name]
+                        pred[
+                            'latent_name'] = self.latent_name  # just for stats
+                        p_vae_recon_loss, rec_loss_dict = self.loss_class(
+                            pred, target, test_mode=False)
+                    else:
+                        with self.ddp_model.no_sync():  # type: ignore
+                            p_vae_recon_loss, rec_loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+                    log_rec3d_loss_dict(
+                        dict(p_vae_recon_loss=p_vae_recon_loss, ))
+
+                    for key in rec_loss_dict.keys():
+                        if 'latent' in key:
+                            log_rec3d_loss_dict({key: rec_loss_dict[key]})
+
+                    p_loss = p_eps_objective + p_vae_recon_loss
+                else:
+                    p_loss = p_eps_objective
+
+                log_rec3d_loss_dict(
+                    dict(p_loss=p_loss, p_eps_objective=p_eps_objective))
+
+                # ! to arrange: update vae params
+
+            self.mp_trainer.backward(p_loss)
+
+            # update ddpm accordingly
+            _ = self.mp_trainer.optimize(
+                self.opt)  # TODO, update two groups of parameters
+
+            # TODO, merge visualization with original AE
+            # ! todo, merge required
+            # =================================== denoised AE log part ===================================
+            if dist_util.get_rank(
+            ) == 0 and self.step % 500 == 0 and behaviour != 'diff':
+
+                with th.no_grad():
+
+                    vae_out.pop('posterior')  # for calculating kl loss
+                    vae_out_for_pred = {
+                        k:
+                        v[0:1].to(dist_util.dev())
+                        if isinstance(v, th.Tensor) else v
+                        for k, v in vae_out.items()
+                    }
+
+                    pred = self.ddp_rec_model(
+                        latent=vae_out_for_pred,
+                        c=micro['c'][0:1],
+                        behaviour=self.render_latent_behaviour)
+                    assert isinstance(pred, dict)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+
+                    if 'image_depth' in pred:
+                        pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_img = self.pool_64(gt_img)
+                        gt_depth = self.pool_64(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'], micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L
+
+                    noised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=eps_t_p[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically
+                        behaviour=self.render_latent_behaviour)
+
+                    pred_x0 = self.sde_diffusion._predict_x0_from_eps(
+                        eps_t_p, pred_eps_p,
+                        logsnr_p)  # for VAE loss, denosied latent
+
+                    # pred_xstart_3D
+                    denoised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=pred_x0[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                        behaviour=self.render_latent_behaviour)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], noised_ae_pred['image_raw'][0:1],
+                        denoised_ae_pred['image_raw'][0:1],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg'
+                    )
+                    del vis, pred_vis, pred_x0, pred_eps_p, micro, vae_out
+
+                    th.cuda.empty_cache()
+
+
+class TrainLoop3DDiffusionLSGMJointnoD_ponly(TrainLoop3DDiffusionLSGMJointnoD):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 sde_diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 triplane_scaling_divider=1,
+                 use_amp=False,
+                 diffusion_input_size=224,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         sde_diffusion=sde_diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         diffusion_input_size=diffusion_input_size,
+                         **kwargs)
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            self._post_run_loop()
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch = next(self.data)
+            # self.run_step(batch, step='ce_ddpm_step')
+
+            batch = next(self.data)
+            self.run_step(batch, step='p_rendering_step')
+            # self.run_step(batch, step='eps_step')
diff --git a/nsr/lsgm/train_util_diffusion_vpsde.py b/nsr/lsgm/train_util_diffusion_vpsde.py
new file mode 100644
index 0000000000000000000000000000000000000000..bea22ff78941b8ce09eae3eb0a71b642ebd036de
--- /dev/null
+++ b/nsr/lsgm/train_util_diffusion_vpsde.py
@@ -0,0 +1,583 @@
+"""
+Modified from:
+https://github.com/NVlabs/LSGM/blob/main/training_obj_joint.py
+"""
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from typing import Any
+
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+from guided_diffusion.gaussian_diffusion import ModelMeanType
+
+import dnnlib
+from dnnlib.util import calculate_adaptive_weight
+
+from ..train_util_diffusion import TrainLoop3DDiffusion
+from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
+
+
+class TrainLoop3DDiffusion_vpsde(TrainLoop3DDiffusion,TrainLoop3DcvD_nvsD_canoD):
+    def __init__(self, *, rec_model, denoise_model, diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, schedule_sampler=None, weight_decay=0, lr_anneal_steps=0, iterations=10001, ignore_resume_opt=False, freeze_ae=False, denoised_ae=True, triplane_scaling_divider=10, use_amp=False, diffusion_input_size=224, **kwargs):
+        super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, schedule_sampler=schedule_sampler, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, ignore_resume_opt=ignore_resume_opt, freeze_ae=freeze_ae, denoised_ae=denoised_ae, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, **kwargs)
+
+    def run_step(self, batch, step='g_step'):
+
+        if step == 'diffusion_step_rec':
+            self.forward_diffusion(batch, behaviour='diffusion_step_rec')
+            _ = self.mp_trainer_rec.optimize(self.opt_rec) # TODO, update two groups of parameters
+            took_step_ddpm = self.mp_trainer.optimize(self.opt) # TODO, update two groups of parameters
+
+            if took_step_ddpm:
+                self._update_ema()  # g_ema # TODO, ema only needs to track ddpm, remove ema tracking in rec
+
+        elif step == 'd_step_rec':
+            self.forward_D(batch, behaviour='rec')
+            # _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+            _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD)
+
+        elif step == 'diffusion_step_nvs':
+            self.forward_diffusion(batch, behaviour='diffusion_step_nvs')
+            _ = self.mp_trainer_rec.optimize(self.opt_rec) # TODO, update two groups of parameters
+            took_step_ddpm = self.mp_trainer.optimize(self.opt) # TODO, update two groups of parameters
+
+            if took_step_ddpm:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step_nvs':
+            self.forward_D(batch, behaviour='nvs')
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            # batch = next(self.data)
+            # self.run_step(batch, 'g_step_rec')
+
+            batch = next(self.data)
+            self.run_step(batch, step='diffusion_step_rec')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_rec')
+
+            # batch = next(self.data)
+            # self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, step='diffusion_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step_nvs')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                th.cuda.empty_cache()
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save(self.mp_trainer, self.mp_trainer.model_name)
+                self.save(self.mp_trainer_rec, self.mp_trainer_rec.model_name)
+                self.save(self.mp_trainer_cvD, 'cvD')
+                self.save(self.mp_trainer_canonical_cvD, 'cano_cvD')
+
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+
+                    self.save(self.mp_trainer, self.mp_trainer.model_name)
+                    self.save(self.mp_trainer_rec, self.mp_trainer_rec.model_name)
+                    self.save(self.mp_trainer_cvD, 'cvD')
+                    self.save(self.mp_trainer_canonical_cvD, 'cano_cvD')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_canonical_cvD, 'cvD')
+
+    def forward_diffusion(self, batch, behaviour='rec', *args, **kwargs):
+        """
+        add sds grad to all ae predicted x_0 
+        """
+
+        self.ddp_cano_cvD.requires_grad_(False)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        self.ddp_model.requires_grad_(True)
+        self.ddp_rec_model.requires_grad_(True)
+
+        # if behaviour != 'diff' and 'rec' in behaviour:
+        # if behaviour != 'diff' and 'rec' in behaviour: # pure diffusion step
+        #     self.ddp_rec_model.requires_grad_(True)
+        for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters( # type: ignore
+        ):  # type: ignore
+            param.requires_grad_(False) # ! disable triplane_decoder grad in each iteration indepenently; 
+        # else:
+
+        self.mp_trainer_rec.zero_grad()
+        self.mp_trainer.zero_grad()
+
+        # ! no 'sds' step now, both add sds grad back to ViT
+
+        # assert behaviour != 'sds'
+        # if behaviour == 'sds':
+        # else:
+        #     self.ddp_ddpm_model.requires_grad_(True)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            vae_nelbo_loss = th.tensor(0.0).to(dist_util.dev())
+            vision_aided_loss = th.tensor(0.0).to(dist_util.dev())
+            denoise_loss = th.tensor(0.0).to(dist_util.dev())
+            d_weight = th.tensor(0.0).to(dist_util.dev())
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp
+                                      and not self.freeze_ae):
+
+                # apply vae
+                vae_out = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='enc_dec_wo_triplane')  # pred: (B, 3, 64, 64)
+                
+
+                if behaviour == 'diffusion_step_rec':
+                    target = micro
+                    pred = self.ddp_rec_model(latent=vae_out,
+                                              c=micro['c'],
+                                              behaviour='triplane_dec')
+
+                    # vae reconstruction loss
+                    if last_batch or not self.use_ddp:
+                        vae_nelbo_loss, loss_dict = self.loss_class(pred,
+                                                             target,
+                                                             test_mode=False)
+                    else:
+                        with self.ddp_model.no_sync():  # type: ignore
+                            vae_nelbo_loss, loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+
+                    last_layer = self.ddp_rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                        -1].weight  # type: ignore
+
+                    if 'image_sr' in pred:
+                        vision_aided_loss = self.ddp_cano_cvD(
+                            0.5 * pred['image_sr'] +
+                            0.5 * th.nn.functional.interpolate(
+                                pred['image_raw'],
+                                size=pred['image_sr'].shape[2:],
+                                mode='bilinear'),
+                            for_G=True).mean()  # [B, 1] shape
+                    else:
+                        vision_aided_loss = self.ddp_cano_cvD(
+                            pred['image_raw'], for_G=True
+                        ).mean(
+                        )   # [B, 1] shape
+
+                    d_weight = calculate_adaptive_weight(
+                        vae_nelbo_loss,
+                        vision_aided_loss,
+                        last_layer,
+                        # disc_weight_max=1) * 1
+                        disc_weight_max=1) * self.loss_class.opt.rec_cvD_lambda
+                    # d_weight = self.loss_class.opt.rec_cvD_lambda # since decoder is fixed here. set to 0.001
+                    
+                    vision_aided_loss *= d_weight
+
+                    # d_weight = self.loss_class.opt.rec_cvD_lambda
+                    loss_dict.update({
+                        'vision_aided_loss/G_rec':
+                        vision_aided_loss,
+                        'd_weight_G_rec':
+                        d_weight,
+                    })
+
+                    log_rec3d_loss_dict(loss_dict)
+
+                elif behaviour == 'diffusion_step_nvs':
+
+                    novel_view_c = th.cat([micro['c'][1:], micro['c'][:1]])
+
+                    pred = self.ddp_rec_model(latent=vae_out,
+                                              c=novel_view_c,
+                                              behaviour='triplane_dec')
+
+                    if 'image_sr' in pred:
+                        vision_aided_loss = self.ddp_nvs_cvD(
+                            # pred_for_rec['image_sr'],
+                            0.5 * pred['image_sr'] +
+                            0.5 * th.nn.functional.interpolate(
+                                pred['image_raw'],
+                                size=pred['image_sr'].shape[2:],
+                                mode='bilinear'),
+                            for_G=True).mean()  # [B, 1] shape
+                    else:
+                        vision_aided_loss = self.ddp_nvs_cvD(
+                            pred['image_raw'], for_G=True
+                        ).mean(
+                        )  # [B, 1] shape
+
+                    d_weight = self.loss_class.opt.nvs_cvD_lambda
+                    vision_aided_loss *= d_weight
+
+                    log_rec3d_loss_dict({
+                        'vision_aided_loss/G_nvs':
+                        vision_aided_loss,
+                    })
+
+                    # ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                # elif behaviour == 'diff':
+                #     self.ddp_rec_model.requires_grad_(False)
+                #     # assert self.ddp_rec_model.module.requires_grad == False, 'freeze ddpm_rec for pure diff step'
+                else:
+                    raise NotImplementedError(behaviour)
+                #     assert behaviour == 'sds'
+
+                # pred = None
+
+                # if behaviour != 'sds': # also train diffusion
+                # assert pred is not None
+
+                # TODO, train diff and sds together, available?
+                eps = vae_out[self.latent_name] 
+
+                # if behaviour != 'sds':
+                # micro_to_denoise.detach_()
+                eps.requires_grad_(True) # single stage diffusion
+
+                t, weights = self.schedule_sampler.sample(
+                    eps.shape[0], dist_util.dev())
+                noise = th.randn(size=vae_out.size(), device='cuda')  # note that this noise value is currently shared!
+
+                model_kwargs = {}
+
+                # ? 
+                # or directly use SSD NeRF version?
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+
+                # ! handle the sampling 
+
+                # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae)
+                t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \
+                    diffusion.iw_quantities(args.batch_size, args.time_eps, args.iw_sample_p, args.iw_subvp_like_vp_sde)
+                eps_t_p = diffusion.sample_q(vae_out, noise, var_t_p, m_t_p)
+
+                # in case we want to train q (vae) with another batch using a different sampling scheme for times t
+                if args.iw_sample_q in ['ll_uniform', 'll_iw']:
+                    t_q, var_t_q, m_t_q, obj_weight_t_q, _, g2_t_q = \
+                        diffusion.iw_quantities(args.batch_size, args.time_eps, args.iw_sample_q, args.iw_subvp_like_vp_sde)
+                    eps_t_q = diffusion.sample_q(vae_out, noise, var_t_q, m_t_q)
+
+                    eps_t_p = eps_t_p.detach().requires_grad_(True)
+                    eps_t = th.cat([eps_t_p, eps_t_q], dim=0)
+                    var_t = th.cat([var_t_p, var_t_q], dim=0)
+                    t = th.cat([t_p, t_q], dim=0)
+                    noise = th.cat([noise, noise], dim=0)
+                else:
+                    eps_t, m_t, var_t, t, g2_t = eps_t_p, m_t_p, var_t_p, t_p, g2_t_p
+                
+                # run the diffusion
+
+                # mixing normal trick
+                # TODO, create a new partial training_losses function 
+                mixing_component = diffusion.mixing_component(eps_t, var_t, t, enabled=dae.mixed_prediction) # TODO, which should I use?
+                params = utils.get_mixed_prediction(dae.mixed_prediction, pred_params, dae.mixing_logit, mixing_component)
+
+                # nelbo loss with kl balancing
+
+
+
+
+                # ! remainign parts of cross entropy in likelihook training
+
+                cross_entropy_per_var += diffusion.cross_entropy_const(args.time_eps)
+                cross_entropy = th.sum(cross_entropy_per_var, dim=[1, 2, 3])
+                cross_entropy += remaining_neg_log_p_total  # for remaining scales if there is any
+                all_neg_log_p = vae.decompose_eps(cross_entropy_per_var)
+                all_neg_log_p.extend(remaining_neg_log_p_per_ver)  # add the remaining neg_log_p
+                kl_all_list, kl_vals_per_group, kl_diag_list = utils.kl_per_group_vada(all_log_q, all_neg_log_p)
+
+
+                kl_coeff = 1.0
+
+                # ! calculate p/q loss; 
+                # ? no spectral regularizer here
+                # ? try adding grid_clip and sn later on.
+                q_loss = th.mean(nelbo_loss) 
+                p_loss = th.mean(p_objective) 
+
+                # backpropagate q_loss for vae and update vae params, if trained
+                if args.train_vae:
+                    grad_scalar.scale(q_loss).backward(retain_graph=utils.different_p_q_objectives(args.iw_sample_p, args.iw_sample_q))
+                    utils.average_gradients(vae.parameters(), args.distributed)
+                    if args.grad_clip_max_norm > 0.:  # apply gradient clipping
+                        grad_scalar.unscale_(vae_optimizer)
+                        th.nn.utils.clip_grad_norm_(vae.parameters(), max_norm=args.grad_clip_max_norm)
+                    grad_scalar.step(vae_optimizer)
+
+                # if we use different p and q objectives or are not training the vae, discard gradients and backpropagate p_loss
+                if utils.different_p_q_objectives(args.iw_sample_p, args.iw_sample_q) or not args.train_vae:
+                    if args.train_vae:
+                        # discard current gradients computed by weighted loss for VAE
+                        dae_optimizer.zero_grad()
+
+                    # compute gradients with unweighted loss
+                    grad_scalar.scale(p_loss).backward()
+
+                # update dae parameters
+                utils.average_gradients(dae.parameters(), args.distributed)
+                if args.grad_clip_max_norm > 0.:         # apply gradient clipping
+                    grad_scalar.unscale_(dae_optimizer)
+                    th.nn.utils.clip_grad_norm_(dae.parameters(), max_norm=args.grad_clip_max_norm)
+                grad_scalar.step(dae_optimizer)
+
+
+                # unpack separate objectives, in case we want to train q (vae) using a different sampling scheme for times t
+                if args.iw_sample_q in ['ll_uniform', 'll_iw']:
+                    l2_term_p, l2_term_q = th.chunk(l2_term, chunks=2, dim=0)
+                    p_objective = th.sum(obj_weight_t_p * l2_term_p, dim=[1, 2, 3])
+                    # cross_entropy_per_var = obj_weight_t_q * l2_term_q
+                else:
+                    p_objective = th.sum(obj_weight_t_p * l2_term, dim=[1, 2, 3])
+                    # cross_entropy_per_var = obj_weight_t_q * l2_term
+
+                # print(micro_to_denoise.min(), micro_to_denoise.max())
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    eps,  # x_start
+                    t,
+                    model_kwargs=model_kwargs,
+                    return_detail=True)
+
+                # ! DDPM step
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                    # denoised_out = denoised_fn()
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        losses = compute_losses()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                denoise_loss = (losses["loss"] * weights).mean()
+
+                x_t = losses.pop('x_t')
+                model_output = losses.pop('model_output')
+                diffusion_target = losses.pop('diffusion_target')
+                alpha_bar = losses.pop('alpha_bar')
+
+                log_loss_dict(self.diffusion, t,
+                              {k: v * weights
+                               for k, v in losses.items()})
+
+                # if behaviour == 'sds':
+                # ! calculate sds grad, and add to the grad of
+
+                # if 'rec' in behaviour and self.loss_class.opt.sds_lamdba > 0:  # only enable sds along with rec step
+                #     w = (
+                #         1 - alpha_bar**2
+                #     ) / self.triplane_scaling_divider * self.loss_class.opt.sds_lamdba  # https://github.com/ashawkey/stable-dreamfusion/issues/106
+                #     sds_grad = denoise_loss.clone().detach(
+                #     ) * w  # * https://pytorch.org/docs/stable/generated/th.Tensor.detach.html. detach() returned Tensor share the same storage with previous one. add clone() here.
+
+                #     # ae_loss = AddGradient.apply(latent[self.latent_name], sds_grad) # add sds_grad during backward
+
+                #     def sds_hook(grad_to_add):
+
+                #         def modify_grad(grad):
+                #             return grad + grad_to_add  # add the sds grad to the original grad for BP
+
+                #         return modify_grad
+
+                #     eps[self.latent_name].register_hook(
+                #         sds_hook(sds_grad))  # merge sds grad with rec/nvs ae step
+
+                loss = vae_nelbo_loss + denoise_loss + vision_aided_loss  # caluclate loss within AMP
+
+            # ! cvD loss
+
+            # exit AMP before backward
+            self.mp_trainer_rec.backward(loss)
+            self.mp_trainer.backward(loss)
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0 and behaviour != 'diff':
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    # st()
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    # if 'image_sr' in pred:  # TODO
+                    #     pred_img = th.cat(
+                    #         [self.pool_512(pred_img), pred['image_sr']],
+                    #         dim=-1)
+                    #     gt_img = th.cat(
+                    #         [self.pool_512(micro['img']), micro['img_sr']],
+                    #         dim=-1)
+                    #     pred_depth = self.pool_512(pred_depth)
+                    #     gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'], micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    noised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=x_t[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically
+                        behaviour=self.render_latent_behaviour)
+
+                    # if denoised_out is None:
+                    # if not self.denoised_ae:
+                    # denoised_out = denoised_fn()
+
+                    if self.diffusion.model_mean_type == ModelMeanType.START_X:
+                        pred_xstart = model_output
+                    else:  # * used here
+                        pred_xstart = self.diffusion._predict_xstart_from_eps(
+                            x_t=x_t, t=t, eps=model_output)
+
+                    denoised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent=pred_xstart[0:1] * self.
+                        triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                        behaviour=self.render_latent_behaviour)
+
+                    # denoised_out = denoised_ae_pred
+
+                    # if not self.denoised_ae:
+                    #     denoised_ae_pred = self.ddp_rec_model(
+                    #         img=None,
+                    #         c=micro['c'][0:1],
+                    #         latent=denoised_out['pred_xstart'][0:1] * self.
+                    #         triplane_scaling_divider,  # TODO, how to define the scale automatically
+                    #         behaviour=self.render_latent_behaviour)
+                    # else:
+                    #     assert denoised_ae_pred is not None
+                    #     denoised_ae_pred['image_raw'] = denoised_ae_pred[
+                    #         'image_raw'][0:1]
+
+                    # print(pred_img.shape)
+                    # print('denoised_ae:', self.denoised_ae)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], noised_ae_pred['image_raw'][0:1],
+                        denoised_ae_pred['image_raw'][0:1],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+                    # s
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis = th.cat([
+                    #     self.pool_128(micro['img']), x_t[:, :3, ...],
+                    #     denoised_out['pred_xstart'][:, :3, ...]
+                    # ],
+                    #              dim=-1)[0].permute(
+                    #                  1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg'
+                    )
+
+                    th.cuda.empty_cache()
diff --git a/nsr/networks_stylegan2.py b/nsr/networks_stylegan2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f12530965c129cec09034430b2e95c6defb18685
--- /dev/null
+++ b/nsr/networks_stylegan2.py
@@ -0,0 +1,1093 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Network architectures from the paper
+"Analyzing and Improving the Image Quality of StyleGAN".
+Matches the original implementation of configs E-F by Karras et al. at
+https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
+
+import numpy as np
+import torch
+from torch_utils import misc
+from torch_utils import persistence
+from torch_utils.ops import conv2d_resample
+from torch_utils.ops import upfirdn2d
+from torch_utils.ops import bias_act
+from torch_utils.ops import fma
+from pdb import set_trace as st
+
+from pdb import set_trace as st
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+def normalize_2nd_moment(x, dim=1, eps=1e-8):
+    return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
+
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+# @torch.autocast(device_type='cuda')
+def modulated_conv2d(
+    x,  # Input tensor of shape [batch_size, in_channels, in_height, in_width].
+    weight,  # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
+    styles,  # Modulation coefficients of shape [batch_size, in_channels].
+    noise=None,  # Optional noise tensor to add to the output activations.
+    up=1,  # Integer upsampling factor.
+    down=1,  # Integer downsampling factor.
+    padding=0,  # Padding with respect to the upsampled image.
+    resample_filter=None,  # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
+    demodulate=True,  # Apply weight demodulation?
+    flip_weight=True,  # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
+    fused_modconv=True,  # Perform modulation, convolution, and demodulation as a single fused operation?
+):
+    batch_size = x.shape[0]
+    out_channels, in_channels, kh, kw = weight.shape
+    misc.assert_shape(weight, [out_channels, in_channels, kh, kw])  # [OIkk]
+    misc.assert_shape(x, [batch_size, in_channels, None, None])  # [NIHW]
+    misc.assert_shape(styles, [batch_size, in_channels])  # [NI]
+
+    # Pre-normalize inputs to avoid FP16 overflow.
+    if x.dtype == torch.float16 and demodulate:
+        weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(
+            float('inf'), dim=[1, 2, 3], keepdim=True))  # max_Ikk
+        styles = styles / styles.norm(float('inf'), dim=1,
+                                      keepdim=True)  # max_I
+
+    # Calculate per-sample weights and demodulation coefficients.
+    w = None
+    dcoefs = None
+    if demodulate or fused_modconv:
+        w = weight.unsqueeze(0)  # [NOIkk]
+        w = w * styles.reshape(batch_size, 1, -1, 1, 1)  # [NOIkk]
+    if demodulate:
+        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
+    if demodulate and fused_modconv:
+        w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1)  # [NOIkk]
+
+    # Execute by scaling the activations before and after the convolution.
+    if not fused_modconv:
+        x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
+        x = conv2d_resample.conv2d_resample(x=x,
+                                            w=weight.to(x.dtype),
+                                            f=resample_filter,
+                                            up=up,
+                                            down=down,
+                                            padding=padding,
+                                            flip_weight=flip_weight)
+        if demodulate and noise is not None:
+            x = fma.fma(x,
+                        dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1),
+                        noise.to(x.dtype))
+        elif demodulate:
+            x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
+        elif noise is not None:
+            x = x.add_(noise.to(x.dtype))
+        return x
+
+    # Execute as one fused op using grouped convolution.
+    with misc.suppress_tracer_warnings(
+    ):  # this value will be treated as a constant
+        batch_size = int(batch_size)
+    misc.assert_shape(x, [batch_size, in_channels, None, None])
+    x = x.reshape(1, -1, *x.shape[2:])
+    w = w.reshape(-1, in_channels, kh, kw)
+    x = conv2d_resample.conv2d_resample(x=x,
+                                        w=w.to(x.dtype),
+                                        f=resample_filter,
+                                        up=up,
+                                        down=down,
+                                        padding=padding,
+                                        groups=batch_size,
+                                        flip_weight=flip_weight)
+    x = x.reshape(batch_size, -1, *x.shape[2:])
+    if noise is not None:
+        x = x.add_(noise)
+    return x
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class FullyConnectedLayer(torch.nn.Module):
+    def __init__(
+            self,
+            in_features,  # Number of input features.
+            out_features,  # Number of output features.
+            bias=True,  # Apply additive bias before the activation function?
+            activation='linear',  # Activation function: 'relu', 'lrelu', etc.
+            lr_multiplier=1,  # Learning rate multiplier.
+            bias_init=0,  # Initial value for the additive bias.
+    ):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.activation = activation
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_features, in_features]) / lr_multiplier)
+        self.bias = torch.nn.Parameter(
+            torch.full([out_features],
+                       np.float32(bias_init))) if bias else None
+        self.weight_gain = lr_multiplier / np.sqrt(in_features)
+        self.bias_gain = lr_multiplier
+
+    def forward(self, x):
+        w = self.weight.to(x.dtype) * self.weight_gain
+        b = self.bias
+        if b is not None:
+            b = b.to(x.dtype)
+            if self.bias_gain != 1:
+                b = b * self.bias_gain
+
+        if self.activation == 'linear' and b is not None:
+            x = torch.addmm(b.unsqueeze(0), x, w.t())
+        else:
+            x = x.matmul(w.t())
+            x = bias_act.bias_act(x, b, act=self.activation)
+        return x
+
+    def extra_repr(self):
+        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class Conv2dLayer(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels,  # Number of input channels.
+            out_channels,  # Number of output channels.
+            kernel_size,  # Width and height of the convolution kernel.
+            bias=True,  # Apply additive bias before the activation function?
+            activation='linear',  # Activation function: 'relu', 'lrelu', etc.
+            up=1,  # Integer upsampling factor.
+            down=1,  # Integer downsampling factor.
+            resample_filter=[
+                1, 3, 3, 1
+            ],  # Low-pass filter to apply when resampling activations.
+            conv_clamp=None,  # Clamp the output to +-X, None = disable clamping.
+            channels_last=False,  # Expect the input to have memory_format=channels_last?
+            trainable=True,  # Update the weights of this layer during training?
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.activation = activation
+        self.up = up
+        self.down = down
+        self.conv_clamp = conv_clamp
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter(resample_filter))
+        self.padding = kernel_size // 2
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
+        self.act_gain = bias_act.activation_funcs[activation].def_gain
+
+        memory_format = torch.channels_last if channels_last else torch.contiguous_format
+        weight = torch.randn(
+            [out_channels, in_channels, kernel_size,
+             kernel_size]).to(memory_format=memory_format)
+        bias = torch.zeros([out_channels]) if bias else None
+        if trainable:
+            self.weight = torch.nn.Parameter(weight)
+            self.bias = torch.nn.Parameter(bias) if bias is not None else None
+        else:
+            self.register_buffer('weight', weight)
+            if bias is not None:
+                self.register_buffer('bias', bias)
+            else:
+                self.bias = None
+
+    # @torch.autocast(device_type='cuda')
+    def forward(self, x, gain=1):
+        w = self.weight * self.weight_gain # w dtype is fp32
+        b = self.bias.to(x.dtype) if self.bias is not None else None
+
+        flip_weight = (self.up == 1)  # slightly faster
+        x = conv2d_resample.conv2d_resample(x=x,
+                                            w=w.to(x.dtype),
+                                            f=self.resample_filter,
+                                            up=self.up,
+                                            down=self.down,
+                                            padding=self.padding,
+                                            flip_weight=flip_weight)
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        x = bias_act.bias_act(x,
+                              b,
+                              act=self.activation,
+                              gain=act_gain,
+                              clamp=act_clamp)
+        return x
+
+    def extra_repr(self):
+        return ' '.join([
+            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
+            f'up={self.up}, down={self.down}'
+        ])
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class MappingNetwork(torch.nn.Module):
+    def __init__(
+        self,
+        z_dim,  # Input latent (Z) dimensionality, 0 = no latent.
+        c_dim,  # Conditioning label (C) dimensionality, 0 = no label.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        num_ws,  # Number of intermediate latents to output, None = do not broadcast.
+        num_layers=8,  # Number of mapping layers.
+        embed_features=None,  # Label embedding dimensionality, None = same as w_dim.
+        layer_features=None,  # Number of intermediate features in the mapping layers, None = same as w_dim.
+        activation='lrelu',  # Activation function: 'relu', 'lrelu', etc.
+        lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
+        w_avg_beta=0.998,  # Decay for tracking the moving average of W during training, None = do not track.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.num_ws = num_ws
+        self.num_layers = num_layers
+        self.w_avg_beta = w_avg_beta
+
+        if embed_features is None:
+            embed_features = w_dim
+        if c_dim == 0:
+            embed_features = 0
+        if layer_features is None:
+            layer_features = w_dim
+        features_list = [z_dim + embed_features
+                         ] + [layer_features] * (num_layers - 1) + [w_dim]
+
+        if c_dim > 0:
+            self.embed = FullyConnectedLayer(c_dim, embed_features)
+        for idx in range(num_layers):
+            in_features = features_list[idx]
+            out_features = features_list[idx + 1]
+            layer = FullyConnectedLayer(in_features,
+                                        out_features,
+                                        activation=activation,
+                                        lr_multiplier=lr_multiplier)
+            setattr(self, f'fc{idx}', layer)
+
+        if num_ws is not None and w_avg_beta is not None:
+            self.register_buffer('w_avg', torch.zeros([w_dim]))
+
+    def forward(self,
+                z,
+                c,
+                truncation_psi=1,
+                truncation_cutoff=None,
+                update_emas=False):
+        # Embed, normalize, and concat inputs.
+        x = None
+        with torch.autograd.profiler.record_function('input'):
+            if self.z_dim > 0:
+                misc.assert_shape(z, [None, self.z_dim])
+                x = normalize_2nd_moment(z.to(torch.float32))
+            if self.c_dim > 0:
+                misc.assert_shape(c, [None, self.c_dim])
+                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
+                x = torch.cat([x, y], dim=1) if x is not None else y
+
+        # Main layers.
+        for idx in range(self.num_layers):
+            layer = getattr(self, f'fc{idx}')
+            x = layer(x)
+
+        # Update moving average of W.
+        if update_emas and self.w_avg_beta is not None:
+            with torch.autograd.profiler.record_function('update_w_avg'):
+                self.w_avg.copy_(x.detach().mean(dim=0).lerp(
+                    self.w_avg, self.w_avg_beta))
+
+        # Broadcast.
+        if self.num_ws is not None:
+            with torch.autograd.profiler.record_function('broadcast'):
+                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+
+        # Apply truncation.
+        if truncation_psi != 1:
+            with torch.autograd.profiler.record_function('truncate'):
+                assert self.w_avg_beta is not None
+                if self.num_ws is None or truncation_cutoff is None:
+                    x = self.w_avg.lerp(x, truncation_psi)
+                else:
+                    x[:, :truncation_cutoff] = self.w_avg.lerp(
+                        x[:, :truncation_cutoff], truncation_psi)
+        return x
+
+    def extra_repr(self):
+        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class SynthesisLayer(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels,  # Number of input channels.
+            out_channels,  # Number of output channels.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            resolution,  # Resolution of this layer.
+            kernel_size=3,  # Convolution kernel size.
+            up=1,  # Integer upsampling factor.
+            use_noise=True,  # Enable noise input?
+            activation='lrelu',  # Activation function: 'relu', 'lrelu', etc.
+            resample_filter=[
+                1, 3, 3, 1
+            ],  # Low-pass filter to apply when resampling activations.
+            conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            channels_last=False,  # Use channels_last format for the weights?
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.w_dim = w_dim
+        self.resolution = resolution
+        self.up = up
+        self.use_noise = use_noise
+        self.activation = activation
+        self.conv_clamp = conv_clamp
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter(resample_filter))
+        self.padding = kernel_size // 2
+        self.act_gain = bias_act.activation_funcs[activation].def_gain
+
+        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
+        memory_format = torch.channels_last if channels_last else torch.contiguous_format
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_channels, in_channels, kernel_size,
+                         kernel_size]).to(memory_format=memory_format))
+        if use_noise:
+            self.register_buffer('noise_const',
+                                 torch.randn([resolution, resolution]))
+            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+
+    # def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
+    def forward(self, x, w, noise_mode='const', fused_modconv=True, gain=1):
+        assert noise_mode in ['random', 'const', 'none']
+        in_resolution = self.resolution // self.up
+        misc.assert_shape(
+            x, [None, self.in_channels, in_resolution, in_resolution])
+        styles = self.affine(w)
+
+        noise = None
+        if self.use_noise and noise_mode == 'random':
+            noise = torch.randn(
+                [x.shape[0], 1, self.resolution, self.resolution],
+                device=x.device) * self.noise_strength
+        if self.use_noise and noise_mode == 'const':
+            noise = self.noise_const * self.noise_strength
+
+        flip_weight = (self.up == 1)  # slightly faster
+        x = modulated_conv2d(x=x,
+                             weight=self.weight,
+                             styles=styles,
+                             noise=noise,
+                             up=self.up,
+                             padding=self.padding,
+                             resample_filter=self.resample_filter,
+                             flip_weight=flip_weight,
+                             fused_modconv=fused_modconv)
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        x = bias_act.bias_act(x,
+                              self.bias.to(x.dtype),
+                              act=self.activation,
+                              gain=act_gain,
+                              clamp=act_clamp)
+        return x
+
+    def extra_repr(self):
+        return ' '.join([
+            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
+            f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'
+        ])
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class ToRGBLayer(torch.nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 w_dim,
+                 kernel_size=1,
+                 conv_clamp=None,
+                 channels_last=False):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.w_dim = w_dim
+        self.conv_clamp = conv_clamp
+        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
+        memory_format = torch.channels_last if channels_last else torch.contiguous_format
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_channels, in_channels, kernel_size,
+                         kernel_size]).to(memory_format=memory_format))
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
+
+    def forward(self, x, w, fused_modconv=True):
+        styles = self.affine(w) * self.weight_gain
+        x = modulated_conv2d(x=x,
+                             weight=self.weight,
+                             styles=styles,
+                             demodulate=False,
+                             fused_modconv=fused_modconv)
+        x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
+        return x
+
+    def extra_repr(self):
+        return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class SynthesisBlock(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels,  # Number of input channels, 0 = first block.
+            out_channels,  # Number of output channels.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            resolution,  # Resolution of this block.
+            img_channels,  # Number of output color channels.
+            is_last,  # Is this the last block?
+            architecture='skip',  # Architecture: 'orig', 'skip', 'resnet'.
+            resample_filter=[
+                1, 3, 3, 1
+            ],  # Low-pass filter to apply when resampling activations.
+            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            use_fp16=False,  # Use FP16 for this block?
+            fp16_channels_last=False,  # Use channels-last memory format with FP16?
+            fused_modconv_default=True,  # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
+            **layer_kwargs,  # Arguments for SynthesisLayer.
+    ):
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.w_dim = w_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.is_last = is_last
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = (use_fp16 and fp16_channels_last)
+        self.fused_modconv_default = fused_modconv_default
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter(resample_filter))
+        self.num_conv = 0
+        self.num_torgb = 0
+
+        if in_channels == 0:
+            self.const = torch.nn.Parameter(
+                torch.randn([out_channels, resolution, resolution]))
+
+        if in_channels != 0:
+            self.conv0 = SynthesisLayer(in_channels,
+                                        out_channels,
+                                        w_dim=w_dim,
+                                        resolution=resolution,
+                                        up=2,
+                                        resample_filter=resample_filter,
+                                        conv_clamp=conv_clamp,
+                                        channels_last=self.channels_last,
+                                        **layer_kwargs)
+            self.num_conv += 1
+
+        self.conv1 = SynthesisLayer(out_channels,
+                                    out_channels,
+                                    w_dim=w_dim,
+                                    resolution=resolution,
+                                    conv_clamp=conv_clamp,
+                                    channels_last=self.channels_last,
+                                    **layer_kwargs)
+        self.num_conv += 1
+
+        if is_last or architecture == 'skip':
+            self.torgb = ToRGBLayer(out_channels,
+                                    img_channels,
+                                    w_dim=w_dim,
+                                    conv_clamp=conv_clamp,
+                                    channels_last=self.channels_last)
+            self.num_torgb += 1
+
+        if in_channels != 0 and architecture == 'resnet':
+            self.skip = Conv2dLayer(in_channels,
+                                    out_channels,
+                                    kernel_size=1,
+                                    bias=False,
+                                    up=2,
+                                    resample_filter=resample_filter,
+                                    channels_last=self.channels_last)
+
+    def forward(self,
+                x,
+                img,
+                ws,
+                force_fp32=False,
+                fused_modconv=None,
+                update_emas=False,
+                **layer_kwargs):
+        _ = update_emas  # unused
+        misc.assert_shape(ws,
+                          [None, self.num_conv + self.num_torgb, self.w_dim])
+        w_iter = iter(ws.unbind(dim=1))
+        if ws.device.type != 'cuda':
+            force_fp32 = True
+        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
+        if fused_modconv is None:
+            fused_modconv = self.fused_modconv_default
+        if fused_modconv == 'inference_only':
+            fused_modconv = (not self.training)
+
+        # Input.
+        if self.in_channels == 0:
+            x = self.const.to(dtype=dtype, memory_format=memory_format)
+            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
+        else:
+            misc.assert_shape(x, [
+                None, self.in_channels, self.resolution // 2,
+                self.resolution // 2
+            ])
+            x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # Main layers.
+        if self.in_channels == 0:
+            x = self.conv1(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+        elif self.architecture == 'resnet':
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+            x = self.conv1(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           gain=np.sqrt(0.5),
+                           **layer_kwargs)
+            x = y.add_(x)
+        else:
+            x = self.conv0(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+            x = self.conv1(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+
+        # ToRGB.
+        if img is not None:
+            misc.assert_shape(img, [
+                None, self.img_channels, self.resolution // 2,
+                self.resolution // 2
+            ])
+            img = upfirdn2d.upsample2d(img, self.resample_filter)
+        if self.is_last or self.architecture == 'skip':
+            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
+            y = y.to(dtype=torch.float32,
+                     memory_format=torch.contiguous_format)
+            img = img.add_(y) if img is not None else y
+
+        # assert x.dtype == dtype
+        assert img is None or img.dtype == torch.float32
+        return x, img
+
+    def extra_repr(self):
+        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class SynthesisNetwork(torch.nn.Module):
+    def __init__(
+            self,
+            w_dim,  # Intermediate latent (W) dimensionality.
+            img_resolution,  # Output image resolution.
+            img_channels,  # Number of color channels.
+            channel_base=32768,  # Overall multiplier for the number of channels.
+            channel_max=512,  # Maximum number of channels in any layer.
+            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
+            **block_kwargs,  # Arguments for SynthesisBlock.
+    ):
+        assert img_resolution >= 4 and img_resolution & (img_resolution -
+                                                         1) == 0
+        super().__init__()
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.num_fp16_res = num_fp16_res
+        self.block_resolutions = [
+            2**i for i in range(2, self.img_resolution_log2 + 1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max)
+            for res in self.block_resolutions
+        }
+        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
+                              8)
+
+        self.num_ws = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res // 2] if res > 4 else 0
+            out_channels = channels_dict[res]
+            use_fp16 = (res >= fp16_resolution)
+            is_last = (res == self.img_resolution)
+            block = SynthesisBlock(in_channels,
+                                   out_channels,
+                                   w_dim=w_dim,
+                                   resolution=res,
+                                   img_channels=img_channels,
+                                   is_last=is_last,
+                                   use_fp16=use_fp16,
+                                   **block_kwargs)
+            self.num_ws += block.num_conv
+            if is_last:
+                self.num_ws += block.num_torgb
+            setattr(self, f'b{res}', block)
+
+    def forward(self, ws, **block_kwargs):
+        block_ws = []
+        with torch.autograd.profiler.record_function('split_ws'):
+            misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
+            ws = ws.to(torch.float32)
+            w_idx = 0
+            for res in self.block_resolutions:
+                block = getattr(self, f'b{res}')
+                block_ws.append(
+                    ws.narrow(1, w_idx, block.num_conv +
+                              block.num_torgb))  # dim start length
+                w_idx += block.num_conv
+                # print(f'synthesisNetwork : b{res}, device={block.conv1.weight.device}')
+
+        x = img = None
+        for res, cur_ws in zip(self.block_resolutions, block_ws):
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, cur_ws, **block_kwargs)
+        return img
+
+    def extra_repr(self):
+        return ' '.join([
+            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
+            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
+            f'num_fp16_res={self.num_fp16_res:d}'
+        ])
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class Generator(torch.nn.Module):
+    def __init__(
+            self,
+            z_dim,  # Input latent (Z) dimensionality.
+            c_dim,  # Conditioning label (C) dimensionality.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            img_resolution,  # Output resolution.
+            img_channels,  # Number of output color channels.
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            **synthesis_kwargs,  # Arguments for SynthesisNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.synthesis = SynthesisNetwork(w_dim=w_dim,
+                                          img_resolution=img_resolution,
+                                          img_channels=img_channels,
+                                          **synthesis_kwargs)
+        self.num_ws = self.synthesis.num_ws
+        self.mapping = MappingNetwork(z_dim=z_dim,
+                                      c_dim=c_dim,
+                                      w_dim=w_dim,
+                                      num_ws=self.num_ws,
+                                      **mapping_kwargs)
+
+    def forward(self,
+                z,
+                c,
+                truncation_psi=1,
+                truncation_cutoff=None,
+                update_emas=False,
+                **synthesis_kwargs):
+        ws = self.mapping(z,
+                          c,
+                          truncation_psi=truncation_psi,
+                          truncation_cutoff=truncation_cutoff,
+                          update_emas=update_emas)
+        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
+        return img
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class DiscriminatorBlock(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels,  # Number of input channels, 0 = first block.
+            tmp_channels,  # Number of intermediate channels.
+            out_channels,  # Number of output channels.
+            resolution,  # Resolution of this block.
+            img_channels,  # Number of input color channels.
+            first_layer_idx,  # Index of the first layer.
+            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
+            activation='lrelu',  # Activation function: 'relu', 'lrelu', etc.
+            resample_filter=[
+                1, 3, 3, 1
+            ],  # Low-pass filter to apply when resampling activations.
+            conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            use_fp16=False,  # Use FP16 for this block?
+            fp16_channels_last=False,  # Use channels-last memory format with FP16?
+            freeze_layers=0,  # Freeze-D: Number of layers to freeze.
+    ):
+        assert in_channels in [0, tmp_channels]
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.first_layer_idx = first_layer_idx
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = (use_fp16 and fp16_channels_last)
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter(resample_filter))
+
+        self.num_layers = 0
+
+        def trainable_gen():
+            while True:
+                layer_idx = self.first_layer_idx + self.num_layers
+                trainable = (layer_idx >= freeze_layers)
+                self.num_layers += 1
+                yield trainable
+
+        trainable_iter = trainable_gen()
+
+        if in_channels == 0 or architecture == 'skip':
+            self.fromrgb = Conv2dLayer(img_channels,
+                                       tmp_channels,
+                                       kernel_size=1,
+                                       activation=activation,
+                                       trainable=next(trainable_iter),
+                                       conv_clamp=conv_clamp,
+                                       channels_last=self.channels_last)
+
+        self.conv0 = Conv2dLayer(tmp_channels,
+                                 tmp_channels,
+                                 kernel_size=3,
+                                 activation=activation,
+                                 trainable=next(trainable_iter),
+                                 conv_clamp=conv_clamp,
+                                 channels_last=self.channels_last)
+
+        self.conv1 = Conv2dLayer(tmp_channels,
+                                 out_channels,
+                                 kernel_size=3,
+                                 activation=activation,
+                                 down=2,
+                                 trainable=next(trainable_iter),
+                                 resample_filter=resample_filter,
+                                 conv_clamp=conv_clamp,
+                                 channels_last=self.channels_last)
+
+        if architecture == 'resnet':
+            self.skip = Conv2dLayer(tmp_channels,
+                                    out_channels,
+                                    kernel_size=1,
+                                    bias=False,
+                                    down=2,
+                                    trainable=next(trainable_iter),
+                                    resample_filter=resample_filter,
+                                    channels_last=self.channels_last)
+
+    def forward(self, x, img, force_fp32=False):
+        if (x if x is not None else img).device.type != 'cuda':
+            force_fp32 = True
+        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        # dtype = img.dtype
+        # dtype = x.dtype
+        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
+
+        # Input.
+        if x is not None:
+            misc.assert_shape(
+                x, [None, self.in_channels, self.resolution, self.resolution])
+            x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # FromRGB.
+        if self.in_channels == 0 or self.architecture == 'skip':
+            misc.assert_shape(
+                img,
+                [None, self.img_channels, self.resolution, self.resolution])
+            img = img.to(dtype=dtype, memory_format=memory_format)
+            y = self.fromrgb(img)
+            x = x + y if x is not None else y
+            img = upfirdn2d.downsample2d(
+                img,
+                self.resample_filter) if self.architecture == 'skip' else None
+
+        # Main layers.
+        if self.architecture == 'resnet':
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(x)
+            x = self.conv1(x, gain=np.sqrt(0.5))
+            x = y.add_(x)
+        else:
+            x = self.conv0(x)
+            x = self.conv1(x)
+
+        assert x.dtype == dtype
+        return x, img
+
+    def extra_repr(self):
+        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class MinibatchStdLayer(torch.nn.Module):
+    def __init__(self, group_size, num_channels=1):
+        super().__init__()
+        self.group_size = group_size
+        self.num_channels = num_channels
+
+    def forward(self, x):
+        N, C, H, W = x.shape
+        with misc.suppress_tracer_warnings(
+        ):  # as_tensor results are registered as constants
+            G = torch.min(
+                torch.as_tensor(self.group_size),
+                torch.as_tensor(N)) if self.group_size is not None else N
+        F = self.num_channels
+        c = C // F
+
+        y = x.reshape(
+            G, -1, F, c, H, W
+        )  # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
+        y = y - y.mean(dim=0)  # [GnFcHW] Subtract mean over group.
+        y = y.square().mean(dim=0)  # [nFcHW]  Calc variance over group.
+        y = (y + 1e-8).sqrt()  # [nFcHW]  Calc stddev over group.
+        y = y.mean(dim=[2, 3,
+                        4])  # [nF]     Take average over channels and pixels.
+        y = y.reshape(-1, F, 1, 1)  # [nF11]   Add missing dimensions.
+        y = y.repeat(G, 1, H, W)  # [NFHW]   Replicate over group and pixels.
+        x = torch.cat([x, y],
+                      dim=1)  # [NCHW]   Append to input as new channels.
+        return x
+
+    def extra_repr(self):
+        return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class DiscriminatorEpilogue(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        cmap_dim,  # Dimensionality of mapped conditioning label, 0 = no label.
+        resolution,  # Resolution of this block.
+        img_channels,  # Number of input color channels.
+        architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
+        mbstd_group_size=4,  # Group size for the minibatch standard deviation layer, None = entire minibatch.
+        mbstd_num_channels=1,  # Number of features for the minibatch standard deviation layer, 0 = disable.
+        activation='lrelu',  # Activation function: 'relu', 'lrelu', etc.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+    ):
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.cmap_dim = cmap_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.architecture = architecture
+
+        if architecture == 'skip':
+            self.fromrgb = Conv2dLayer(img_channels,
+                                       in_channels,
+                                       kernel_size=1,
+                                       activation=activation)
+        self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size,
+                                       num_channels=mbstd_num_channels
+                                       ) if mbstd_num_channels > 0 else None
+        self.conv = Conv2dLayer(in_channels + mbstd_num_channels,
+                                in_channels,
+                                kernel_size=3,
+                                activation=activation,
+                                conv_clamp=conv_clamp)
+        self.fc = FullyConnectedLayer(in_channels * (resolution**2),
+                                      in_channels,
+                                      activation=activation)
+        self.out = FullyConnectedLayer(in_channels,
+                                       1 if cmap_dim == 0 else cmap_dim)
+
+    def forward(self, x, img, cmap, force_fp32=False):
+        misc.assert_shape(
+            x, [None, self.in_channels, self.resolution, self.resolution
+                ])  # [NCHW]
+        _ = force_fp32  # unused
+        # dtype = torch.float32
+        dtype = x.dtype
+        memory_format = torch.contiguous_format
+
+        # FromRGB.
+        x = x.to(dtype=dtype, memory_format=memory_format)
+        if self.architecture == 'skip':
+            misc.assert_shape(
+                img,
+                [None, self.img_channels, self.resolution, self.resolution])
+            img = img.to(dtype=dtype, memory_format=memory_format)
+            x = x + self.fromrgb(img)
+
+        # Main layers.
+        if self.mbstd is not None:
+            x = self.mbstd(x)
+        x = self.conv(x)
+        x = self.fc(x.flatten(1))
+        x = self.out(x)
+
+        # Conditioning.
+        if self.cmap_dim > 0:
+            misc.assert_shape(cmap, [None, self.cmap_dim])
+            x = (x * cmap).sum(dim=1,
+                               keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        assert x.dtype == dtype
+        return x
+
+    def extra_repr(self):
+        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class Discriminator(torch.nn.Module):
+    def __init__(
+            self,
+            c_dim,  # Conditioning label (C) dimensionality.
+            img_resolution,  # Input resolution.
+            img_channels,  # Number of input color channels.
+            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
+            channel_base=32768,  # Overall multiplier for the number of channels.
+            channel_max=512,  # Maximum number of channels in any layer.
+            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
+            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+            block_kwargs={},  # Arguments for DiscriminatorBlock.
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [
+            2**i for i in range(self.img_resolution_log2, 2, -1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max)
+            for res in self.block_resolutions + [4]
+        }
+        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
+                              8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(img_channels=img_channels,
+                             architecture=architecture,
+                             conv_clamp=conv_clamp)
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = (res >= fp16_resolution)
+            block = DiscriminatorBlock(in_channels,
+                                       tmp_channels,
+                                       out_channels,
+                                       resolution=res,
+                                       first_layer_idx=cur_layer_idx,
+                                       use_fp16=use_fp16,
+                                       **block_kwargs,
+                                       **common_kwargs)
+            setattr(self, f'b{res}', block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(z_dim=0,
+                                          c_dim=c_dim,
+                                          w_dim=cmap_dim,
+                                          num_ws=None,
+                                          w_avg_beta=None,
+                                          **mapping_kwargs)
+        self.b4 = DiscriminatorEpilogue(channels_dict[4],
+                                        cmap_dim=cmap_dim,
+                                        resolution=4,
+                                        **epilogue_kwargs,
+                                        **common_kwargs)
+
+    def forward(self, img, c, update_emas=False, **block_kwargs):
+        _ = update_emas  # unused
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+    def extra_repr(self):
+        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
+
+
+#----------------------------------------------------------------------------
diff --git a/nsr/networks_stylegan3.py b/nsr/networks_stylegan3.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c346b8a15b3ee00e58a2ac998dda3d5b4453020
--- /dev/null
+++ b/nsr/networks_stylegan3.py
@@ -0,0 +1,679 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Generator architecture from the paper
+"Alias-Free Generative Adversarial Networks"."""
+
+import numpy as np
+import scipy.signal
+import scipy.optimize
+import torch
+from torch_utils import misc
+from torch_utils import persistence
+from torch_utils.ops import conv2d_gradfix
+from torch_utils.ops import filtered_lrelu
+from torch_utils.ops import bias_act
+
+#----------------------------------------------------------------------------
+# from pdb import set_trace as st
+
+
+@misc.profiled_function
+def modulated_conv2d(
+    x,  # Input tensor: [batch_size, in_channels, in_height, in_width]
+    w,  # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
+    s,  # Style tensor: [batch_size, in_channels]
+    demodulate=True,  # Apply weight demodulation?
+    padding=0,  # Padding: int or [padH, padW]
+    input_gain=None,  # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
+):
+    with misc.suppress_tracer_warnings(
+    ):  # this value will be treated as a constant
+        batch_size = int(x.shape[0])
+    out_channels, in_channels, kh, kw = w.shape
+    misc.assert_shape(w, [out_channels, in_channels, kh, kw])  # [OIkk]
+    misc.assert_shape(x, [batch_size, in_channels, None, None])  # [NIHW]
+    misc.assert_shape(s, [batch_size, in_channels])  # [NI]
+
+    # Pre-normalize inputs.
+    if demodulate:
+        w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt()
+        s = s * s.square().mean().rsqrt()
+
+    # Modulate weights.
+    w = w.unsqueeze(0)  # [NOIkk]
+    w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4)  # [NOIkk]
+
+    # Demodulate weights.
+    if demodulate:
+        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
+        w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4)  # [NOIkk]
+
+    # Apply input scaling.
+    if input_gain is not None:
+        input_gain = input_gain.expand(batch_size, in_channels)  # [NI]
+        w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4)  # [NOIkk]
+
+    # Execute as one fused op using grouped convolution.
+    x = x.reshape(1, -1, *x.shape[2:])
+    w = w.reshape(-1, in_channels, kh, kw)
+    x = conv2d_gradfix.conv2d(input=x,
+                              weight=w.to(x.dtype),
+                              padding=padding,
+                              groups=batch_size)
+    x = x.reshape(batch_size, -1, *x.shape[2:])
+    return x
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class FullyConnectedLayer(torch.nn.Module):
+    def __init__(
+            self,
+            in_features,  # Number of input features.
+            out_features,  # Number of output features.
+            activation='linear',  # Activation function: 'relu', 'lrelu', etc.
+            bias=True,  # Apply additive bias before the activation function?
+            lr_multiplier=1,  # Learning rate multiplier.
+            weight_init=1,  # Initial standard deviation of the weight tensor.
+            bias_init=0,  # Initial value of the additive bias.
+    ):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.activation = activation
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_features, in_features]) *
+            (weight_init / lr_multiplier))
+        bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32),
+                                    [out_features])
+        self.bias = torch.nn.Parameter(
+            torch.from_numpy(bias_init / lr_multiplier)) if bias else None
+        self.weight_gain = lr_multiplier / np.sqrt(in_features)
+        self.bias_gain = lr_multiplier
+
+    def forward(self, x):
+        w = self.weight.to(x.dtype) * self.weight_gain
+        b = self.bias
+        if b is not None:
+            b = b.to(x.dtype)
+            if self.bias_gain != 1:
+                b = b * self.bias_gain
+        if self.activation == 'linear' and b is not None:
+            x = torch.addmm(b.unsqueeze(0), x, w.t())
+        else:
+            x = x.matmul(w.t())
+            x = bias_act.bias_act(x, b, act=self.activation)
+        return x
+
+    def extra_repr(self):
+        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class MappingNetwork(torch.nn.Module):
+    def __init__(
+            self,
+            z_dim,  # Input latent (Z) dimensionality.
+            c_dim,  # Conditioning label (C) dimensionality, 0 = no labels.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            num_ws,  # Number of intermediate latents to output.
+            num_layers=2,  # Number of mapping layers.
+            lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
+            w_avg_beta=0.998,  # Decay for tracking the moving average of W during training.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.num_ws = num_ws
+        self.num_layers = num_layers
+        self.w_avg_beta = w_avg_beta
+
+        # Construct layers.
+        self.embed = FullyConnectedLayer(
+            self.c_dim, self.w_dim) if self.c_dim > 0 else None
+        features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)
+                    ] + [self.w_dim] * self.num_layers
+        for idx, in_features, out_features in zip(range(num_layers),
+                                                  features[:-1], features[1:]):
+            layer = FullyConnectedLayer(in_features,
+                                        out_features,
+                                        activation='lrelu',
+                                        lr_multiplier=lr_multiplier)
+            setattr(self, f'fc{idx}', layer)
+        self.register_buffer('w_avg', torch.zeros([w_dim]))
+
+    def forward(self,
+                z,
+                c,
+                truncation_psi=1,
+                truncation_cutoff=None,
+                update_emas=False):
+        misc.assert_shape(z, [None, self.z_dim])
+        if truncation_cutoff is None:
+            truncation_cutoff = self.num_ws
+
+        # Embed, normalize, and concatenate inputs.
+        x = z.to(torch.float32)
+        x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
+        if self.c_dim > 0:
+            misc.assert_shape(c, [None, self.c_dim])
+            y = self.embed(c.to(torch.float32))
+            y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
+            x = torch.cat([x, y], dim=1) if x is not None else y
+
+        # Execute layers.
+        for idx in range(self.num_layers):
+            x = getattr(self, f'fc{idx}')(x)
+
+        # Update moving average of W.
+        if update_emas:
+            self.w_avg.copy_(x.detach().mean(dim=0).lerp(
+                self.w_avg, self.w_avg_beta))
+
+        # Broadcast and apply truncation.
+        x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+        if truncation_psi != 1:
+            x[:, :truncation_cutoff] = self.w_avg.lerp(
+                x[:, :truncation_cutoff], truncation_psi)
+        return x
+
+    def extra_repr(self):
+        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class SynthesisInput(torch.nn.Module):
+    def __init__(
+            self,
+            w_dim,  # Intermediate latent (W) dimensionality.
+            channels,  # Number of output channels.
+            size,  # Output spatial size: int or [width, height].
+            sampling_rate,  # Output sampling rate.
+            bandwidth,  # Output bandwidth.
+    ):
+        super().__init__()
+        self.w_dim = w_dim
+        self.channels = channels
+        self.size = np.broadcast_to(np.asarray(size), [2])
+        self.sampling_rate = sampling_rate
+        self.bandwidth = bandwidth
+
+        # Draw random frequencies from uniform 2D disc.
+        freqs = torch.randn([self.channels, 2])
+        radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
+        freqs /= radii * radii.square().exp().pow(0.25)
+        freqs *= bandwidth
+        phases = torch.rand([self.channels]) - 0.5
+
+        # Setup parameters and buffers.
+        self.weight = torch.nn.Parameter(
+            torch.randn([self.channels, self.channels]))
+        self.affine = FullyConnectedLayer(w_dim,
+                                          4,
+                                          weight_init=0,
+                                          bias_init=[1, 0, 0, 0])
+        self.register_buffer('transform', torch.eye(
+            3, 3))  # User-specified inverse transform wrt. resulting image.
+        self.register_buffer('freqs', freqs)
+        self.register_buffer('phases', phases)
+
+    def forward(self, w):
+        # Introduce batch dimension.
+        transforms = self.transform.unsqueeze(0)  # [batch, row, col]
+        freqs = self.freqs.unsqueeze(0)  # [batch, channel, xy]
+        phases = self.phases.unsqueeze(0)  # [batch, channel]
+
+        # Apply learned transformation.
+        t = self.affine(w)  # t = (r_c, r_s, t_x, t_y)
+        t = t / t[:, :2].norm(dim=1,
+                              keepdim=True)  # t' = (r'_c, r'_s, t'_x, t'_y)
+        m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat(
+            [w.shape[0], 1, 1])  # Inverse rotation wrt. resulting image.
+        m_r[:, 0, 0] = t[:, 0]  # r'_c
+        m_r[:, 0, 1] = -t[:, 1]  # r'_s
+        m_r[:, 1, 0] = t[:, 1]  # r'_s
+        m_r[:, 1, 1] = t[:, 0]  # r'_c
+        m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat(
+            [w.shape[0], 1, 1])  # Inverse translation wrt. resulting image.
+        m_t[:, 0, 2] = -t[:, 2]  # t'_x
+        m_t[:, 1, 2] = -t[:, 3]  # t'_y
+        transforms = m_r @ m_t @ transforms  # First rotate resulting image, then translate, and finally apply user-specified transform.
+
+        # Transform frequencies.
+        phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
+        freqs = freqs @ transforms[:, :2, :2]
+
+        # Dampen out-of-band frequencies that may occur due to the user-specified transform.
+        amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) /
+                      (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
+
+        # Construct sampling grid.
+        theta = torch.eye(2, 3, device=w.device)
+        theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
+        theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
+        grids = torch.nn.functional.affine_grid(
+            theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]],
+            align_corners=False)
+
+        # Compute Fourier features.
+        x = (grids.unsqueeze(3) @ freqs.permute(
+            0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(
+                3)  # [batch, height, width, channel]
+        x = x + phases.unsqueeze(1).unsqueeze(2)
+        x = torch.sin(x * (np.pi * 2))
+        x = x * amplitudes.unsqueeze(1).unsqueeze(2)
+
+        # Apply trainable mapping.
+        weight = self.weight / np.sqrt(self.channels)
+        x = x @ weight.t()
+
+        # Ensure correct shape.
+        x = x.permute(0, 3, 1, 2)  # [batch, channel, height, width]
+        misc.assert_shape(
+            x,
+            [w.shape[0], self.channels,
+             int(self.size[1]),
+             int(self.size[0])])
+        return x
+
+    def extra_repr(self):
+        return '\n'.join([
+            f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
+            f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'
+        ])
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class SynthesisLayer(torch.nn.Module):
+    def __init__(
+            self,
+            w_dim,  # Intermediate latent (W) dimensionality.
+            is_torgb,  # Is this the final ToRGB layer?
+            is_critically_sampled,  # Does this layer use critical sampling?
+            use_fp16,  # Does this layer use FP16?
+
+            # Input & output specifications.
+        in_channels,  # Number of input channels.
+            out_channels,  # Number of output channels.
+            in_size,  # Input spatial size: int or [width, height].
+            out_size,  # Output spatial size: int or [width, height].
+            in_sampling_rate,  # Input sampling rate (s).
+            out_sampling_rate,  # Output sampling rate (s).
+            in_cutoff,  # Input cutoff frequency (f_c).
+            out_cutoff,  # Output cutoff frequency (f_c).
+            in_half_width,  # Input transition band half-width (f_h).
+            out_half_width,  # Output Transition band half-width (f_h).
+
+            # Hyperparameters.
+        conv_kernel=3,  # Convolution kernel size. Ignored for final the ToRGB layer.
+            filter_size=6,  # Low-pass filter size relative to the lower resolution when up/downsampling.
+            lrelu_upsampling=2,  # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
+            use_radial_filters=False,  # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
+            conv_clamp=256,  # Clamp the output to [-X, +X], None = disable clamping.
+            magnitude_ema_beta=0.999,  # Decay rate for the moving average of input magnitudes.
+    ):
+        super().__init__()
+        self.w_dim = w_dim
+        self.is_torgb = is_torgb
+        self.is_critically_sampled = is_critically_sampled
+        self.use_fp16 = use_fp16
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.in_size = np.broadcast_to(np.asarray(in_size), [2])
+        self.out_size = np.broadcast_to(np.asarray(out_size), [2])
+        self.in_sampling_rate = in_sampling_rate
+        self.out_sampling_rate = out_sampling_rate
+        self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (
+            1 if is_torgb else lrelu_upsampling)
+        self.in_cutoff = in_cutoff
+        self.out_cutoff = out_cutoff
+        self.in_half_width = in_half_width
+        self.out_half_width = out_half_width
+        self.conv_kernel = 1 if is_torgb else conv_kernel
+        self.conv_clamp = conv_clamp
+        self.magnitude_ema_beta = magnitude_ema_beta
+
+        # Setup parameters and buffers.
+        self.affine = FullyConnectedLayer(self.w_dim,
+                                          self.in_channels,
+                                          bias_init=1)
+        self.weight = torch.nn.Parameter(
+            torch.randn([
+                self.out_channels, self.in_channels, self.conv_kernel,
+                self.conv_kernel
+            ]))
+        self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
+        self.register_buffer('magnitude_ema', torch.ones([]))
+
+        # Design upsampling filter.
+        self.up_factor = int(
+            np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
+        assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
+        self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
+        self.register_buffer(
+            'up_filter',
+            self.design_lowpass_filter(numtaps=self.up_taps,
+                                       cutoff=self.in_cutoff,
+                                       width=self.in_half_width * 2,
+                                       fs=self.tmp_sampling_rate))
+
+        # Design downsampling filter.
+        self.down_factor = int(
+            np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
+        assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
+        self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
+        self.down_radial = use_radial_filters and not self.is_critically_sampled
+        self.register_buffer(
+            'down_filter',
+            self.design_lowpass_filter(numtaps=self.down_taps,
+                                       cutoff=self.out_cutoff,
+                                       width=self.out_half_width * 2,
+                                       fs=self.tmp_sampling_rate,
+                                       radial=self.down_radial))
+
+        # Compute padding.
+        pad_total = (
+            self.out_size - 1
+        ) * self.down_factor + 1  # Desired output size before downsampling.
+        pad_total -= (self.in_size + self.conv_kernel -
+                      1) * self.up_factor  # Input size after upsampling.
+        pad_total += self.up_taps + self.down_taps - 2  # Size reduction caused by the filters.
+        pad_lo = (
+            pad_total + self.up_factor
+        ) // 2  # Shift sample locations according to the symmetric interpretation (Appendix C.3).
+        pad_hi = pad_total - pad_lo
+        self.padding = [
+            int(pad_lo[0]),
+            int(pad_hi[0]),
+            int(pad_lo[1]),
+            int(pad_hi[1])
+        ]
+
+    def forward(self,
+                x,
+                w,
+                noise_mode='random',
+                force_fp32=False,
+                update_emas=False):
+        assert noise_mode in ['random', 'const', 'none']  # unused
+        misc.assert_shape(x, [
+            None, self.in_channels,
+            int(self.in_size[1]),
+            int(self.in_size[0])
+        ])
+        misc.assert_shape(w, [x.shape[0], self.w_dim])
+
+        # Track input magnitude.
+        if update_emas:
+            with torch.autograd.profiler.record_function(
+                    'update_magnitude_ema'):
+                magnitude_cur = x.detach().to(torch.float32).square().mean()
+                self.magnitude_ema.copy_(
+                    magnitude_cur.lerp(self.magnitude_ema,
+                                       self.magnitude_ema_beta))
+        input_gain = self.magnitude_ema.rsqrt()
+
+        # Execute affine layer.
+        styles = self.affine(w)
+        if self.is_torgb:
+            weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel**2))
+            styles = styles * weight_gain
+
+        # Execute modulated conv2d.
+        dtype = torch.float16 if (self.use_fp16 and not force_fp32 and
+                                  x.device.type == 'cuda') else torch.float32
+        x = modulated_conv2d(x=x.to(dtype),
+                             w=self.weight,
+                             s=styles,
+                             padding=self.conv_kernel - 1,
+                             demodulate=(not self.is_torgb),
+                             input_gain=input_gain)
+
+        # Execute bias, filtered leaky ReLU, and clamping.
+        gain = 1 if self.is_torgb else np.sqrt(2)
+        slope = 1 if self.is_torgb else 0.2
+        x = filtered_lrelu.filtered_lrelu(x=x,
+                                          fu=self.up_filter,
+                                          fd=self.down_filter,
+                                          b=self.bias.to(x.dtype),
+                                          up=self.up_factor,
+                                          down=self.down_factor,
+                                          padding=self.padding,
+                                          gain=gain,
+                                          slope=slope,
+                                          clamp=self.conv_clamp)
+
+        # Ensure correct shape and dtype.
+        misc.assert_shape(x, [
+            None, self.out_channels,
+            int(self.out_size[1]),
+            int(self.out_size[0])
+        ])
+        assert x.dtype == dtype
+        return x
+
+    @staticmethod
+    def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
+        assert numtaps >= 1
+
+        # Identity filter.
+        if numtaps == 1:
+            return None
+
+        # Separable Kaiser low-pass filter.
+        if not radial:
+            f = scipy.signal.firwin(numtaps=numtaps,
+                                    cutoff=cutoff,
+                                    width=width,
+                                    fs=fs)
+            return torch.as_tensor(f, dtype=torch.float32)
+
+        # Radially symmetric jinc-based filter.
+        x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
+        r = np.hypot(*np.meshgrid(x, x))
+        f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
+        beta = scipy.signal.kaiser_beta(
+            scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
+        w = np.kaiser(numtaps, beta)
+        f *= np.outer(w, w)
+        f /= np.sum(f)
+        return torch.as_tensor(f, dtype=torch.float32)
+
+    def extra_repr(self):
+        return '\n'.join([
+            f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
+            f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
+            f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
+            f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
+            f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
+            f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
+            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'
+        ])
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class SynthesisNetwork(torch.nn.Module):
+    def __init__(
+            self,
+            w_dim,  # Intermediate latent (W) dimensionality.
+            img_resolution,  # Output image resolution.
+            img_channels,  # Number of color channels.
+            channel_base=32768,  # Overall multiplier for the number of channels.
+            channel_max=512,  # Maximum number of channels in any layer.
+            num_layers=14,  # Total number of layers, excluding Fourier features and ToRGB.
+            num_critical=2,  # Number of critically sampled layers at the end.
+            first_cutoff=2,  # Cutoff frequency of the first layer (f_{c,0}).
+            first_stopband=2**
+        2.1,  # Minimum stopband of the first layer (f_{t,0}).
+            last_stopband_rel=2**
+        0.3,  # Minimum stopband of the last layer, expressed relative to the cutoff.
+            margin_size=10,  # Number of additional pixels outside the image.
+            output_scale=0.25,  # Scale factor for the output image.
+            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
+            **layer_kwargs,  # Arguments for SynthesisLayer.
+    ):
+        super().__init__()
+        self.w_dim = w_dim
+        self.num_ws = num_layers + 2
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.num_layers = num_layers
+        self.num_critical = num_critical
+        self.margin_size = margin_size
+        self.output_scale = output_scale
+        self.num_fp16_res = num_fp16_res
+
+        # Geometric progression of layer cutoffs and min. stopbands.
+        last_cutoff = self.img_resolution / 2  # f_{c,N}
+        last_stopband = last_cutoff * last_stopband_rel  # f_{t,N}
+        exponents = np.minimum(
+            np.arange(self.num_layers + 1) /
+            (self.num_layers - self.num_critical), 1)
+        cutoffs = first_cutoff * (last_cutoff /
+                                  first_cutoff)**exponents  # f_c[i]
+        stopbands = first_stopband * (last_stopband /
+                                      first_stopband)**exponents  # f_t[i]
+
+        # Compute remaining layer parameters.
+        sampling_rates = np.exp2(
+            np.ceil(np.log2(np.minimum(stopbands * 2,
+                                       self.img_resolution))))  # s[i]
+        half_widths = np.maximum(stopbands,
+                                 sampling_rates / 2) - cutoffs  # f_h[i]
+        sizes = sampling_rates + self.margin_size * 2
+        sizes[-2:] = self.img_resolution
+        channels = np.rint(
+            np.minimum((channel_base / 2) / cutoffs, channel_max))
+        channels[-1] = self.img_channels
+
+        # Construct layers.
+        self.input = SynthesisInput(w_dim=self.w_dim,
+                                    channels=int(channels[0]),
+                                    size=int(sizes[0]),
+                                    sampling_rate=sampling_rates[0],
+                                    bandwidth=cutoffs[0])
+        self.layer_names = []
+        for idx in range(self.num_layers + 1):
+            prev = max(idx - 1, 0)
+            is_torgb = (idx == self.num_layers)
+            is_critically_sampled = (idx >=
+                                     self.num_layers - self.num_critical)
+            use_fp16 = (sampling_rates[idx] *
+                        (2**self.num_fp16_res) > self.img_resolution)
+            layer = SynthesisLayer(w_dim=self.w_dim,
+                                   is_torgb=is_torgb,
+                                   is_critically_sampled=is_critically_sampled,
+                                   use_fp16=use_fp16,
+                                   in_channels=int(channels[prev]),
+                                   out_channels=int(channels[idx]),
+                                   in_size=int(sizes[prev]),
+                                   out_size=int(sizes[idx]),
+                                   in_sampling_rate=int(sampling_rates[prev]),
+                                   out_sampling_rate=int(sampling_rates[idx]),
+                                   in_cutoff=cutoffs[prev],
+                                   out_cutoff=cutoffs[idx],
+                                   in_half_width=half_widths[prev],
+                                   out_half_width=half_widths[idx],
+                                   **layer_kwargs)
+            name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
+            setattr(self, name, layer)
+            self.layer_names.append(name)
+
+    def forward(self, ws, **layer_kwargs):
+        misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
+        ws = ws.to(torch.float32).unbind(dim=1)
+
+        # Execute layers.
+        x = self.input(ws[0])
+        for name, w in zip(self.layer_names, ws[1:]):
+            x = getattr(self, name)(x, w, **layer_kwargs)
+        if self.output_scale != 1:
+            x = x * self.output_scale
+
+        # Ensure correct shape and dtype.
+        misc.assert_shape(x, [
+            None, self.img_channels, self.img_resolution, self.img_resolution
+        ])
+        x = x.to(torch.float32)
+        return x
+
+    def extra_repr(self):
+        return '\n'.join([
+            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
+            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
+            f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
+            f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'
+        ])
+
+
+#----------------------------------------------------------------------------
+
+
+@persistence.persistent_class
+class Generator(torch.nn.Module):
+    def __init__(
+            self,
+            z_dim,  # Input latent (Z) dimensionality.
+            c_dim,  # Conditioning label (C) dimensionality.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            img_resolution,  # Output resolution.
+            img_channels,  # Number of output color channels.
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            **synthesis_kwargs,  # Arguments for SynthesisNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.synthesis = SynthesisNetwork(w_dim=w_dim,
+                                          img_resolution=img_resolution,
+                                          img_channels=img_channels,
+                                          **synthesis_kwargs)
+        self.num_ws = self.synthesis.num_ws
+        self.mapping = MappingNetwork(z_dim=z_dim,
+                                      c_dim=c_dim,
+                                      w_dim=w_dim,
+                                      num_ws=self.num_ws,
+                                      **mapping_kwargs)
+
+    def forward(self,
+                z,
+                c,
+                truncation_psi=1,
+                truncation_cutoff=None,
+                update_emas=False,
+                **synthesis_kwargs):
+        ws = self.mapping(z,
+                          c,
+                          truncation_psi=truncation_psi,
+                          truncation_cutoff=truncation_cutoff,
+                          update_emas=update_emas)
+        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
+        return img
+
+
+#----------------------------------------------------------------------------
diff --git a/nsr/options.py b/nsr/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8c75bd4cc1f883cff55cfdae88a39d8c3ca7e4f
--- /dev/null
+++ b/nsr/options.py
@@ -0,0 +1,120 @@
+import tyro
+from dataclasses import dataclass
+from typing import Tuple, Literal, Dict, Optional
+
+
+@dataclass
+class Options:
+    ### model
+    # Unet image input size
+    input_size: int = 256
+    # Unet definition
+    down_channels: Tuple[int, ...] = (64, 128, 256, 512, 1024, 1024)
+    down_attention: Tuple[bool, ...] = (False, False, False, True, True, True)
+    mid_attention: bool = True
+    up_channels: Tuple[int, ...] = (1024, 1024, 512, 256)
+    up_attention: Tuple[bool, ...] = (True, True, True, False)
+    # Unet output size, dependent on the input_size and U-Net structure!
+    splat_size: int = 64
+    # gaussian render size
+    output_size: int = 256
+
+    ### dataset
+    # data mode (only support s3 now)
+    data_mode: Literal['s3'] = 's3'
+    # fovy of the dataset
+    fovy: float = 49.1
+    # camera near plane
+    znear: float = 0.5
+    # camera far plane
+    zfar: float = 2.5
+    # number of all views (input + output)
+    num_views: int = 12
+    # number of views
+    num_input_views: int = 4
+    # camera radius
+    cam_radius: float = 1.5 # to better use [-1, 1]^3 space
+    # num workers
+    num_workers: int = 8
+
+    ### training
+    # workspace
+    workspace: str = './workspace'
+    # resume
+    resume: Optional[str] = None
+    # batch size (per-GPU)
+    batch_size: int = 8
+    # gradient accumulation
+    gradient_accumulation_steps: int = 1
+    # training epochs
+    num_epochs: int = 30
+    # lpips loss weight
+    lambda_lpips: float = 1.0
+    # gradient clip
+    gradient_clip: float = 1.0
+    # mixed precision
+    mixed_precision: str = 'bf16'
+    # learning rate
+    lr: float = 4e-4
+    # augmentation prob for grid distortion
+    prob_grid_distortion: float = 0.5
+    # augmentation prob for camera jitter
+    prob_cam_jitter: float = 0.5
+
+    ### testing
+    # test image path
+    test_path: Optional[str] = None
+
+    ### misc
+    # nvdiffrast backend setting
+    force_cuda_rast: bool = False
+    # render fancy video with gaussian scaling effect
+    fancy_video: bool = False
+    
+
+# all the default settings
+config_defaults: Dict[str, Options] = {}
+config_doc: Dict[str, str] = {}
+
+config_doc['lrm'] = 'the default settings for LGM'
+config_defaults['lrm'] = Options()
+
+config_doc['small'] = 'small model with lower resolution Gaussians'
+config_defaults['small'] = Options(
+    input_size=256,
+    splat_size=64,
+    output_size=256,
+    batch_size=8,
+    gradient_accumulation_steps=1,
+    mixed_precision='bf16',
+)
+
+config_doc['big'] = 'big model with higher resolution Gaussians'
+config_defaults['big'] = Options(
+    input_size=256,
+    up_channels=(1024, 1024, 512, 256, 128), # one more decoder
+    up_attention=(True, True, True, False, False),
+    splat_size=128,
+    output_size=512, # render & supervise Gaussians at a higher resolution.
+    batch_size=8,
+    num_views=8,
+    gradient_accumulation_steps=1,
+    mixed_precision='bf16',
+)
+
+config_doc['tiny'] = 'tiny model for ablation'
+config_defaults['tiny'] = Options(
+    input_size=256, 
+    down_channels=(32, 64, 128, 256, 512),
+    down_attention=(False, False, False, False, True),
+    up_channels=(512, 256, 128),
+    up_attention=(True, False, False, False),
+    splat_size=64,
+    output_size=256,
+    batch_size=16,
+    num_views=8,
+    gradient_accumulation_steps=1,
+    mixed_precision='bf16',
+)
+
+AllConfigs = tyro.extras.subcommand_type_from_defaults(config_defaults, config_doc)
\ No newline at end of file
diff --git a/nsr/script_util.py b/nsr/script_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..082c8205a0964cb4161690425d149f62ffe9ec4a
--- /dev/null
+++ b/nsr/script_util.py
@@ -0,0 +1,1708 @@
+import torch
+from torch import nn
+from nsr.triplane import Triplane_fg_bg_plane
+# import timm
+# from vit.vit_triplane import ViTTriplane, Triplane, ViTTriplaneDecomposed
+from vit.vit_triplane import Triplane, ViTTriplaneDecomposed
+import argparse
+import inspect
+import dnnlib
+from guided_diffusion import dist_util
+
+from pdb import set_trace as st
+
+import vit.vision_transformer as vits
+from guided_diffusion import logger
+from .confnet import ConfNet
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder, MVEncoder, MVEncoderGS, MVEncoderGSDynamicInp, MVEncoderGSDynamicInp_CA
+from ldm.modules.diffusionmodules.mv_unet import MVUNet, LGM_MVEncoder
+from torch.profiler import profile, record_function, ProfilerActivity
+
+from nsr.gs import GaussianRenderer
+from nsr.gs_surfel import GaussianRenderer2DGS
+# from nsr.srt.encoder import ImprovedSRTEncoderVAE, ImprovedSRTEncoderVAE_L5_vitl, ImprovedSRTEncoderVAE_mlp_ratio4, ImprovedSRTEncoderVAE_L6, ImprovedSRTEncoderVAE_mlp_ratio4_f8, ImprovedSRTEncoderVAE_mlp_ratio4_heavyPatchify, ImprovedSRTEncoderVAE_mlp_ratio4_f8_L6, ImprovedSRTEncoderVAE_mlp_ratio4_L6, HybridEncoder, ImprovedSRTEncoderVAE_mlp_ratio4_decomposed, HybridEncoderPCDStructuredLatent
+from nsr.srt.encoder import *
+
+# from ldm.modules.diffusionmodules.openaimodel import MultiViewUNetModel_Encoder
+
+# * create pre-trained encoder & triplane / other nsr decoder
+
+
+class AE(torch.nn.Module):
+
+    def __init__(self,
+                 encoder,
+                 decoder,
+                 img_size,
+                 encoder_cls_token,
+                 decoder_cls_token,
+                 preprocess,
+                 use_clip,
+                 dino_version='v1',
+                 clip_dtype=None,
+                 no_dim_up_mlp=False,
+                 dim_up_mlp_as_func=False,
+                 uvit_skip_encoder=False,
+                 confnet=None) -> None:
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.img_size = img_size
+        self.encoder_cls_token = encoder_cls_token
+        self.decoder_cls_token = decoder_cls_token
+        self.use_clip = use_clip
+        self.dino_version = dino_version
+        self.confnet = confnet
+
+        if self.dino_version == 'v2':
+            self.encoder.mask_token = None
+            self.decoder.vit_decoder.mask_token = None
+
+        if 'sd' not in self.dino_version:
+
+            self.uvit_skip_encoder = uvit_skip_encoder
+            if uvit_skip_encoder:
+                logger.log(
+                    f'enables uvit: length of vit_encoder.blocks: {len(self.encoder.blocks)}'
+                )
+                for blk in self.encoder.blocks[len(self.encoder.blocks) // 2:]:
+                    blk.skip_linear = nn.Linear(2 * self.encoder.embed_dim,
+                                                self.encoder.embed_dim)
+
+                    # trunc_normal_(blk.skip_linear.weight, std=.02)
+                    nn.init.constant_(blk.skip_linear.weight, 0)
+                    if isinstance(
+                            blk.skip_linear,
+                            nn.Linear) and blk.skip_linear.bias is not None:
+                        nn.init.constant_(blk.skip_linear.bias, 0)
+            else:
+                logger.log(f'disable uvit')
+        else:
+            if 'dit' not in self.dino_version:  # dino vit, not dit
+                self.decoder.vit_decoder.cls_token = None
+                self.decoder.vit_decoder.patch_embed.proj = nn.Identity()
+                self.decoder.triplane_decoder.planes = None
+                self.decoder.vit_decoder.mask_token = None
+
+            if self.use_clip:
+                self.clip_dtype = clip_dtype  # torch.float16
+
+            else:
+
+                if not no_dim_up_mlp and self.encoder.embed_dim != self.decoder.vit_decoder.embed_dim:
+                    self.dim_up_mlp = nn.Linear(
+                        self.encoder.embed_dim,
+                        self.decoder.vit_decoder.embed_dim)
+                    logger.log(
+                        f"dim_up_mlp: {self.encoder.embed_dim} -> {self.decoder.vit_decoder.embed_dim}, as_func: {self.dim_up_mlp_as_func}"
+                    )
+                else:
+                    logger.log('ignore dim_up_mlp: ', no_dim_up_mlp)
+
+        self.preprocess = preprocess
+
+        self.dim_up_mlp = None  # CLIP/B-16
+        self.dim_up_mlp_as_func = dim_up_mlp_as_func
+
+        # * remove certain components to make sure no unused parameters during DDP
+        # self.decoder.vit_decoder.cls_token = nn.Identity()
+        torch.cuda.empty_cache()
+        # self.decoder.vit_decoder.patch_embed.proj.bias = nn.Identity()
+        # self.decoder.vit_decoder.patch_embed.proj.weight = nn.Identity()
+        # self.decoder.vit_decoder.patch_embed.proj.bias = nn.Identity()
+
+    def encode(self, *args, **kwargs):
+        if not self.use_clip:
+            if self.dino_version == 'v1':
+                latent = self.encode_dinov1(*args, **kwargs)
+            elif self.dino_version == 'v2':
+                if self.uvit_skip_encoder:
+                    latent = self.encode_dinov2_uvit(*args, **kwargs)
+                else:
+                    latent = self.encode_dinov2(*args, **kwargs)
+            else:
+                latent = self.encoder(*args, **kwargs)
+
+        else:
+            latent = self.encode_clip(*args, **kwargs)
+
+        return latent
+
+    def encode_dinov1(self, x):
+        # return self.encoder(img)
+        x = self.encoder.prepare_tokens(x)
+        for blk in self.encoder.blocks:
+            x = blk(x)
+        x = self.encoder.norm(x)
+        if not self.encoder_cls_token:
+            return x[:, 1:]
+
+        return x
+
+    def encode_dinov2(self, x):
+        # return self.encoder(img)
+        x = self.encoder.prepare_tokens_with_masks(x, masks=None)
+        for blk in self.encoder.blocks:
+            x = blk(x)
+        x_norm = self.encoder.norm(x)
+
+        if not self.encoder_cls_token:
+            return x_norm[:, 1:]
+        # else:
+        # return x_norm[:, :1]
+
+        # return {
+        #     "x_norm_clstoken": x_norm[:, 0],
+        #     "x_norm_patchtokens": x_norm[:, 1:],
+        # }
+
+        return x_norm
+
+    def encode_dinov2_uvit(self, x):
+        # return self.encoder(img)
+        x = self.encoder.prepare_tokens_with_masks(x, masks=None)
+
+        # for blk in self.encoder.blocks:
+        #     x = blk(x)
+
+        skips = [x]
+
+        # in blks
+        for blk in self.encoder.blocks[0:len(self.encoder.blocks) // 2 - 1]:
+            x = blk(x)  # B 3 N C
+            skips.append(x)
+
+        # mid blks
+        for blk in self.encoder.blocks[len(self.encoder.blocks) // 2 -
+                                       1:len(self.encoder.blocks) // 2]:
+            x = blk(x)  # B 3 N C
+
+        # out blks
+        for blk in self.encoder.blocks[len(self.encoder.blocks) // 2:]:
+            x = x + blk.skip_linear(torch.cat(
+                [x, skips.pop()], dim=-1))  # long skip connections in uvit
+            x = blk(x)  # B 3 N C
+
+        x_norm = self.encoder.norm(x)
+
+        if not self.decoder_cls_token:
+            return x_norm[:, 1:]
+
+        return x_norm
+
+    def encode_clip(self, x):
+        # * replace with CLIP encoding pipeline
+        # return self.encoder(img)
+        # x = x.dtype(self.clip_dtype)
+        x = self.encoder.conv1(x)  # shape = [*, width, grid, grid]
+        x = x.reshape(x.shape[0], x.shape[1],
+                      -1)  # shape = [*, width, grid ** 2]
+        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+        x = torch.cat([
+            self.encoder.class_embedding.to(x.dtype) + torch.zeros(
+                x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x
+        ],
+                      dim=1)  # shape = [*, grid ** 2 + 1, width]
+        x = x + self.encoder.positional_embedding.to(x.dtype)
+        x = self.encoder.ln_pre(x)
+
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.encoder.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.encoder.ln_post(x[:, 1:, :])  # * return the spatial tokens
+
+        return x
+
+        # x = self.ln_post(x[:, 0, :]) # * return the spatial tokens
+
+        # if self.proj is not None:
+        #     x = x @ self.proj
+
+        # return x
+
+    def decode_wo_triplane(self, latent, c=None, img_size=None):
+        if img_size is None:
+            img_size = self.img_size
+
+        if self.dim_up_mlp is not None:
+            if not self.dim_up_mlp_as_func:
+                latent = self.dim_up_mlp(latent)
+                # return self.decoder.vit_decode(latent, img_size)
+            else:
+                return self.decoder.vit_decode(
+                    latent, img_size,
+                    dim_up_mlp=self.dim_up_mlp)  # used in vae-ldm
+
+        return self.decoder.vit_decode(latent, img_size, c=c)
+
+    def decode(self, latent, c, img_size=None, return_raw_only=False):
+        # if img_size is None:
+        #     img_size = self.img_size
+
+        # if self.dim_up_mlp is not None:
+        #     latent = self.dim_up_mlp(latent)
+
+        latent = self.decode_wo_triplane(latent, img_size=img_size, c=c)
+        # return self.decoder.triplane_decode(latent, c, return_raw_only=return_raw_only)
+        return self.decoder.triplane_decode(latent, c)
+
+    def decode_after_vae_no_render(
+        self,
+        ret_dict,
+        img_size=None,
+    ):
+
+        if img_size is None:
+            img_size = self.img_size
+
+        assert self.dim_up_mlp is None
+        # if not self.dim_up_mlp_as_func:
+        #     latent = self.dim_up_mlp(latent)
+        # return self.decoder.vit_decode(latent, img_size)
+
+        latent = self.decoder.vit_decode_backbone(ret_dict, img_size)
+        ret_dict = self.decoder.vit_decode_postprocess(latent, ret_dict)
+        return ret_dict
+
+    def decode_after_vae_no_render_gs(
+        self,
+        ret_dict,
+        img_size=None,
+    ):
+
+        ret_after_decoder = self.decode_after_vae_no_render(ret_dict, img_size)
+        return self.decoder.forward_gaussians(ret_after_decoder, c=None)
+
+    def decode_after_vae(
+            self,
+            #  latent,
+            ret_dict,  # vae_dict
+            c,
+            img_size=None,
+            return_raw_only=False):
+        ret_dict = self.decode_after_vae_no_render(ret_dict, img_size)
+        return self.decoder.triplane_decode(ret_dict, c)
+
+    def decode_confmap(self, img):
+        assert self.confnet is not None
+        # https://github.com/elliottwu/unsup3d/blob/dc961410d61684561f19525c2f7e9ee6f4dacb91/unsup3d/model.py#L152
+        # conf_sigma_l1 = self.confnet(img)  # Bx2xHxW
+        return self.confnet(img)  # Bx1xHxW
+
+    def encode_decode(self, img, c, return_raw_only=False):
+        latent = self.encode(img)
+        pred = self.decode(latent, c, return_raw_only=return_raw_only)
+        if self.confnet is not None:
+            pred.update({
+                'conf_sigma': self.decode_confmap(img)  # 224x224
+            })
+
+        return pred
+
+    def forward(self,
+                img=None,
+                c=None,
+                latent=None,
+                behaviour='enc_dec',
+                coordinates=None,
+                directions=None,
+                return_raw_only=False,
+                *args,
+                **kwargs):
+        """wrap all operations inside forward() for DDP use.
+        """
+
+        if behaviour == 'enc_dec':
+            pred = self.encode_decode(img, c, return_raw_only=return_raw_only)
+            return pred
+
+        elif behaviour == 'enc':
+            latent = self.encode(img)
+            return latent
+
+        elif behaviour == 'dec':
+            assert latent is not None
+            pred: dict = self.decode(latent,
+                                     c,
+                                     self.img_size,
+                                     return_raw_only=return_raw_only)
+            return pred
+
+        elif behaviour == 'dec_wo_triplane':
+            assert latent is not None
+            pred: dict = self.decode_wo_triplane(latent, self.img_size)
+            return pred
+
+        elif behaviour == 'enc_dec_wo_triplane':
+            # with profile(activities=[
+            #     ProfilerActivity.CUDA], record_shapes=True) as prof:
+                # with record_function("encoding"):
+            latent = self.encode(img, c=c, **kwargs)
+
+            # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+            # with profile(activities=[
+            #     ProfilerActivity.CUDA], record_shapes=True) as prof:
+            #     with record_function("decoding"):
+            pred: dict = self.decode_wo_triplane(latent,
+                                                img_size=self.img_size,
+                                                c=c)
+            # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+            # st()
+
+            return pred
+
+        elif behaviour == 'encoder_vae':
+            latent = self.encode(img)
+            ret_dict = self.decoder.vae_reparameterization(latent, True)
+            return ret_dict
+
+        elif behaviour == 'decode_after_vae_no_render':
+            pred: dict = self.decode_after_vae_no_render(latent, self.img_size)
+            return pred
+
+        elif behaviour == 'decode_gs_after_vae_no_render':
+            pred: dict = self.decode_after_vae_no_render_gs(latent, self.img_size)
+            return pred
+
+        elif behaviour == 'decode_after_vae':
+            pred: dict = self.decode_after_vae(latent, c, self.img_size)
+            return pred
+
+        # elif behaviour == 'gaussian_dec':
+        #     assert latent is not None
+        #     pred: dict = self.decoder.triplane_decode(
+        #         latent, c, return_raw_only=return_raw_only, **kwargs)
+        #     # pred: dict = self.decoder.triplane_decode(latent, c)
+
+        elif behaviour == 'triplane_dec':
+            assert latent is not None
+            pred: dict = self.decoder.triplane_decode(
+                latent, c, return_raw_only=return_raw_only, **kwargs)
+            # pred: dict = self.decoder.triplane_decode(latent, c)
+
+        elif behaviour == 'triplane_decode_grid':
+            assert latent is not None
+            pred: dict = self.decoder.triplane_decode_grid(latent, **kwargs)
+            # pred: dict = self.decoder.triplane_decode(latent, c)
+
+        elif behaviour == 'vit_postprocess_triplane_dec':
+            assert latent is not None
+            latent = self.decoder.vit_decode_postprocess(
+                latent)  # translate spatial token from vit-decoder into 2D
+            pred: dict = self.decoder.triplane_decode(
+                latent, c)  # render with triplane
+
+        elif behaviour == 'triplane_renderer':
+            assert latent is not None
+            pred: dict = self.decoder.triplane_renderer(
+                latent, coordinates, directions)
+
+        # elif behaviour == 'triplane_SR':
+        #     assert latent is not None
+        #     pred: dict = self.decoder.triplane_renderer(
+        #         latent, coordinates, directions)
+
+        elif behaviour == 'get_rendering_kwargs':
+            pred = self.decoder.triplane_decoder.rendering_kwargs
+
+        return pred
+
+
+class AE_CLIPEncoder(AE):
+
+    def __init__(self, encoder, decoder, img_size, cls_token) -> None:
+        super().__init__(encoder, decoder, img_size, cls_token)
+
+
+class AE_with_Diffusion(torch.nn.Module):
+
+    def __init__(self, auto_encoder, denoise_model) -> None:
+        super().__init__()
+        self.auto_encoder = auto_encoder
+        self.denoise_model = denoise_model  # simply for easy MPTrainer manipulation
+
+    def forward(self,
+                img,
+                c,
+                behaviour='enc_dec',
+                latent=None,
+                *args,
+                **kwargs):
+        # wrap auto_encoder and denoising model inside a single forward function to use DDP (only forward supported) and MPTrainer (single model) easier
+        if behaviour == 'enc_dec':
+            pred = self.auto_encoder(img, c)
+            return pred
+        elif behaviour == 'enc':
+            latent = self.auto_encoder.encode(img)
+            if self.auto_encoder.dim_up_mlp is not None:
+                latent = self.auto_encoder.dim_up_mlp(latent)
+            return latent
+        elif behaviour == 'dec':
+            assert latent is not None
+            pred: dict = self.auto_encoder.decode(latent, c, self.img_size)
+            return pred
+        elif behaviour == 'denoise':
+            assert latent is not None
+            pred: dict = self.denoise_model(*args, **kwargs)
+            return pred
+
+
+def eg3d_options_default():
+
+    opts = dnnlib.EasyDict(
+        dict(
+            cbase=32768,
+            cmax=512,
+            map_depth=2,
+            g_class_name='nsr.triplane.TriPlaneGenerator',  # TODO
+            g_num_fp16_res=0,
+        ))
+
+    return opts
+
+
+def rendering_options_defaults(opts):
+
+    rendering_options = {
+        # 'image_resolution': c.training_set_kwargs.resolution,
+        'image_resolution': 256,
+        'disparity_space_sampling': False,
+        'clamp_mode': 'softplus',
+        'c_gen_conditioning_zero':
+        True,  # if true, fill generator pose conditioning label with dummy zero vector
+        # 'gpc_reg_prob': opts.gpc_reg_prob if opts.gen_pose_cond else None,
+        'c_scale':
+        opts.c_scale,  # mutliplier for generator pose conditioning label
+        'superresolution_noise_mode': 'none',
+        'density_reg': opts.density_reg,  # strength of density regularization
+        'density_reg_p_dist': opts.
+        density_reg_p_dist,  # distance at which to sample perturbed points for density regularization
+        'reg_type': opts.
+        reg_type,  # for experimenting with variations on density regularization
+        'decoder_lr_mul': 1,
+        # opts.decoder_lr_mul,  # learning rate multiplier for decoder
+        'decoder_activation': 'sigmoid',
+        'sr_antialias': True,
+        'return_triplane_features': False,  # for DDF supervision
+        'return_sampling_details_flag': False,
+
+        # * shape default sr
+
+        # 'superresolution_module': 'nsr.superresolution.SuperresolutionHybrid4X',
+        # 'superresolution_module':
+        # 'torch_utils.components.PixelUnshuffleUpsample',
+        'superresolution_module': 'torch_utils.components.NearestConvSR',
+    }
+
+    if opts.cfg == 'ffhq':
+        rendering_options.update({
+            'superresolution_module':
+            'nsr.superresolution.SuperresolutionHybrid8XDC',
+            'focal': 2985.29 / 700,
+            'depth_resolution':
+            48 - 0,  # number of uniform samples to take per ray.
+            'depth_resolution_importance':
+            48 - 0,  # number of importance samples to take per ray.
+            'bg_depth_resolution':
+            16,  # 4/14 in stylenerf, https://github.com/facebookresearch/StyleNeRF/blob/7f5610a058f27fcc360c6b972181983d7df794cb/conf/model/stylenerf_ffhq.yaml#L48
+            'ray_start':
+            2.25,  # near point along each ray to start taking samples.
+            'ray_end':
+            3.3,  # far point along each ray to stop taking samples. 
+            'box_warp':
+            1,  # the side-length of the bounding box spanned by the tri-planes; box_warp=1 means [-0.5, -0.5, -0.5] -> [0.5, 0.5, 0.5].
+            'avg_camera_radius':
+            2.7,  # used only in the visualizer to specify camera orbit radius.
+            'avg_camera_pivot': [
+                0, 0, 0.2
+            ],  # used only in the visualizer to control center of camera rotation.
+            'superresolution_noise_mode': 'random',
+        })
+    elif opts.cfg == 'afhq':
+        rendering_options.update({
+            'superresolution_module':
+            'nsr.superresolution.SuperresolutionHybrid8X',
+            'superresolution_noise_mode': 'random',
+            'focal': 4.2647,
+            'depth_resolution': 48,
+            'depth_resolution_importance': 48,
+            'ray_start': 2.25,
+            'ray_end': 3.3,
+            'box_warp': 1,
+            'avg_camera_radius': 2.7,
+            'avg_camera_pivot': [0, 0, -0.06],
+        })
+    elif opts.cfg == 'shapenet':  # TODO, lies in a sphere
+        rendering_options.update({
+            'depth_resolution': 64,
+            'depth_resolution_importance': 64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start': 0.2,
+            'ray_end': 2.2,
+            # 'ray_start': opts.ray_start,
+            # 'ray_end': opts.ray_end,
+            'box_warp': 2,  # TODO, how to set this value?
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'eg3d_shapenet_aug_resolution':
+        rendering_options.update({
+            'depth_resolution': 80,
+            'depth_resolution_importance': 80,
+            'ray_start': 0.1,
+            'ray_end': 1.9,  # 2.6/1.7*1.2
+            'box_warp': 1.1,
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair':
+        rendering_options.update({
+            'depth_resolution': 96,
+            'depth_resolution_importance': 96,
+            'ray_start': 0.1,
+            'ray_end': 1.9,  # 2.6/1.7*1.2
+            'box_warp': 1.1,
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair_128':
+        rendering_options.update({
+            'depth_resolution': 128,
+            'depth_resolution_importance': 128,
+            'ray_start': 0.1,
+            'ray_end': 1.9,  # 2.6/1.7*1.2
+            'box_warp': 1.1,
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair_64':
+        rendering_options.update({
+            'depth_resolution': 64,
+            'depth_resolution_importance': 64,
+            'ray_start': 0.1,
+            'ray_end': 1.9,  # 2.6/1.7*1.2
+            'box_warp': 1.1,
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'srn_shapenet_aug_resolution_chair_128':
+        rendering_options.update({
+            'depth_resolution': 128,
+            'depth_resolution_importance': 128,
+            'ray_start': 1.25,
+            'ray_end': 2.75,
+            'box_warp': 1.5,
+            'white_back': True,
+            'avg_camera_radius': 2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair_128_residualSR':
+        rendering_options.update({
+            'depth_resolution':
+            128,
+            'depth_resolution_importance':
+            128,
+            'ray_start':
+            0.1,
+            'ray_end':
+            1.9,  # 2.6/1.7*1.2
+            'box_warp':
+            1.1,
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.2,
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR_Residual',
+        })
+
+    elif opts.cfg == 'shapenet_tuneray':  # TODO, lies in a sphere
+        rendering_options.update({
+            'depth_resolution': 64,
+            'depth_resolution_importance': 64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start': opts.ray_start,
+            'ray_end': opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution': 80,
+            'depth_resolution_importance': 80,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start': opts.ray_start,
+            'ray_end': opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution': 128,
+            'depth_resolution_importance': 128,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start': opts.ray_start,
+            'ray_end': opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_96':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution': 96,
+            'depth_resolution_importance': 96,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start': opts.ray_start,
+            'ray_end': opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+    # ! default version
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_96_nearestSR':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            96,
+            'depth_resolution_importance':
+            96,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            opts.ray_start,
+            'ray_end':
+            opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.2,
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR',
+        })
+
+    # ! 64+64, since ssdnerf adopts this setting
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_64_nearestSR':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            64,
+            'depth_resolution_importance':
+            64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            opts.ray_start,
+            'ray_end':
+            opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.2,
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR',
+        })
+
+    # ! 64+64+patch, since ssdnerf adopts this setting
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_64_nearestSR_patch':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            64,
+            'depth_resolution_importance':
+            64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            opts.ray_start,
+            'ray_end':
+            opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.2,
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR',
+            # patch configs
+            'PatchRaySampler':
+            True,
+            # 'patch_rendering_resolution': 32,
+            # 'patch_rendering_resolution': 48,
+            'patch_rendering_resolution':
+            opts.patch_rendering_resolution,
+        })
+
+    elif opts.cfg == 'objverse_tuneray_aug_resolution_64_64_nearestSR':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            64,
+            'depth_resolution_importance':
+            64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            opts.ray_start,
+            # 'auto',
+            'ray_end':
+            opts.ray_end,
+            # 'auto',
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            # 2,
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.946,  # ?
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR',
+            # patch configs
+            # 'PatchRaySampler': False,
+            # 'patch_rendering_resolution': 32,
+            # 'patch_rendering_resolution': 48,
+            # 'patch_rendering_resolution': opts.patch_rendering_resolution,
+        })
+
+    elif opts.cfg == 'objverse_tuneray_aug_resolution_64_64_auto':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            64,
+            'depth_resolution_importance':
+            64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            'auto',
+            'ray_end':
+            'auto',
+            'box_warp':
+            0.9,
+            'white_back':
+            True,
+            'radius_range': [1.5, 2],
+            # 'z_near': 1.5-0.45, # radius in [1.5, 2], https://github.com/modelscope/richdreamer/issues/12#issuecomment-1897734616
+            # 'z_far': 2.0+0.45,
+            'sampler_bbox_min':
+            -0.45,
+            'sampler_bbox_max':
+            0.45,
+            # 'avg_camera_pivot': [0, 0, 0], # not used
+            'filter_out_of_bbox':
+            True,
+            # 'superresolution_module':
+            # 'torch_utils.components.NearestConvSR',
+            # patch configs
+            'PatchRaySampler':
+            True,
+            # 'patch_rendering_resolution': 32,
+            # 'patch_rendering_resolution': 48,
+            'patch_rendering_resolution':
+            opts.patch_rendering_resolution,
+        })
+        rendering_options['z_near'] = rendering_options['radius_range'][
+            0] + rendering_options['sampler_bbox_min']
+        rendering_options['z_far'] = rendering_options['radius_range'][
+            1] + rendering_options['sampler_bbox_max']
+
+    elif opts.cfg == 'objverse_tuneray_aug_resolution_56_56_auto':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            56,
+            'depth_resolution_importance':
+            56,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            'auto',
+            'ray_end':
+            'auto',
+            'box_warp':
+            0.9,
+            'white_back':
+            True,
+            'radius_range': [1.5, 2],
+            # 'z_near': 1.5-0.45, # radius in [1.5, 2], https://github.com/modelscope/richdreamer/issues/12#issuecomment-1897734616
+            # 'z_far': 2.0+0.45,
+            'sampler_bbox_min':
+            -0.45,
+            'sampler_bbox_max':
+            0.45,
+            # 'avg_camera_pivot': [0, 0, 0], # not used
+            'filter_out_of_bbox':
+            True,
+            # 'superresolution_module':
+            # 'torch_utils.components.NearestConvSR',
+            # patch configs
+            'PatchRaySampler':
+            True,
+            # 'patch_rendering_resolution': 32,
+            # 'patch_rendering_resolution': 48,
+            'patch_rendering_resolution':
+            opts.patch_rendering_resolution,
+        })
+        rendering_options['z_near'] = rendering_options['radius_range'][
+            0] + rendering_options['sampler_bbox_min']
+        rendering_options['z_far'] = rendering_options['radius_range'][
+            1] + rendering_options['sampler_bbox_max']
+
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_96_nearestResidualSR':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            96,
+            'depth_resolution_importance':
+            96,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            opts.ray_start,
+            'ray_end':
+            opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.2,
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR_Residual',
+        })
+
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_64_nearestResidualSR':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution':
+            64,
+            'depth_resolution_importance':
+            64,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start':
+            opts.ray_start,
+            'ray_end':
+            opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back':
+            True,
+            'avg_camera_radius':
+            1.2,
+            'avg_camera_pivot': [0, 0, 0],
+            'superresolution_module':
+            'torch_utils.components.NearestConvSR_Residual',
+        })
+
+    elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_104':  # to differentiate hwc
+        rendering_options.update({
+            'depth_resolution': 104,
+            'depth_resolution_importance': 104,
+            # * radius 1.2 setting, newly rendered images
+            'ray_start': opts.ray_start,
+            'ray_end': opts.ray_end,
+            'box_warp':
+            opts.ray_end - opts.ray_start,  # TODO, how to set this value?
+            'white_back': True,
+            'avg_camera_radius': 1.2,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+
+    rendering_options.update({'return_sampling_details_flag': True})
+    rendering_options.update({'return_sampling_details_flag': True})
+
+    return rendering_options
+
+
+def model_encoder_defaults():
+
+    return dict(
+        use_clip=False,
+        arch_encoder="vits",
+        arch_decoder="vits",
+        load_pretrain_encoder=False,
+        encoder_lr=1e-5,
+        encoder_weight_decay=
+        0.001,  # https://github.com/google-research/vision_transformer
+        no_dim_up_mlp=False,
+        dim_up_mlp_as_func=False,
+        decoder_load_pretrained=True,
+        uvit_skip_encoder=False,
+        # vae ldm
+        vae_p=1,
+        ldm_z_channels=4,
+        ldm_embed_dim=4,
+        use_conf_map=False,
+        # sd E, lite version by default
+        sd_E_ch=64,
+        z_channels=3 * 4,
+        latent_num=768,
+        sd_E_num_res_blocks=1,
+        num_frames=4,
+        # vit_decoder
+        arch_dit_decoder='DiT2-B/2',
+        return_all_dit_layers=False,
+        # sd D
+        # sd_D_ch=32,
+        # sd_D_res_blocks=1,
+        # sd_D_res_blocks=1,
+        lrm_decoder=False,
+        gs_rendering=False,
+        surfel_rendering=False,
+        plane_n=3,
+        in_plane_attention=True,
+        vae_dit_token_size=16,
+    )
+
+
+def triplane_decoder_defaults():
+    opts = dict(
+        triplane_fg_bg=False,flexicube_decoder=False,
+        cfg='shapenet',
+        density_reg=0.25,
+        density_reg_p_dist=0.004,
+        reg_type='l1',
+        triplane_decoder_lr=0.0025,  # follow eg3d G lr
+        super_resolution_lr=0.0025,
+        # triplane_decoder_wd=0.1,
+        c_scale=1,
+        nsr_lr=0.02,
+        triplane_size=224,
+        decoder_in_chans=32,
+        triplane_in_chans=-1,
+        decoder_output_dim=3,
+        out_chans=96,
+        c_dim=25,  # Conditioning label (C) dimensionality.
+        # ray_start=0.2,
+        # ray_end=2.2,
+        ray_start=0.6,  # shapenet default
+        ray_end=1.8,
+        rendering_kwargs={},
+        sr_training=False,
+        bcg_synthesis=False,  # from panohead
+        bcg_synthesis_kwargs={},  # G_kwargs.copy()
+        #
+        image_size=128,  # raw 3D rendering output resolution.
+        patch_rendering_resolution=45,
+    )
+
+    # else:
+    #     assert False, "Need to specify config"
+
+    # opts = dict(opts)
+    # opts.pop('cfg')
+
+    return opts
+
+
+def vit_decoder_defaults():
+    res = dict(
+        vit_decoder_lr=1e-5,  # follow eg3d G lr
+        vit_decoder_wd=0.001,
+    )
+    return res
+
+
+def nsr_decoder_defaults():
+    res = {
+        'decomposed': False,
+    }  # TODO, add defaults for all nsr
+    res.update(triplane_decoder_defaults())  # triplane by default now
+    res.update(vit_decoder_defaults())  # type: ignore
+    return res
+
+
+def loss_defaults():
+    opt = dict(
+        color_criterion='mse',
+        l2_lambda=1.0,
+        lpips_lambda=0.,
+        lpips_delay_iter=0,
+        sr_delay_iter=0,
+        # kl_anneal=0,
+        kl_anneal=False,
+        latent_lambda=0.,
+        latent_criterion='mse',
+        kl_lambda=0.0,
+        pt_ft_kl=False,
+        ft_kl=False,
+        # pt_kl_lambda=1e-8,
+        # kl_anneal=False,
+        ssim_lambda=0.,
+        l1_lambda=0.,
+        id_lambda=0.0,
+        depth_lambda=0.0,  # TODO
+        alpha_lambda=0.0,  # TODO
+        fg_mse=False,
+        bg_lamdba=0.0,
+        density_reg=0.0,  # tvloss in eg3d
+        density_reg_p_dist=0.004,  # 'density regularization strength.'
+        density_reg_every=4,  # lazy density reg
+
+        # 3D supervision, ffhq/afhq eg3d warm up
+        shape_uniform_lambda=0.005,
+        shape_importance_lambda=0.01,
+        shape_depth_lambda=0.,
+        xyz_lambda=0.0,
+        emd_lambda=0.0,
+        cd_lambda=0.0,
+        pruning_ot_lambda=0.0,
+        # 2dgs
+        lambda_normal=0.0,
+        lambda_dist=0.0,
+
+        #gghead reg
+        lambda_scale_reg=0.0,
+        lambda_opa_reg=0.0,
+
+        # gan loss
+        rec_cvD_lambda=0.01,
+        nvs_cvD_lambda=0.025,
+        patchgan_disc_factor=0.01,
+        patchgan_disc_g_weight=0.2,  # 
+        r1_gamma=1.0,  # ffhq default value for eg3d
+        sds_lamdba=1.0,
+        nvs_D_lr_mul=1,  # compared with 1e-4
+        cano_D_lr_mul=1,  # compared with 1e-4
+
+        # lsgm loss
+        ce_balanced_kl=1.,
+        p_eps_lambda=1,
+        # symmetric loss
+        symmetry_loss=False,
+        depth_smoothness_lambda=0.0,
+        ce_lambda=1.0,
+        negative_entropy_lambda=1.0,
+        grad_clip=False,
+        online_mask=False,  # in unsup3d
+        fps_sampling=False,  # for emd loss
+        subset_fps_sampling=False,  # for emd loss
+        subset_half_fps_sampling=False,
+        # gaussian loss
+        commitment_loss_lambda=0.0,
+        rand_aug_bg=False,
+    )
+    return opt
+
+
+def dataset_defaults():
+    res = dict(
+        use_lmdb=False,
+        dataset_size=-1,
+        use_wds=False,
+        use_lmdb_compressed=True,
+        compile=False,
+        interval=1,
+        objv_dataset=False,
+        decode_encode_img_only=False,
+        load_wds_diff=False,
+        load_wds_latent=False,
+        eval_load_wds_instance=True,
+        shards_lst="",
+        eval_shards_lst="",
+        mv_input=False,
+        duplicate_sample=True,
+        orthog_duplicate=False,
+        split_chunk_input=False,  # split=8 per chunk
+        load_real=False,
+        load_mv_real=False,
+        load_gso=False,
+        four_view_for_latent=False,
+        single_view_for_i23d=False,
+        shuffle_across_cls=False,
+        load_extra_36_view=False,
+        mv_latent_dir='',
+        append_depth=False,
+        append_xyz=False,
+        read_normal=False,
+        plucker_embedding=False,
+        perturb_pcd_scale=0.0,
+        gs_cam_format=False,
+        frame_0_as_canonical=
+        False,  # transform the first pose to a fixed position
+        pcd_path=None,
+        stage_1_output_dir='',
+        load_pcd=False,
+        use_chunk=False, # jpeg chunk
+        split_chunk_size=8,
+        load_caption_dataset=False,
+        load_mv_dataset=False,
+        export_mesh=False,
+    )
+    return res
+
+
+def encoder_and_nsr_defaults():
+    """
+    Defaults for image training.
+    """
+    # ViT configs
+    res = dict(
+        dino_version='v1',
+        encoder_in_channels=3,
+        img_size=[224],
+        patch_size=16,  # ViT-S/16
+        in_chans=384,
+        num_classes=0,
+        embed_dim=384,  # Check ViT encoder dim
+        depth=6,
+        num_heads=16,
+        mlp_ratio=4.,
+        qkv_bias=False,
+        qk_scale=None,
+        drop_rate=0.1,
+        attn_drop_rate=0.,
+        drop_path_rate=0.,
+        norm_layer='nn.LayerNorm',
+        # img_resolution=128,  # Output resolution.
+        cls_token=False,
+        # image_size=128,  # rendered output resolution.
+        # img_channels=3,  # Number of output color channels.
+        encoder_cls_token=False,
+        decoder_cls_token=False,
+        sr_kwargs={},
+        sr_ratio=2,
+        # sd configs
+    )
+    # Triplane configs
+    res.update(model_encoder_defaults())
+    res.update(nsr_decoder_defaults())
+    res.update(
+        ae_classname='vit.vit_triplane.ViTTriplaneDecomposed')  # if add SR
+    return res
+
+
+def create_3DAE_model(
+        arch_encoder,
+        arch_decoder,
+        dino_version='v1',
+        img_size=[224],
+        patch_size=16,
+        in_chans=384,
+        num_classes=0,
+        embed_dim=1024,  # Check ViT encoder dim
+        depth=6,
+        num_heads=16,
+        mlp_ratio=4.,
+        qkv_bias=False,
+        qk_scale=None,
+        drop_rate=0.1,
+        attn_drop_rate=0.,
+        drop_path_rate=0.,
+        # norm_layer=nn.LayerNorm,
+        norm_layer='nn.LayerNorm',
+        out_chans=96,
+        decoder_in_chans=32,
+        triplane_in_chans=-1,
+        decoder_output_dim=32,
+        encoder_cls_token=False,
+        decoder_cls_token=False,
+        c_dim=25,  # Conditioning label (C) dimensionality.
+        image_size=128,  # Output resolution.
+        img_channels=3,  # Number of output color channels.
+        rendering_kwargs={},
+        load_pretrain_encoder=False,
+        decomposed=True,
+        triplane_size=224,
+        ae_classname='ViTTriplaneDecomposed',
+        use_clip=False,
+        sr_kwargs={},
+        sr_ratio=2,
+        no_dim_up_mlp=False,
+        dim_up_mlp_as_func=False,
+        decoder_load_pretrained=True,
+        uvit_skip_encoder=False,
+        bcg_synthesis_kwargs={},
+        # decoder params
+        vae_p=1,
+        ldm_z_channels=4,
+        vae_dit_token_size=16,
+        ldm_embed_dim=4,
+        use_conf_map=False,
+        triplane_fg_bg=False,
+        flexicube_decoder=False,
+        encoder_in_channels=3,
+        sd_E_ch=64,
+        z_channels=3 * 4,
+        latent_num=768,
+        sd_E_num_res_blocks=1,
+        num_frames=4,
+        arch_dit_decoder='DiT2-B/2',
+        in_plane_attention=True,
+        lrm_decoder=False,
+        gs_rendering=False,
+        surfel_rendering=False,
+        return_all_dit_layers=False,
+        plane_n=3,
+        *args,
+        **kwargs):
+
+    # TODO, check pre-trained ViT encoder cfgs
+
+    preprocess = None
+    clip_dtype = None
+    if load_pretrain_encoder:
+        if not use_clip:
+            if dino_version == 'v1':
+                encoder = torch.hub.load(
+                    'facebookresearch/dino:main',
+                    'dino_{}{}'.format(arch_encoder, patch_size))
+                logger.log(
+                    f'loaded pre-trained dino v1 ViT-S{patch_size} encoder ckpt'
+                )
+            elif dino_version == 'v2':
+                encoder = torch.hub.load(
+                    'facebookresearch/dinov2',
+                    'dinov2_{}{}'.format(arch_encoder, patch_size))
+                logger.log(
+                    f'loaded pre-trained dino v2 {arch_encoder}{patch_size} encoder ckpt'
+                )
+            elif 'sd' in dino_version:  # just for compat
+
+                if 'mv' in dino_version:
+                    if 'lgm' in dino_version:
+                        encoder_cls = MVUNet(
+                            input_size=256,
+                            up_channels=(1024, 1024, 512, 256,
+                                         128),  # one more decoder
+                            up_attention=(True, True, True, False, False),
+                            splat_size=128,
+                            output_size=
+                            512,  # render & supervise Gaussians at a higher resolution.
+                            batch_size=8,
+                            num_views=8,
+                            gradient_accumulation_steps=1,
+                            # mixed_precision='bf16',
+                        )
+                    elif 'gs' in dino_version:
+                        encoder_cls = MVEncoder
+                    else:
+                        encoder_cls = MVEncoder
+
+                else:
+                    encoder_cls = Encoder
+
+                encoder = encoder_cls(  # mono input
+                    double_z=True,
+                    resolution=256,
+                    in_channels=encoder_in_channels,
+                    # ch=128,
+                    ch=64,  # ! fit in the memory
+                    # ch_mult=[1,2,4,4],
+                    # num_res_blocks=2,
+                    ch_mult=[1, 2, 4, 4],
+                    num_res_blocks=1,
+                    dropout=0.0,
+                    attn_resolutions=[],
+                    out_ch=3,  # unused
+                    z_channels=z_channels,
+                )  # stable diffusion encoder
+            else:
+                raise NotImplementedError()
+
+        else:
+            import clip
+            model, preprocess = clip.load("ViT-B/16", device=dist_util.dev())
+            model.float()  # convert weight to float32
+            clip_dtype = model.dtype
+            encoder = getattr(
+                model, 'visual')  # only use the CLIP visual encoder here
+            encoder.requires_grad_(False)
+            logger.log(
+                f'loaded pre-trained CLIP ViT-B{patch_size} encoder, fixed.')
+
+    elif 'sd' in dino_version:
+        attn_kwargs = {}
+        attn_type = "mv-vanilla"
+        if 'mv' in dino_version:
+            if 'lgm' in dino_version:
+                encoder = LGM_MVEncoder(
+                    in_channels=9,
+                    # input_size=256,
+                    up_channels=(1024, 1024, 512, 256,
+                                 128),  # one more decoder
+                    up_attention=(True, True, True, False, False),
+                    # splat_size=128,
+                    # output_size=
+                    # 512,  # render & supervise Gaussians at a higher resolution.
+                    # batch_size=8,
+                    # num_views=8,
+                    # gradient_accumulation_steps=1,
+                    # mixed_precision='bf16',
+                )
+
+            elif 'srt' in dino_version:
+                if 'hybrid' in dino_version:
+                    encoder_cls = HybridEncoder # best overall performance
+                # if 'vitl' in dino_version:
+                #     encoder_cls = ImprovedSRTEncoderVAE_L5_vitl
+                # elif 'l6' in dino_version:
+                #     encoder_cls = ImprovedSRTEncoderVAE_L6
+                # elif 'mlp4' in dino_version:
+                #     if 'f8'in dino_version:
+                #         # encoder_cls = ImprovedSRTEncoderVAE_mlp_ratio4_f8
+                #         encoder_cls = ImprovedSRTEncoderVAE_mlp_ratio4_f8_L6
+                elif 'l6' in dino_version:
+                    encoder_cls = ImprovedSRTEncoderVAE_mlp_ratio4_L6
+                elif 'heavy' in dino_version:
+                    encoder_cls = ImprovedSRTEncoderVAE_mlp_ratio4_heavyPatchify
+
+                elif 'decomposed' in dino_version:
+                    encoder_cls = ImprovedSRTEncoderVAE_mlp_ratio4_decomposed
+                elif 'pcd-structured' in dino_version:
+                    attn_kwargs = {
+                        'n_heads': 8,
+                        'd_head': 64,
+                    }
+                    if 'pc2' in dino_version:
+                        encoder_cls = HybridEncoderPCDStructuredLatentSNoPCD_PC2 # pixel-aligned by rasterization projection
+                    elif 'nopcd' in dino_version:
+                        encoder_cls = HybridEncoderPCDStructuredLatentSNoPCD
+                    elif 'uniformfps' in dino_version:
+                        encoder_cls = HybridEncoderPCDStructuredLatentUniformFPS
+                    elif 'pixelaligned' in dino_version:
+                        encoder_cls = HybridEncoderPCDStructuredLatentSNoPCD_PixelAlignedQuery
+                    else:
+                        encoder_cls = HybridEncoderPCDStructuredLatent
+                else:
+                    encoder_cls = ImprovedSRTEncoderVAE_mlp_ratio4 # best overall performance
+                # else:  # default version
+                #     encoder_cls = ImprovedSRTEncoderVAE
+
+            elif 'gs' in dino_version:
+
+                if 'dynaInp' in dino_version:
+                    if 'ca' in dino_version:
+                        encoder_cls = MVEncoderGSDynamicInp_CA
+                    else:
+                        encoder_cls = MVEncoderGSDynamicInp
+                else:
+                    encoder_cls = MVEncoderGS
+
+                attn_kwargs = {
+                    'n_heads': 8,
+                    'd_head': 64,
+                }
+
+            else:
+                if 'dynaInp' in dino_version:
+                    if 'ca' in dino_version:
+                        encoder_cls = MVEncoderGSDynamicInp_CA
+                    else:
+                        encoder_cls = MVEncoderGSDynamicInp
+                else:
+                    encoder_cls = MVEncoder
+                attn_kwargs = {
+                    'n_heads': 8,
+                    'd_head': 64,
+                }
+
+        else:
+            encoder_cls = Encoder
+
+        if 'lgm' not in dino_version:  # TODO, for compat now
+            # st()
+            encoder = encoder_cls(
+                double_z=True,
+                resolution=256,
+                in_channels=encoder_in_channels,
+                # ch=128,
+                # ch=64, # ! fit in the memory
+                ch=sd_E_ch,
+                # ch_mult=[1,2,4,4],
+                # num_res_blocks=2,
+                ch_mult=[1, 2, 4, 4],
+                # num_res_blocks=1,
+                num_res_blocks=sd_E_num_res_blocks,
+                num_frames=num_frames,
+                dropout=0.0,
+                attn_resolutions=[],
+                out_ch=3,  # unused
+                z_channels=z_channels,  # 4 * 3
+                attn_kwargs=attn_kwargs,
+                attn_type=attn_type,
+                latent_num=latent_num,
+            )  # stable diffusion encoder
+
+    else:
+        encoder = vits.__dict__[arch_encoder](
+            patch_size=patch_size,
+            drop_path_rate=drop_path_rate,  # stochastic depth
+            img_size=img_size)
+
+    assert decomposed
+    if decomposed:
+
+        if not gs_rendering:
+            if triplane_in_chans == -1:
+                triplane_in_chans = decoder_in_chans
+
+            if triplane_fg_bg:
+                triplane_renderer_cls = Triplane_fg_bg_plane
+            elif flexicube_decoder:
+                triplane_renderer_cls = Triplane
+            else:
+                triplane_renderer_cls = Triplane
+
+            # triplane_decoder = Triplane(
+            triplane_decoder = triplane_renderer_cls(
+                c_dim,  # Conditioning label (C) dimensionality.
+                image_size,  # Output resolution.
+                img_channels,  # Number of output color channels.
+                rendering_kwargs=rendering_kwargs,
+                out_chans=out_chans,
+                # create_triplane=True,  # compatability, remove later
+                triplane_size=triplane_size,
+                decoder_in_chans=triplane_in_chans,
+                decoder_output_dim=decoder_output_dim,
+                sr_kwargs=sr_kwargs,
+                bcg_synthesis_kwargs=bcg_synthesis_kwargs,
+                lrm_decoder=lrm_decoder)
+        elif surfel_rendering:
+            triplane_decoder = GaussianRenderer2DGS(
+                image_size, out_chans, rendering_kwargs=rendering_kwargs)
+        else:
+            triplane_decoder = GaussianRenderer(
+                image_size, out_chans, rendering_kwargs=rendering_kwargs)
+
+        if load_pretrain_encoder:
+
+            if dino_version == 'v1':
+                vit_decoder = torch.hub.load(
+                    'facebookresearch/dino:main',
+                    'dino_{}{}'.format(arch_decoder, patch_size))
+                logger.log(
+                    'loaded pre-trained decoder',
+                    "facebookresearch/dino:main', 'dino_{}{}".format(
+                        arch_decoder, patch_size))
+            else:
+
+                vit_decoder = torch.hub.load(
+                    'facebookresearch/dinov2',
+                    # 'dinov2_{}{}'.format(arch_decoder, patch_size))
+                    'dinov2_{}{}'.format(arch_decoder, patch_size),
+                    pretrained=decoder_load_pretrained)
+                logger.log(
+                    'loaded pre-trained decoder',
+                    "facebookresearch/dinov2', 'dinov2_{}{}".format(
+                        arch_decoder,
+                        patch_size), 'pretrianed=', decoder_load_pretrained)
+
+        elif 'dit' in dino_version:
+            from dit.dit_decoder import DiT2_models, DiTBlock
+
+            # st()
+            vit_decoder = DiT2_models[arch_dit_decoder](
+                input_size=16,
+                num_classes=0,
+                learn_sigma=False,
+                in_channels=embed_dim,
+                mixed_prediction=False,
+                context_dim=None,  # add CLIP text embedding
+                roll_out=True,
+                plane_n=4 if ('gs' in dino_version
+                              and 'trilatent' not in dino_version) else 3,
+                return_all_layers=return_all_dit_layers,
+                in_plane_attention=in_plane_attention,
+                vit_blk=DiTBlock,
+            )
+
+        else:  # has bug on global token, to fix
+            vit_decoder = vits.__dict__[arch_decoder](
+                patch_size=patch_size,
+                drop_path_rate=drop_path_rate,  # stochastic depth
+                img_size=img_size)
+
+        # decoder = ViTTriplaneDecomposed(vit_decoder, triplane_decoder)
+        # if True:
+        decoder_kwargs = dict(
+            class_name=ae_classname,
+            vit_decoder=vit_decoder,
+            triplane_decoder=triplane_decoder,
+            # encoder_cls_token=encoder_cls_token,
+            cls_token=decoder_cls_token,
+            sr_ratio=sr_ratio,
+            vae_p=vae_p,
+            ldm_z_channels=ldm_z_channels,
+            ldm_embed_dim=ldm_embed_dim,
+            vae_dit_token_size=vae_dit_token_size,
+            plane_n=plane_n,
+        )
+        decoder = dnnlib.util.construct_class_by_name(**decoder_kwargs)
+    else:
+        # deprecated
+        decoder = ViTTriplane(
+            img_size,
+            patch_size,
+            in_chans,
+            num_classes,
+            embed_dim,
+            depth,
+            num_heads,
+            mlp_ratio,
+            qkv_bias,
+            qk_scale,
+            drop_rate,
+            attn_drop_rate,
+            drop_path_rate,
+            norm_layer,
+            out_chans,
+            cls_token,
+            c_dim,  # Conditioning label (C) dimensionality.
+            image_size,  # Output resolution.
+            img_channels,  # Number of output color channels.
+            # TODO, replace with c
+            rendering_kwargs=rendering_kwargs,
+        )
+    # if return_encoder_decoder:
+    #     return encoder, decoder, img_size[0], cls_token
+    # else:
+
+    if use_conf_map:
+        confnet = ConfNet(cin=3, cout=1, nf=64, zdim=128)
+    else:
+        confnet = None
+
+    auto_encoder = AE(
+        encoder,
+        decoder,
+        img_size[0],
+        encoder_cls_token,
+        decoder_cls_token,
+        preprocess,
+        use_clip,
+        dino_version,
+        clip_dtype,
+        no_dim_up_mlp=no_dim_up_mlp,
+        dim_up_mlp_as_func=dim_up_mlp_as_func,
+        uvit_skip_encoder=uvit_skip_encoder,
+        confnet=confnet,
+    )
+
+    logger.log(auto_encoder)
+    torch.cuda.empty_cache()
+
+    return auto_encoder
+
+
+# def create_3DAE_Diffusion_model(
+#         arch_encoder,
+#         arch_decoder,
+#         img_size=[224],
+#         patch_size=16,
+#         in_chans=384,
+#         num_classes=0,
+#         embed_dim=1024,  # Check ViT encoder dim
+#         depth=6,
+#         num_heads=16,
+#         mlp_ratio=4.,
+#         qkv_bias=False,
+#         qk_scale=None,
+#         drop_rate=0.1,
+#         attn_drop_rate=0.,
+#         drop_path_rate=0.,
+#         # norm_layer=nn.LayerNorm,
+#         norm_layer='nn.LayerNorm',
+#         out_chans=96,
+#         decoder_in_chans=32,
+#         decoder_output_dim=32,
+#         cls_token=False,
+#         c_dim=25,  # Conditioning label (C) dimensionality.
+#         img_resolution=128,  # Output resolution.
+#         img_channels=3,  # Number of output color channels.
+#         rendering_kwargs={},
+#         load_pretrain_encoder=False,
+#         decomposed=True,
+#         triplane_size=224,
+#         ae_classname='ViTTriplaneDecomposed',
+#         # return_encoder_decoder=False,
+#         *args,
+#         **kwargs
+#         ):
+
+#     # TODO, check pre-trained ViT encoder cfgs
+
+#     encoder, decoder, img_size, cls_token = create_3DAE_model(
+#         arch_encoder,
+#         arch_decoder,
+#         img_size,
+#         patch_size,
+#         in_chans,
+#         num_classes,
+#         embed_dim,  # Check ViT encoder dim
+#         depth,
+#         num_heads,
+#         mlp_ratio,
+#         qkv_bias,
+#         qk_scale,
+#         drop_rate,
+#         attn_drop_rate,
+#         drop_path_rate,
+#         # norm_layer=nn.LayerNorm,
+#         norm_layer,
+#         out_chans=96,
+#         decoder_in_chans=32,
+#         decoder_output_dim=32,
+#         cls_token=False,
+#         c_dim=25,  # Conditioning label (C) dimensionality.
+#         img_resolution=128,  # Output resolution.
+#         img_channels=3,  # Number of output color channels.
+#         rendering_kwargs={},
+#         load_pretrain_encoder=False,
+#         decomposed=True,
+#         triplane_size=224,
+#         ae_classname='ViTTriplaneDecomposed',
+#         return_encoder_decoder=False,
+#         *args,
+#         **kwargs
+#     ) # type: ignore
+
+
+def create_Triplane(
+        c_dim=25,  # Conditioning label (C) dimensionality.
+        img_resolution=128,  # Output resolution.
+        img_channels=3,  # Number of output color channels.
+        rendering_kwargs={},
+        decoder_output_dim=32,
+        *args,
+        **kwargs):
+
+    decoder = Triplane(
+        c_dim,  # Conditioning label (C) dimensionality.
+        img_resolution,  # Output resolution.
+        img_channels,  # Number of output color channels.
+        # TODO, replace with c
+        rendering_kwargs=rendering_kwargs,
+        create_triplane=True,
+        decoder_output_dim=decoder_output_dim)
+    return decoder
+
+
+def DiT_defaults():
+    return {
+        'dit_model': "DiT-B/16",
+        'vae': "ema"
+        # dit_model="DiT-XL/2",
+        # dit_patch_size=8,
+    }
diff --git a/nsr/srt/__init__.py b/nsr/srt/__init__.py
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diff --git a/nsr/srt/encoder.py b/nsr/srt/encoder.py
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--- /dev/null
+++ b/nsr/srt/encoder.py
@@ -0,0 +1,936 @@
+from calendar import c
+import imageio
+import torchvision
+import random
+# import einops
+import kornia
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+from .layers import RayEncoder, Transformer, PreNorm
+from pdb import set_trace as st
+
+from pathlib import Path
+import math
+from ldm.modules.attention import MemoryEfficientCrossAttention
+from timm.models.vision_transformer import PatchEmbed
+from ldm.modules.diffusionmodules.model import Encoder
+from guided_diffusion import dist_util, logger
+import point_cloud_utils as pcu
+
+import pytorch3d.ops
+from pytorch3d.ops.utils import masked_gather
+from pytorch3d.implicitron.dataset.data_loader_map_provider import FrameData
+from pytorch3d.renderer import PointsRasterizationSettings, PointsRasterizer
+from pytorch3d.renderer.cameras import CamerasBase, PerspectiveCameras
+from pytorch3d.structures import Pointclouds
+
+from timm.models.vision_transformer import PatchEmbed, Mlp
+
+from vit.vit_triplane import XYZPosEmbed
+
+from utils.geometry import index, perspective
+
+
+def approx_gelu():
+    return nn.GELU(approximate="tanh")
+
+
+class SRTConvBlock(nn.Module):
+
+    def __init__(self, idim, hdim=None, odim=None):
+        super().__init__()
+        if hdim is None:
+            hdim = idim
+
+        if odim is None:
+            odim = 2 * hdim
+
+        conv_kwargs = {'bias': False, 'kernel_size': 3, 'padding': 1}
+        self.layers = nn.Sequential(
+            nn.Conv2d(idim, hdim, stride=1, **conv_kwargs), nn.ReLU(),
+            nn.Conv2d(hdim, odim, stride=2, **conv_kwargs), nn.ReLU())
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class SRTEncoder(nn.Module):
+    """ Scene Representation Transformer Encoder, as presented in the SRT paper at CVPR 2022 (caveats below)"""
+
+    def __init__(self,
+                 num_conv_blocks=4,
+                 num_att_blocks=10,
+                 pos_start_octave=0,
+                 scale_embeddings=False):
+        super().__init__()
+        self.ray_encoder = RayEncoder(pos_octaves=15,
+                                      pos_start_octave=pos_start_octave,
+                                      ray_octaves=15)
+
+        conv_blocks = [SRTConvBlock(idim=183, hdim=96)]
+        cur_hdim = 192
+        for i in range(1, num_conv_blocks):
+            conv_blocks.append(SRTConvBlock(idim=cur_hdim, odim=None))
+            cur_hdim *= 2
+
+        self.conv_blocks = nn.Sequential(*conv_blocks)
+
+        self.per_patch_linear = nn.Conv2d(cur_hdim, 768, kernel_size=1)
+
+        # Original SRT initializes with stddev=1/math.sqrt(d).
+        # But model initialization likely also differs between torch & jax, and this worked, so, eh.
+        embedding_stdev = (1. / math.sqrt(768)) if scale_embeddings else 1.
+        self.pixel_embedding = nn.Parameter(
+            torch.randn(1, 768, 15, 20) * embedding_stdev)
+        self.canonical_camera_embedding = nn.Parameter(
+            torch.randn(1, 1, 768) * embedding_stdev)
+        self.non_canonical_camera_embedding = nn.Parameter(
+            torch.randn(1, 1, 768) * embedding_stdev)
+
+        # SRT as in the CVPR paper does not use actual self attention, but a special type:
+        # the current features in the Nth layer don't self-attend, but they
+        # always attend into the initial patch embedding (i.e., the output of
+        # the CNN). SRT further used post-normalization rather than
+        # pre-normalization.  Since then though, in OSRT, pre-norm and regular
+        # self-attention was found to perform better overall.  So that's what
+        # we do here, though it may be less stable under some circumstances.
+        self.transformer = Transformer(768,
+                                       depth=num_att_blocks,
+                                       heads=12,
+                                       dim_head=64,
+                                       mlp_dim=1536,
+                                       selfatt=True)
+
+    def forward(self, images, camera_pos, rays):
+        """
+        Args:
+            images: [batch_size, num_images, 3, height, width].
+                Assume the first image is canonical - shuffling happens in the data loader.
+            camera_pos: [batch_size, num_images, 3]
+            rays: [batch_size, num_images, height, width, 3]
+        Returns:
+            scene representation: [batch_size, num_patches, channels_per_patch]
+        """
+
+        batch_size, num_images = images.shape[:2]
+
+        x = images.flatten(0, 1)
+        camera_pos = camera_pos.flatten(0, 1)
+        rays = rays.flatten(0, 1)
+
+        canonical_idxs = torch.zeros(batch_size, num_images)
+        canonical_idxs[:, 0] = 1
+        canonical_idxs = canonical_idxs.flatten(
+            0, 1).unsqueeze(-1).unsqueeze(-1).to(x)
+        camera_id_embedding = canonical_idxs * self.canonical_camera_embedding + \
+                (1. - canonical_idxs) * self.non_canonical_camera_embedding
+
+        ray_enc = self.ray_encoder(camera_pos, rays)
+        x = torch.cat((x, ray_enc), 1)
+        x = self.conv_blocks(x)
+        x = self.per_patch_linear(x)
+        height, width = x.shape[2:]
+        x = x + self.pixel_embedding[:, :, :height, :width]
+        x = x.flatten(2, 3).permute(0, 2, 1)
+        x = x + camera_id_embedding
+
+        patches_per_image, channels_per_patch = x.shape[1:]
+        x = x.reshape(batch_size, num_images * patches_per_image,
+                      channels_per_patch)
+
+        x = self.transformer(x)
+
+        return x
+
+
+class ImprovedSRTEncoder(nn.Module):
+    """
+    Scene Representation Transformer Encoder with the improvements from Appendix A.4 in the OSRT paper.
+    """
+
+    def __init__(self,
+                 num_conv_blocks=3,
+                 num_att_blocks=5,
+                 pos_start_octave=0):
+        super().__init__()
+        self.ray_encoder = RayEncoder(pos_octaves=15,
+                                      pos_start_octave=pos_start_octave,
+                                      ray_octaves=15)
+
+        conv_blocks = [SRTConvBlock(idim=183, hdim=96)]
+        cur_hdim = 192
+        for i in range(1, num_conv_blocks):
+            conv_blocks.append(SRTConvBlock(idim=cur_hdim, odim=None))
+            cur_hdim *= 2
+
+        self.conv_blocks = nn.Sequential(*conv_blocks)
+
+        self.per_patch_linear = nn.Conv2d(cur_hdim, 768, kernel_size=1)
+
+        self.transformer = Transformer(768,
+                                       depth=num_att_blocks,
+                                       heads=12,
+                                       dim_head=64,
+                                       mlp_dim=1536,
+                                       selfatt=True)
+
+    def forward(self, images, camera_pos, rays):
+        """
+        Args:
+            images: [batch_size, num_images, 3, height, width]. Assume the first image is canonical.
+            camera_pos: [batch_size, num_images, 3]
+            rays: [batch_size, num_images, height, width, 3]
+        Returns:
+            scene representation: [batch_size, num_patches, channels_per_patch]
+        """
+
+        batch_size, num_images = images.shape[:2]
+
+        x = images.flatten(0, 1)
+        camera_pos = camera_pos.flatten(0, 1)
+        rays = rays.flatten(0, 1)
+
+        ray_enc = self.ray_encoder(camera_pos, rays)
+        x = torch.cat((x, ray_enc), 1)
+        x = self.conv_blocks(x)
+        x = self.per_patch_linear(x)
+        x = x.flatten(2, 3).permute(0, 2, 1)
+
+        patches_per_image, channels_per_patch = x.shape[1:]
+        x = x.reshape(batch_size, num_images * patches_per_image,
+                      channels_per_patch)
+
+        x = self.transformer(x)
+
+        return x
+
+
+class ImprovedSRTEncoderVAE(nn.Module):
+    """
+    Modified from ImprovedSRTEncoder
+    1. replace conv_blocks to timm embedder 
+    2. replace ray_PE with Plucker coordinate
+    3. add xformers/flash for transformer attention
+    """
+
+    def __init__(
+            self,
+            *,
+            ch,
+            out_ch,
+            ch_mult=(1, 2, 4, 8),
+            num_res_blocks,
+            attn_resolutions,
+            dropout=0.0,
+            resamp_with_conv=True,
+            in_channels,
+            resolution,
+            z_channels,
+            double_z=True,
+            num_frames=4,
+            num_att_blocks=5,
+            tx_dim=768,
+            num_heads=12,
+            mlp_ratio=2,  # denoted by srt
+            patch_size=16,
+            decomposed=False,
+            **kwargs):
+        super().__init__()
+        # self.ray_encoder = RayEncoder(pos_octaves=15, pos_start_octave=pos_start_octave,
+        #                               ray_octaves=15)
+
+        # conv_blocks = [SRTConvBlock(idim=183, hdim=96)]
+        # cur_hdim = 192
+        # for i in range(1, num_conv_blocks):
+        #     conv_blocks.append(SRTConvBlock(idim=cur_hdim, odim=None))
+        #     cur_hdim *= 2
+        self.num_frames = num_frames
+        self.embed_dim = tx_dim
+        self.embedder = PatchEmbed(
+            img_size=256,
+            patch_size=patch_size,
+            # patch_size=8, # compare the performance
+            in_chans=in_channels,
+            embed_dim=self.embed_dim,
+            norm_layer=None,
+            flatten=True,
+            bias=True,
+        )  # downsample f=16 here.
+
+        # same configuration as vit-B
+        if not decomposed:
+            self.transformer = Transformer(
+                self.embed_dim,  # 12 * 64 = 768
+                depth=num_att_blocks,
+                heads=num_heads,
+                mlp_dim=mlp_ratio * self.embed_dim,  # 1536 by default
+            )
+        else:
+            self.transformer_selfattn = Transformer(
+                self.embed_dim,  # 12 * 64 = 768
+                depth=1,
+                heads=num_heads,
+                mlp_dim=mlp_ratio * self.embed_dim,  # 1536 by default
+            )
+            self.transformer = Transformer(
+                self.embed_dim,  # 12 * 64 = 768
+                # depth=num_att_blocks-1,
+                depth=num_att_blocks,
+                heads=num_heads,
+                mlp_dim=mlp_ratio * self.embed_dim,  # 1536 by default
+            )
+
+        # to a compact latent, with CA
+        # query_dim = 4*(1+double_z)
+        query_dim = 12 * (1 + double_z
+                          )  # for high-quality 3D encoding, follow direct3D
+        self.latent_embedding = nn.Parameter(
+            torch.randn(1, 32 * 32 * 3, query_dim))
+        self.readout_ca = MemoryEfficientCrossAttention(
+            query_dim,
+            self.embed_dim,
+        )
+
+    def forward_tx(self, x):
+        x = self.transformer(x)  # B VL C
+
+        # ? 3DPE
+        x = self.readout_ca(self.latent_embedding.repeat(x.shape[0], 1, 1), x)
+
+        # ! reshape to 3D latent here. how to make the latent 3D-aware? Later. Performance first.
+        x = einops.rearrange(x, 'B (N H W) C -> B C (N H) W', H=32, W=32, N=3)
+        return x
+
+    def forward(self, x, **kwargs):
+        """
+        Args:
+            images: [batch_size, num_images, 3, height, width]. Assume the first image is canonical.
+            camera_pos: [batch_size, num_images, 3]
+            rays: [batch_size, num_images, height, width, 3]
+        Returns:
+            scene representation: [batch_size, num_patches, channels_per_patch]
+        """
+
+        x = self.embedder(x)  # B L C
+        x = einops.rearrange(x, '(B V) L C -> B (V L) C', V=self.num_frames)
+        x = self.forward_tx(x)
+
+        return x
+
+
+# ! ablation the srt design
+class ImprovedSRTEncoderVAE_K8(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(patch_size=8, **kwargs)
+
+
+class ImprovedSRTEncoderVAE_L6(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(num_att_blocks=6, **kwargs)
+
+
+class ImprovedSRTEncoderVAE_L5_vitl(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(num_att_blocks=5, tx_dim=1024, num_heads=16, **kwargs)
+
+
+class ImprovedSRTEncoderVAE_mlp_ratio4(ImprovedSRTEncoderVAE
+                                       ):  # ! by default now
+
+    def __init__(self, **kwargs):
+        super().__init__(mlp_ratio=4, **kwargs)
+
+
+class ImprovedSRTEncoderVAE_mlp_ratio4_decomposed(
+        ImprovedSRTEncoderVAE_mlp_ratio4):
+
+    def __init__(self, **kwargs):
+        super().__init__(decomposed=True, **kwargs)  # just decompose tx
+
+    def forward(self, x, **kwargs):
+        """
+        Args:
+            images: [batch_size, num_images, 3, height, width]. Assume the first image is canonical.
+            camera_pos: [batch_size, num_images, 3]
+            rays: [batch_size, num_images, height, width, 3]
+        Returns:
+            scene representation: [batch_size, num_patches, channels_per_patch]
+        """
+
+        x = self.embedder(x)  # B L C
+        # x = einops.rearrange(x, '(B V) L C -> B (V L) C', V=self.num_frames)
+        x = self.transformer_selfattn(x)
+        x = einops.rearrange(x, '(B V) L C -> B (V L) C', V=self.num_frames)
+        x = self.forward_tx(x)
+
+        return x
+
+
+class ImprovedSRTEncoderVAE_mlp_ratio4_f8(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(mlp_ratio=4, patch_size=8, **kwargs)
+
+
+class ImprovedSRTEncoderVAE_mlp_ratio4_f8_L6(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(mlp_ratio=4, patch_size=8, num_att_blocks=6, **kwargs)
+
+
+class ImprovedSRTEncoderVAE_mlp_ratio4_L6(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(mlp_ratio=4, num_att_blocks=6, **kwargs)
+
+
+# ! an SD VAE with one SRT attention + one CA attention for KL
+class HybridEncoder(Encoder):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        # st()
+        self.srt = ImprovedSRTEncoderVAE(
+            **kwargs,
+            #  num_frames=4,
+            num_att_blocks=1,  # only one layer required
+            tx_dim=self.conv_out.weight.shape[1],
+            num_heads=8,  # 256  / 64
+            mlp_ratio=4,  # denoted by srt
+            #  patch_size=16,
+        )
+        del self.srt.embedder  # use original
+        self.conv_out = nn.Identity()
+
+    def forward(self, x, **kwargs):
+        x = super().forward(x)
+        x = einops.rearrange(x,
+                             '(B V) C H W -> B (V H W) C',
+                             V=self.srt.num_frames)
+        x = self.srt.forward_tx(x)
+        return x
+
+
+class ImprovedSRTEncoderVAE_mlp_ratio4_heavyPatchify(ImprovedSRTEncoderVAE):
+
+    def __init__(self, **kwargs):
+        super().__init__(mlp_ratio=4, **kwargs)
+        del self.embedder
+
+        conv_blocks = [SRTConvBlock(idim=10, hdim=48)]  # match the ViT-B dim
+        cur_hdim = 48 * 2
+        for i in range(1,
+                       4):  # f=16 still. could reduce attention layers by one?
+            conv_blocks.append(SRTConvBlock(idim=cur_hdim, odim=None))
+            cur_hdim *= 2
+
+        self.embedder = nn.Sequential(*conv_blocks)
+
+    def forward(self, x, **kwargs):
+        """
+        Args:
+            images: [batch_size, num_images, 3, height, width]. Assume the first image is canonical.
+            camera_pos: [batch_size, num_images, 3]
+            rays: [batch_size, num_images, height, width, 3]
+        Returns:
+            scene representation: [batch_size, num_patches, channels_per_patch]
+        """
+
+        x = self.embedder(x)  # B C H W
+        x = einops.rearrange(x,
+                             '(B V) C H W -> B (V H W) C',
+                             V=self.num_frames)
+        x = self.transformer(x)  # B VL C
+
+        # ? 3DPE
+        x = self.readout_ca(self.latent_embedding.repeat(x.shape[0], 1, 1), x)
+
+        # ! reshape to 3D latent here. how to make the latent 3D-aware? Later. Performance first.
+        x = einops.rearrange(x, 'B (N H W) C -> B C (N H) W', H=32, W=32, N=3)
+
+        return x
+
+
+class HybridEncoderPCDStructuredLatent(Encoder):
+
+    def __init__(self, num_frames, latent_num=768, **kwargs):
+        super().__init__(**kwargs)
+        # st()
+        self.num_frames = num_frames
+        tx_dim = self.conv_out.weight.shape[1]  # after encoder mid_layers
+        self.srt = ImprovedSRTEncoderVAE(
+            **kwargs,
+            #  num_frames=4,
+            num_att_blocks=3,  # only one layer required
+            tx_dim=tx_dim,
+            num_heads=8,  # 256  / 64
+            mlp_ratio=4,  # denoted by srt
+        )
+        del self.srt.embedder, self.srt.readout_ca, self.srt.latent_embedding  # use original
+
+        # self.box_pool2d = kornia.filters.BlurPool2D(kernel_size=(8,8), stride=8)
+        self.box_pool2d = kornia.filters.BlurPool2D(kernel_size=(8, 8),
+                                                    stride=8)
+        # self.pool2d = kornia.filters.MedianBlur(kernel_size=(8,8), stride=8)
+        self.agg_ca = MemoryEfficientCrossAttention(
+            tx_dim,
+            tx_dim,
+            qk_norm=True,  # as in vit-22B
+        )
+        self.spatial_token_reshape = lambda x: einops.rearrange(
+            x, '(B V) C H W -> B (V H W) C', V=self.num_frames)
+        self.latent_num = latent_num  # 768 * 3 by default
+        self.xyz_pos_embed = XYZPosEmbed(tx_dim)
+
+        # ! VAE part
+        self.conv_out = nn.Identity()
+        self.Mlp_out = PreNorm(
+            tx_dim,  # ! add PreNorm before VAE reduction, stablize training.
+            Mlp(
+                in_features=tx_dim,  # reduce dim
+                hidden_features=tx_dim,
+                out_features=self.z_channels * 2,  # double_z
+                act_layer=approx_gelu,
+                drop=0))
+        self.ca_no_pcd = False
+        self.pixel_aligned_query = False
+        self.pc2 = True
+        if self.pc2:
+            # https://github.com/lukemelas/projection-conditioned-point-cloud-diffusion/blob/64fd55a0d00b52735cf02e11c5112374c7104ece/experiments/model/projection_model.py#L87
+            # Save rasterization settings
+            raster_point_radius: float = 0.0075  # point size
+            image_size = 512 # ? hard coded
+            raster_points_per_pixel: int = 1
+            bin_size: int = 0
+            self.raster_settings = PointsRasterizationSettings(
+                image_size=(image_size, image_size),
+                radius=raster_point_radius,
+                points_per_pixel=raster_points_per_pixel,
+                bin_size=bin_size,
+            )
+            self.scale_factor = 1
+
+
+    # def _process_token_xyz(self, token_xyz, h):
+    #     # pad zero xyz points to reasonable value.
+
+    #     nonzero_mask = (token_xyz != 0).all(dim=2)  # Shape: (B, N)
+    #     non_zero_token_xyz = token_xyz[nonzero_mask]
+    #     non_zero_token_h = h[nonzero_mask]
+
+    #     # for loop to get foreground points of each instance
+    #     # TODO, accelerate with vmap
+    #     # No, directly use sparse pcd as input as surface points? fps sampling 768 from 4096 points.
+    #     # All points here should not have 0 xyz.
+    #     # fg_token_xyz = []
+    #     # for idx in range(token_xyz.shape[1]):
+
+    #     fps_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+    #         non_zero_token_xyz, K=self.latent_num) # B self.latent_num
+    #     # pcu.save_mesh_v(f'xyz.ply', xyz[0].float().detach().permute(1,2,0).reshape(-1,3).cpu().numpy(),) # check result first, before fps sampling
+    #     # pcu.save_mesh_v(f'fps_xyz.ply', fps_xyz[0].float().detach().reshape(-1,3).cpu().numpy(),) # check result first, before fps sampling
+    #     pcu.save_mesh_v(f'token_xyz3.ply', token_xyz[0].float().detach().reshape(-1,3).cpu().numpy(),)
+    #     # xyz = self.spatial_token_reshape(xyz)
+    #     # pcu.save_mesh_v(f'xyz_new.ply', xyz[0].float().detach().reshape(-1,3).cpu().numpy(),)
+
+    #     st()
+    #     query_h = masked_gather(non_zero_token_h, fps_idx) # torch.gather with dim expansion
+
+    #     return query_h, fps_xyz
+
+    def _process_token_xyz(self, pcd, pcd_h):
+        # ! 16x uniform downsample before FPS.
+        # rand_start_pt = random.randint(0,16)
+        # query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+        #     pcd[:, rand_start_pt::16], K=self.latent_num, random_start_point=True) # B self.latent_num
+        # query_pcd_h = masked_gather(pcd_h[:, rand_start_pt::16], fps_idx) # torch.gather with dim expansion
+
+        # ! fps very slow on high-res pcd
+        query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+            pcd, K=self.latent_num,
+            # random_start_point=False)  # B self.latent_num
+            random_start_point=True)  # B self.latent_num
+        query_pcd_h = masked_gather(pcd_h,
+                                    fps_idx)  # torch.gather with dim expansion
+
+        # pcu.save_mesh_v(f'xyz.ply', xyz[0].float().detach().permute(1,2,0).reshape(-1,3).cpu().numpy(),) # check result first, before fps sampling
+        # pcu.save_mesh_v(f'fps_xyz.ply', fps_xyz[0].float().detach().reshape(-1,3).cpu().numpy(),) # check result first, before fps sampling
+        # pcu.save_mesh_v(f'query_pcd_xyz.ply', query_pcd_xyz[0].float().detach().reshape(-1,3).cpu().numpy(),)
+        # pcu.save_mesh_v(f'pcd_xyz.ply', pcd[0].float().detach().reshape(-1,3).cpu().numpy(),)
+        # xyz = self.spatial_token_reshape(xyz)
+        # pcu.save_mesh_v(f'xyz_new.ply', xyz[0].float().detach().reshape(-1,3).cpu().numpy(),)
+
+        return query_pcd_h, query_pcd_xyz
+
+    def forward(self, x, pcd, **kwargs):
+
+        # def forward(self, x, num_frames=None):
+        assert x.shape[1] == 15  # rgb(3),normal(3),plucker_ray(6),xyz(3)
+        xyz = x[:, -3:, ...]  # for fps downsampling
+
+        # 0. retrieve VAE tokens
+        h = super().forward(
+            x, num_frames=self.num_frames
+        )  # ! support data augmentation, different FPS different latent corresponding to the same instance?
+
+        # st()
+        # pcu.save_mesh_v(f'{Path(logger.get_dir())}/anchor_all.ply',pcd[0].float().detach().cpu().numpy())
+
+        # ! add 3D PE.
+        # 1. unproj 2D tokens to 3D
+        token_xyz = xyz[..., 4::8, 4::8]
+
+        if self.pixel_aligned_query:
+
+            # h = self.spatial_token_reshape(h) # V frames merge to a single latent here.
+            # h = h + self.xyz_pos_embed(token_xyz) # directly add PE to h here.
+
+            # # ! PE over surface fps-pcd
+            # pcd_h = self.xyz_pos_embed(pcd) # directly add PE to h here.
+
+            # 2. fps sampling surface as pcd-structured latent.
+            h, query_pcd_xyz = self._process_token_xyz(
+                pcd, token_xyz, h, c=kwargs.get('c'),
+                x=x)  # aggregate with pixel-aligned operation.
+        
+        elif self.pc2: # rasterize the point cloud to multi-view feature maps
+            # https://github.com/lukemelas/projection-conditioned-point-cloud-diffusion/blob/64fd55a0d00b52735cf02e11c5112374c7104ece/experiments/model/projection_model.py#L128
+
+            # ! prepare the features before projection
+            token_xyz = self.spatial_token_reshape(token_xyz)
+            h = self.spatial_token_reshape(
+                h)  # V frames merge to a single latent here.
+            # directly add PE to h here.
+            h = h + self.xyz_pos_embed(token_xyz) # h: B L C
+
+            # ! prepare pytorch3d camera
+            c = kwargs['c'] # gs_format dict
+            focal_length = c['orig_pose'][..., 16:17] # B V 1
+            img_h, img_w = x.shape[-2:]
+            R, T = c['R'], c['T'] # B V 3 3, B V 3
+            
+            # ! bs=1 test. will merge B, V later for parallel compute.
+            V = focal_length.shape[1]
+            principal_point = torch.zeros(V, 2)
+            img_size = torch.Tensor([img_h, img_w]).unsqueeze(0).repeat_interleave(V, 0).to(focal_length)
+            camera = PerspectiveCameras(focal_length=focal_length[0],principal_point=principal_point, R=R[0], T=T[0], image_size=img_size)
+
+            # camera = PerspectiveCameras(focal_length=focal_length, R=R, T=T, image_size=(img_h, img_w))
+            # !Create rasterizer
+            rasterizer = PointsRasterizer(cameras=camera.to(pcd.device), raster_settings=self.raster_settings)
+
+            fragments = rasterizer(Pointclouds(pcd[0:1].repeat_interleave(V, 0)))  # (B, H, W, R)
+            fragments_idx: Tensor = fragments.idx.long()
+            visible_pixels = (fragments_idx > -1)  # (B, H, W, R)
+
+            view_idx = 0  # Index of the viewpoint
+            # (Pdb) fragments.zbuf.shape
+            # torch.Size([8, 512, 512, 1])
+            # depth_image = fragments.zbuf[0, ..., 0].cpu().numpy()  # Take the nearest point's depth
+            # depth_image = (depth_image - depth_image.min()) / (depth_image.max()-depth_image.min())
+            # imageio.imwrite('tmp/depth.jpg', (depth_image*255.0).astype(np.uint8))
+            # st()
+
+            points_to_visible_pixels = fragments_idx[visible_pixels]
+            # ! visualize the results
+
+            # for debug
+            normal = x[:, 3:6, ...]
+            normal_map = (normal * 127.5 + 127.5).float().to(
+                torch.uint8)  # BV 3 H W
+
+            st()
+            pass
+
+        else:
+            token_xyz = self.spatial_token_reshape(token_xyz)
+            h = self.spatial_token_reshape(
+                h)  # V frames merge to a single latent here.
+            h = h + self.xyz_pos_embed(token_xyz)  # directly add PE to h here.
+
+            # ! PE over surface fps-pcd
+            pcd_h = self.xyz_pos_embed(pcd)  # directly add PE to h here.
+
+            # 2. fps sampling surface as pcd-structured latent.
+            query_pcd_h, query_pcd_xyz = self._process_token_xyz(pcd, pcd_h)
+
+            # 2.5 Cross attention to aggregate from all tokens.
+            if self.ca_no_pcd:
+                h = self.agg_ca(query_pcd_h, h)
+            else:
+                h = self.agg_ca(
+                    query_pcd_h, torch.cat([h, pcd_h], dim=1)
+                )  # cross attend to aggregate info from both vae-h and pcd-h
+
+        # 3. add vit TX (5 layers, concat xyz-PE)
+        # h = h + self.xyz_pos_embed(fps_xyz) # TODO, add PE of query pts. directly add to h here.
+        h = self.srt.transformer(h)  # B L C
+
+        h = self.Mlp_out(h)  # equivalent to conv_out, 256 -> 8 in sd-VAE
+        # h = einops.rearrange(h, 'B L C -> B C L') # for VAE compat
+
+        return {
+            'h': h,
+            'query_pcd_xyz': query_pcd_xyz
+        }  # h_0, point cloud-structured latent space. For VAE later.
+
+
+class HybridEncoderPCDStructuredLatentUniformFPS(
+        HybridEncoderPCDStructuredLatent):
+
+    def __init__(self, num_frames, latent_num=768, **kwargs):
+        super().__init__(num_frames, latent_num, **kwargs)
+        self.ca_no_pcd = True  # check speed up ratio
+
+    def _process_token_xyz(self, pcd, pcd_h):
+        # ! 16x uniform downsample before FPS.
+        rand_start_pt = random.randint(0, 16)
+        # rand_start_pt = 0
+        query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+            # pcd[:, rand_start_pt::16], K=self.latent_num, random_start_point=False) # B self.latent_num
+            pcd[:, rand_start_pt::16],
+            K=self.latent_num,
+            random_start_point=True)  # B self.latent_num
+        query_pcd_h = masked_gather(pcd_h[:, rand_start_pt::16],
+                                    fps_idx)  # torch.gather with dim expansion
+        # st()
+
+        # ! fps very slow on high-res pcd
+        # query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+        #     pcd, K=self.latent_num, random_start_point=True) # B self.latent_num
+        # query_pcd_h = masked_gather(pcd_h, fps_idx) # torch.gather with dim expansion
+
+        # pcu.save_mesh_v(f'xyz.ply', xyz[0].float().detach().permute(1,2,0).reshape(-1,3).cpu().numpy(),) # check result first, before fps sampling
+        # pcu.save_mesh_v(f'fps_xyz.ply', fps_xyz[0].float().detach().reshape(-1,3).cpu().numpy(),) # check result first, before fps sampling
+        # pcu.save_mesh_v(f'query_pcd_xyz.ply', query_pcd_xyz[0].float().detach().reshape(-1,3).cpu().numpy(),)
+        # pcu.save_mesh_v(f'pcd_xyz.ply', pcd[0].float().detach().reshape(-1,3).cpu().numpy(),)
+        # xyz = self.spatial_token_reshape(xyz)
+        # pcu.save_mesh_v(f'xyz_new.ply', xyz[0].float().detach().reshape(-1,3).cpu().numpy(),)
+
+        return query_pcd_h, query_pcd_xyz
+
+
+class HybridEncoderPCDStructuredLatentSNoPCD(HybridEncoderPCDStructuredLatent):
+
+    def __init__(self, num_frames, latent_num=768, **kwargs):
+        super().__init__(num_frames, latent_num, **kwargs)
+        self.ca_no_pcd = True
+
+class HybridEncoderPCDStructuredLatentSNoPCD_PC2(HybridEncoderPCDStructuredLatentSNoPCD):
+
+    def __init__(self, num_frames, latent_num=768, **kwargs):
+        super().__init__(num_frames, latent_num, **kwargs)
+        self.pc2 = True
+
+
+class HybridEncoderPCDStructuredLatentSNoPCD_PixelAlignedQuery(
+        HybridEncoderPCDStructuredLatent):
+
+    def __init__(self, num_frames, latent_num=768, **kwargs):
+        super().__init__(num_frames, latent_num, **kwargs)
+        self.ca_no_pcd = True
+        self.pixel_aligned_query = True
+        self.F = 4  # pixel-aligned query from nearest F views
+
+        del self.agg_ca  # for average pooling now.
+
+    def _pcd_to_homo(self, pcd):
+        return torch.cat([pcd, torch.ones_like(pcd[..., 0:1])], -1)
+
+    # ! FPS sampling
+    def _process_token_xyz(self, pcd, token_xyz, h, c, x=None):
+        V = c['cam_pos'].shape[1]
+
+        # (Pdb) p c.keys()
+        # dict_keys(['source_cv2wT_quat', 'cam_view', 'cam_view_proj', 'cam_pos', 'tanfov', 'orig_pose', 'orig_c2w', 'orig_w2c'])
+        # (Pdb) p c['cam_view'].shape
+        # torch.Size([8, 9, 4, 4])
+        # (Pdb) p c['cam_pos'].shape
+        # torch.Size([8, 9, 3])
+
+        # ! 16x uniform downsample before FPS.
+        # rand_start_pt = random.randint(0,16)
+        # query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+        #     pcd[:, rand_start_pt::16], K=self.latent_num, random_start_point=True) # B self.latent_num
+        # query_pcd_h = masked_gather(pcd_h[:, rand_start_pt::16], fps_idx) # torch.gather with dim expansion
+
+        # ! fps very slow on high-res pcd, but better.
+        # '''
+        query_pcd_xyz, fps_idx = pytorch3d.ops.sample_farthest_points(
+            pcd, K=self.latent_num, random_start_point=True) # B self.latent_num
+        # query_pcd_h = masked_gather(pcd_h, fps_idx) # torch.gather with dim expansion
+
+        # '''
+        # ! use unprojected xyz for pixel-aligned projection check
+
+        # query_pcd_xyz = self.spatial_token_reshape(token_xyz)
+        B, N = query_pcd_xyz.shape[:2]
+
+        normal = x[:, 3:6, ...]
+        normal_map = (normal * 127.5 + 127.5).float().to(
+            torch.uint8)  # BV 3 H W
+
+        normal_map = einops.rearrange(normal_map,
+                                           '(B V) C H W -> B V C H W',
+                                           B=B,
+                                           V=V).detach().cpu()  # V C H W
+        img_size = normal_map.shape[-1]
+
+        # ! ====== single-view debug here
+        for b in range(c['orig_w2c'].shape[0]):
+            for V in range(c['orig_w2c'].shape[1]):
+                selected_normal = normal_map[b, V]
+                proj_point = c['orig_w2c'][b, V] @ self._pcd_to_homo(query_pcd_xyz[b]).permute(1, 0)
+                proj_point[:2, ...] /= proj_point[2, ...]
+                proj_point[2, ...] = 1 # homo
+
+
+                intrin = c['orig_intrin'][b, V]
+                proj_point = intrin @ proj_point[:3]
+                proj_point = proj_point.permute(1,0)[..., :2] # 768 4
+
+                # st()
+
+                # proj_point = c['cam_view_proj'][b, V] @ self._pcd_to_homo(query_pcd_xyz[b]).permute(1, 0)
+
+                # plot proj_point and save
+                for uv_idx in range(proj_point.shape[0]):
+                    # uv = proj_point[uv_idx] * 127.5 + 127.5
+                    # uv = proj_point[uv_idx] * 127.5 + 127.5
+                    uv = proj_point[uv_idx] * img_size
+                    x, y = int(uv[0].clip(0, img_size)), int(uv[1].clip(0, img_size))
+                    selected_normal[:, max(y - 1, 0):min(y + 1, img_size),
+                                    max(x - 1, 0):min(x + 1, img_size)] = torch.Tensor([
+                                        255, 0, 0
+                                    ]).reshape(3, 1, 1).to(selected_normal)  # set to red
+
+                torchvision.utils.save_image(selected_normal.float(),
+                                            f'tmp/pifu_normal_{b}_{V}.jpg',
+                                            normalize=True,
+                                            value_range=(0, 255))
+            
+
+            st()
+            pass
+
+        st()
+        # ! ====== single-view debug done
+
+
+        # ! project pcd to each views
+        batched_query_pcd = einops.repeat(self._pcd_to_homo(query_pcd_xyz),
+                                          'B N C -> (B V N) C 1',
+                                          V=V)
+        batched_cam_view_proj = einops.repeat(c['cam_view_proj'],
+                                              'B V H W -> (B V N) H W',
+                                              N=N)
+
+        batched_proj_uv = einops.rearrange(
+            (batched_cam_view_proj @ batched_query_pcd),
+            '(B V N) L 1 -> (B V) L N',
+            B=B,
+            V=V,
+            N=N)  # BV 4 N
+        batched_proj_uv = batched_proj_uv[..., :2, :]  # BV N 2
+
+        # draw projected UV coordinate on 2d normal map
+        # idx_to_vis = 15 * 32 + 16 # middle of the img
+        # idx_to_vis = 16 * 6 + 15 * 32 + 16  # middle of the img
+        idx_to_vis = 0 # use fps points here
+        # st()
+
+        selected_proj_uv = einops.rearrange(batched_proj_uv,
+                                            '(B V) C N -> B V C N',
+                                            B=B,
+                                            V=V,
+                                            N=N)[0, ...,
+                                                 idx_to_vis]  # V 2 N -> V 2
+        # selected_normal = einops.rearrange(normal_map,
+        #                                    '(B V) C H W -> B V C H W',
+        #                                    B=B,
+        #                                    V=V)[0].detach().cpu()  # V C H W
+
+        for uv_idx in range(selected_proj_uv.shape[0]):
+            uv = selected_proj_uv[uv_idx] * 127.5 + 127.5
+            x, y = int(uv[0].clip(0, 255)), int(uv[1].clip(0, 255))
+            selected_normal[uv_idx, :,
+                            max(y - 5, 0):min(y + 5, 255),
+                            max(x - 5, 0):min(x + 5, 255)] = torch.Tensor([
+                                255, 0, 0
+                            ]).reshape(3, 1,
+                                       1).to(selected_normal)  # set to red
+            # selected_normal[uv_idx, :, max(y-5, 0):min(y+5, 255), max(x-5,0):min(x+5,255)] = torch.Tensor([255,0,0]).to(selected_normal) # set to red
+        # st()
+        torchvision.utils.save_image(selected_normal.float(),
+                                     'pifu_normal.jpg',
+                                     normalize=True,
+                                     value_range=(0, 255))
+        st()
+        pass
+
+        # ! grid sample
+        query_pcd_h = index(
+            h, batched_proj_uv)  # h: (B V) C H W, uv: (B V) N 2  -> BV 256 768
+
+        query_pcd_h_to_gather = einops.rearrange(query_pcd_h,
+                                                 '(B V) C N -> B N V C',
+                                                 B=B,
+                                                 V=V,
+                                                 N=N)
+
+        # ! find nearest F views
+        _, knn_idx, _ = pytorch3d.ops.knn_points(
+            query_pcd_xyz, c['cam_pos'], K=self.F,
+            return_nn=False)  # knn_idx: B N F
+        knn_idx_expanded = knn_idx[..., None].expand(
+            -1, -1, -1, query_pcd_h_to_gather.shape[-1])  # B N F -> B N F C
+        knn_pcd_h = torch.gather(
+            query_pcd_h_to_gather, dim=2,
+            index=knn_idx_expanded)  # torch.Size([8, 768, 4, 256])
+
+        # average pooling knn feature.
+        query_pcd_h = knn_pcd_h.mean(dim=2)
+
+        # add PE
+        pcd_h = self.xyz_pos_embed(query_pcd_xyz)  # pcd_h as PE feature.
+        query_pcd_h = query_pcd_h + pcd_h
+
+        # TODO: QKV aggregation with pcd_h as q, query_pcd_h as kv. Requires gather?
+        '''not used; binary mask for aggregation.
+
+        # * mask idx not used anymore. torch.gather() instead, more flexible.
+        # knn_idx_mask = torch.zeros((B,N,V), device=knn_idx.device)
+        # knn_idx_mask.scatter_(dim=2, index=knn_idx, src=torch.ones_like(knn_idx_mask)) # ! B N V
+
+        # try gather
+        # gather_idx = einops.rearrange(knn_idx_mask, 'B N V -> B N V 1').bool()
+
+        # query_pcd_h = einops.rearrange(query_pcd_h, "(B V) C N -> B N V C", B=pcd_h.shape[0], N=self.latent_num, V=V) # torch.Size([8, 768, 4, 256])
+        # ! apply KNN mask and average the feature.
+        # query_pcd_h = einops.reduce(query_pcd_h * knn_idx_mask.unsqueeze(-1), 'B N V C -> B N C', 'sum') / self.F # B 768 256. average pooling aggregated feature, like in pifu.
+        '''
+        '''
+        # pixel-aligned projection, not efficient enough.
+        knn_cam_view_proj = pytorch3d.ops.knn_gather(einops.rearrange(c['cam_view_proj'], 'B V H W-> B V (H W)'), knn_idx) # get corresponding cam_view_projection matrix (P matrix)
+        knn_cam_view_proj = einops.rearrange(knn_cam_view_proj, 'B N F (H W) -> (B N F) H W', H=4, W=4) # for matmul. H=W=4 here, P matrix.
+
+        batched_query_pcd = einops.repeat(self._pcd_to_homo(query_pcd_xyz), 'B N C -> (B N F) C 1', F=self.F)
+        xyz = knn_cam_view_proj @ batched_query_pcd # BNF 4 1
+
+        # st()
+        knn_spatial_feat = pytorch3d.ops.knn_gather(einops.rearrange(h, '(B V) C H W -> B V (C H W)', V=self.num_frames), knn_idx) # get corresponding feat for grid_sample
+        knn_spatial_feat = einops.rearrange(knn_spatial_feat, 'B N F (C H W) -> (B N F) C H W', C=h.shape[-3], H=h.shape[-2], W=h.shape[-1])
+        '''
+
+        # grid_sample
+        # https://github.com/shunsukesaito/PIFu/blob/f0a9c99ef887e1eb360e865a87aa5f166231980e/lib/geometry.py#L15
+
+        # average pooling multi-view extracted information
+
+        # return query_pcd_h, query_pcd_xyz
+        return query_pcd_h, query_pcd_xyz
diff --git a/nsr/srt/layers.py b/nsr/srt/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b26be0995f50b79b9e62057a4e57f9026ccec6b
--- /dev/null
+++ b/nsr/srt/layers.py
@@ -0,0 +1,190 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+import math
+from einops import rearrange
+from vit.vision_transformer import MemEffAttention, Attention
+# from xformers.triton import FusedLayerNorm as LayerNorm
+from torch.nn import LayerNorm
+from xformers.components.feedforward import fused_mlp
+# from xformers.components.feedforward import mlp
+from xformers.components.activations import build_activation, Activation
+
+
+class PositionalEncoding(nn.Module):
+
+    def __init__(self, num_octaves=8, start_octave=0):
+        super().__init__()
+        self.num_octaves = num_octaves
+        self.start_octave = start_octave
+
+    def forward(self, coords, rays=None):
+        embed_fns = []
+        batch_size, num_points, dim = coords.shape
+
+        octaves = torch.arange(self.start_octave,
+                               self.start_octave + self.num_octaves)
+        octaves = octaves.float().to(coords)
+        multipliers = 2**octaves * math.pi
+        coords = coords.unsqueeze(-1)
+        while len(multipliers.shape) < len(coords.shape):
+            multipliers = multipliers.unsqueeze(0)
+
+        scaled_coords = coords * multipliers
+
+        sines = torch.sin(scaled_coords).reshape(batch_size, num_points,
+                                                 dim * self.num_octaves)
+        cosines = torch.cos(scaled_coords).reshape(batch_size, num_points,
+                                                   dim * self.num_octaves)
+
+        result = torch.cat((sines, cosines), -1)
+        return result
+
+
+class RayEncoder(nn.Module):
+
+    def __init__(self,
+                 pos_octaves=8,
+                 pos_start_octave=0,
+                 ray_octaves=4,
+                 ray_start_octave=0):
+        super().__init__()
+        self.pos_encoding = PositionalEncoding(num_octaves=pos_octaves,
+                                               start_octave=pos_start_octave)
+        self.ray_encoding = PositionalEncoding(num_octaves=ray_octaves,
+                                               start_octave=ray_start_octave)
+
+    def forward(self, pos, rays):
+        if len(rays.shape) == 4:
+            batchsize, height, width, dims = rays.shape
+            pos_enc = self.pos_encoding(pos.unsqueeze(1))
+            pos_enc = pos_enc.view(batchsize, pos_enc.shape[-1], 1, 1)
+            pos_enc = pos_enc.repeat(1, 1, height, width)
+            rays = rays.flatten(1, 2)
+
+            ray_enc = self.ray_encoding(rays)
+            ray_enc = ray_enc.view(batchsize, height, width, ray_enc.shape[-1])
+            ray_enc = ray_enc.permute((0, 3, 1, 2))
+            x = torch.cat((pos_enc, ray_enc), 1)
+        else:
+            pos_enc = self.pos_encoding(pos)
+            ray_enc = self.ray_encoding(rays)
+            x = torch.cat((pos_enc, ray_enc), -1)
+
+        return x
+
+
+# Transformer implementation based on ViT
+# https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit.py
+
+
+class PreNorm(nn.Module):
+
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+
+class FeedForward(nn.Module):
+
+    def __init__(self, dim, hidden_dim, dropout=0.):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(dim, hidden_dim), nn.GELU(),
+                                 nn.Dropout(dropout),
+                                 nn.Linear(hidden_dim,
+                                           dim), nn.Dropout(dropout))
+
+    def forward(self, x):
+        return self.net(x)
+
+
+# class Attention(nn.Module):
+#     def __init__(self, dim, heads=8, dim_head=64, dropout=0., selfatt=True, kv_dim=None):
+#         super().__init__()
+#         inner_dim = dim_head * heads
+#         project_out = not (heads == 1 and dim_head == dim)
+
+#         self.heads = heads
+#         self.scale = dim_head ** -0.5
+
+#         self.attend = nn.Softmax(dim=-1)
+#         if selfatt:
+#             self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
+#         else:
+#             self.to_q = nn.Linear(dim, inner_dim, bias=False)
+#             self.to_kv = nn.Linear(kv_dim, inner_dim * 2, bias=False)
+
+#         self.to_out = nn.Sequential(
+#             nn.Linear(inner_dim, dim),
+#             nn.Dropout(dropout)
+#         ) if project_out else nn.Identity()
+
+#     def forward(self, x, z=None):
+#         if z is None:
+#             qkv = self.to_qkv(x).chunk(3, dim=-1)
+#         else:
+#             q = self.to_q(x)
+#             k, v = self.to_kv(z).chunk(2, dim=-1)
+#             qkv = (q, k, v)
+
+#         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
+
+#         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+#         attn = self.attend(dots)
+
+#         out = torch.matmul(attn, v)
+#         out = rearrange(out, 'b h n d -> b n (h d)')
+#         return self.to_out(out)
+
+
+class Transformer(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 heads,
+                 mlp_dim,
+                 dropout=0.,
+                 selfatt=True,
+                 kv_dim=None, 
+                 no_flash_op=False,):
+        super().__init__()
+
+        # if no_flash_op:
+        #     attn_cls = Attention # raw torch attention
+        # else:
+        attn_cls = MemEffAttention
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList([
+                    PreNorm(dim,
+                            attn_cls(
+                                dim,
+                                num_heads=heads,
+                                qkv_bias=True,
+                                qk_norm=True, # as in vit-22B
+                                no_flash_op=no_flash_op, 
+                            )),
+                    PreNorm(
+                        dim,
+                        fused_mlp.FusedMLP(dim,
+                        # mlp.MLP(dim,
+                                           hidden_layer_multiplier=mlp_dim //
+                                           dim,
+                                           dropout=dropout,
+                                           activation=Activation.GeLU))
+                ]))
+
+    def forward(self, x):
+        for attn, ff in self.layers: # type: ignore
+            x = attn(x) + x
+            x = ff(x) + x
+        return x
diff --git a/nsr/superresolution.py b/nsr/superresolution.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca286d4ba3b08f03f875b97a35ca91b98baa49b4
--- /dev/null
+++ b/nsr/superresolution.py
@@ -0,0 +1,446 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Superresolution network architectures from the paper
+"Efficient Geometry-aware 3D Generative Adversarial Networks"."""
+
+import torch
+from nsr.networks_stylegan2 import Conv2dLayer, SynthesisLayer, ToRGBLayer
+from torch_utils.ops import upfirdn2d
+from torch_utils import persistence
+from torch_utils import misc
+
+from nsr.networks_stylegan2 import SynthesisBlock
+import numpy as np
+from pdb import set_trace as st
+
+
+@persistence.persistent_class
+class SynthesisBlockNoUp(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels,  # Number of input channels, 0 = first block.
+            out_channels,  # Number of output channels.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            resolution,  # Resolution of this block.
+            img_channels,  # Number of output color channels.
+            is_last,  # Is this the last block?
+            architecture='skip',  # Architecture: 'orig', 'skip', 'resnet'.
+            resample_filter=[
+                1, 3, 3, 1
+            ],  # Low-pass filter to apply when resampling activations.
+            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+            use_fp16=False,  # Use FP16 for this block?
+            fp16_channels_last=False,  # Use channels-last memory format with FP16?
+            fused_modconv_default=True,  # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
+            **layer_kwargs,  # Arguments for SynthesisLayer.
+    ):
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.w_dim = w_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.is_last = is_last
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = (use_fp16 and fp16_channels_last)
+        self.fused_modconv_default = fused_modconv_default
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter(resample_filter))
+        self.num_conv = 0
+        self.num_torgb = 0
+
+        if in_channels == 0:
+            self.const = torch.nn.Parameter(
+                torch.randn([out_channels, resolution, resolution]))
+
+        if in_channels != 0:
+            self.conv0 = SynthesisLayer(in_channels,
+                                        out_channels,
+                                        w_dim=w_dim,
+                                        resolution=resolution,
+                                        conv_clamp=conv_clamp,
+                                        channels_last=self.channels_last,
+                                        **layer_kwargs)
+            self.num_conv += 1
+
+        self.conv1 = SynthesisLayer(out_channels,
+                                    out_channels,
+                                    w_dim=w_dim,
+                                    resolution=resolution,
+                                    conv_clamp=conv_clamp,
+                                    channels_last=self.channels_last,
+                                    **layer_kwargs)
+        self.num_conv += 1
+
+        if is_last or architecture == 'skip':
+            self.torgb = ToRGBLayer(out_channels,
+                                    img_channels,
+                                    w_dim=w_dim,
+                                    conv_clamp=conv_clamp,
+                                    channels_last=self.channels_last)
+            self.num_torgb += 1
+
+        if in_channels != 0 and architecture == 'resnet':
+            self.skip = Conv2dLayer(in_channels,
+                                    out_channels,
+                                    kernel_size=1,
+                                    bias=False,
+                                    up=2,
+                                    resample_filter=resample_filter,
+                                    channels_last=self.channels_last)
+
+    def forward(self,
+                x,
+                img,
+                ws,
+                force_fp32=False,
+                fused_modconv=None,
+                update_emas=False,
+                **layer_kwargs):
+        _ = update_emas  # unused
+        misc.assert_shape(ws,
+                          [None, self.num_conv + self.num_torgb, self.w_dim])
+        w_iter = iter(ws.unbind(dim=1))
+        if ws.device.type != 'cuda':
+            force_fp32 = True
+        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
+        if fused_modconv is None:
+            fused_modconv = self.fused_modconv_default
+        if fused_modconv == 'inference_only':
+            fused_modconv = (not self.training)
+
+        # Input.
+        if self.in_channels == 0:
+            x = self.const.to(dtype=dtype, memory_format=memory_format)
+            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
+        else:
+            misc.assert_shape(
+                x, [None, self.in_channels, self.resolution, self.resolution])
+            x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # Main layers.
+        if self.in_channels == 0:
+            x = self.conv1(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+        elif self.architecture == 'resnet':
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+            x = self.conv1(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           gain=np.sqrt(0.5),
+                           **layer_kwargs)
+            x = y.add_(x)
+        else:
+            x = self.conv0(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+            x = self.conv1(x,
+                           next(w_iter),
+                           fused_modconv=fused_modconv,
+                           **layer_kwargs)
+
+        # ToRGB.
+        # if img is not None:
+        # misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
+        # img = upfirdn2d.upsample2d(img, self.resample_filter)
+        if self.is_last or self.architecture == 'skip':
+            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
+            y = y.to(dtype=torch.float32,
+                     memory_format=torch.contiguous_format)
+            img = img.add_(y) if img is not None else y
+
+        # assert x.dtype == dtype # support AMP in this library
+        assert img is None or img.dtype == torch.float32
+        return x, img
+
+    def extra_repr(self):
+        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
+
+
+#----------------------------------------------------------------------------
+
+
+# for 512x512 generation
+@persistence.persistent_class
+class SuperresolutionHybrid8X(torch.nn.Module):
+    def __init__(
+            self,
+            channels,
+            img_resolution,
+            sr_num_fp16_res,
+            sr_antialias,
+            num_fp16_res=4,
+            conv_clamp=None,
+            channel_base=None,
+            channel_max=None,  # IGNORE
+            **block_kwargs):
+        super().__init__()
+        # assert img_resolution == 512
+
+        use_fp16 = sr_num_fp16_res > 0
+        self.input_resolution = 128
+        self.sr_antialias = sr_antialias
+        self.block0 = SynthesisBlock(channels,
+                                     128,
+                                     w_dim=512,
+                                     resolution=256,
+                                     img_channels=3,
+                                     is_last=False,
+                                     use_fp16=use_fp16,
+                                     conv_clamp=(256 if use_fp16 else None),
+                                     **block_kwargs)
+        self.block1 = SynthesisBlock(128,
+                                     64,
+                                     w_dim=512,
+                                     resolution=512,
+                                     img_channels=3,
+                                     is_last=True,
+                                     use_fp16=use_fp16,
+                                     conv_clamp=(256 if use_fp16 else None),
+                                     **block_kwargs)
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter([1, 3, 3, 1]))
+
+    def forward(self, rgb, x, ws, **block_kwargs):
+        ws = ws[:, -1:, :].repeat(1, 3, 1)
+
+        if x.shape[-1] != self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=self.sr_antialias)
+            rgb = torch.nn.functional.interpolate(rgb,
+                                                  size=(self.input_resolution,
+                                                        self.input_resolution),
+                                                  mode='bilinear',
+                                                  align_corners=False,
+                                                  antialias=self.sr_antialias)
+
+        x, rgb = self.block0(x, rgb, ws, **block_kwargs) # block_kwargs: {'noise_mode': 'none'}
+        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
+        return rgb
+
+
+#----------------------------------------------------------------------------
+
+
+# for 256x256 generation
+@persistence.persistent_class
+class SuperresolutionHybrid4X(torch.nn.Module):
+    def __init__(
+            self,
+            channels,
+            img_resolution,
+            sr_num_fp16_res,
+            sr_antialias,
+            num_fp16_res=4,
+            conv_clamp=None,
+            channel_base=None,
+            channel_max=None,  # IGNORE
+            **block_kwargs):
+        super().__init__()
+        # assert img_resolution == 256
+        use_fp16 = sr_num_fp16_res > 0
+        self.sr_antialias = sr_antialias
+        self.input_resolution = 128
+        self.block0 = SynthesisBlockNoUp(
+            channels,
+            128,
+            w_dim=512,
+            resolution=128,
+            img_channels=3,
+            is_last=False,
+            use_fp16=use_fp16,
+            conv_clamp=(256 if use_fp16 else None),
+            **block_kwargs)
+        self.block1 = SynthesisBlock(128,
+                                     64,
+                                     w_dim=512,
+                                     resolution=256,
+                                     img_channels=3,
+                                     is_last=True,
+                                     use_fp16=use_fp16,
+                                     conv_clamp=(256 if use_fp16 else None),
+                                     **block_kwargs)
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter([1, 3, 3, 1]))
+
+    def forward(self, rgb, x, ws, **block_kwargs):
+        ws = ws[:, -1:, :].repeat(1, 3, 1)
+
+        if x.shape[-1] < self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=self.sr_antialias)
+            rgb = torch.nn.functional.interpolate(rgb,
+                                                  size=(self.input_resolution,
+                                                        self.input_resolution),
+                                                  mode='bilinear',
+                                                  align_corners=False,
+                                                  antialias=self.sr_antialias)
+
+        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
+        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
+        return rgb
+
+
+#----------------------------------------------------------------------------
+
+
+# for 128 x 128 generation
+@persistence.persistent_class
+class SuperresolutionHybrid2X(torch.nn.Module):
+    def __init__(
+            self,
+            channels,
+            img_resolution,
+            sr_num_fp16_res,
+            sr_antialias,
+            num_fp16_res=4,
+            conv_clamp=None,
+            channel_base=None,
+            channel_max=None,  # IGNORE
+            **block_kwargs):
+        super().__init__()
+        assert img_resolution == 128
+
+        use_fp16 = sr_num_fp16_res > 0
+        self.input_resolution = 64
+        # self.input_resolution = 128
+
+        self.sr_antialias = sr_antialias
+        self.block0 = SynthesisBlockNoUp(
+            channels,
+            128,
+            w_dim=512,
+            resolution=64,
+            # resolution=128,
+            img_channels=3,
+            is_last=False,
+            use_fp16=use_fp16,
+            conv_clamp=(256 if use_fp16 else None),
+            **block_kwargs)
+        self.block1 = SynthesisBlock(128,
+                                     64,
+                                     w_dim=512,
+                                     resolution=128,
+                                    #  resolution=256,
+                                     img_channels=3,
+                                     is_last=True,
+                                     use_fp16=use_fp16,
+                                     conv_clamp=(256 if use_fp16 else None),
+                                     **block_kwargs)
+        self.register_buffer('resample_filter',
+                             upfirdn2d.setup_filter([1, 3, 3, 1]))
+
+    def forward(self, rgb, x, ws, **block_kwargs):
+        ws = ws[:, -1:, :].repeat(1, 3, 1)
+
+        if x.shape[-1] != self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=self.sr_antialias)
+            rgb = torch.nn.functional.interpolate(rgb,
+                                                  size=(self.input_resolution,
+                                                        self.input_resolution),
+                                                  mode='bilinear',
+                                                  align_corners=False,
+                                                  antialias=self.sr_antialias)
+
+        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
+        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
+        return rgb
+
+
+#----------------------------------------------------------------------------
+
+
+# for 512x512 generation
+@persistence.persistent_class
+class SuperresolutionHybrid8XDC(torch.nn.Module):
+    def __init__(
+            self,
+            channels,
+            img_resolution,
+            sr_num_fp16_res,
+            sr_antialias,
+            num_fp16_res=4,
+            conv_clamp=None,
+            channel_base=None,
+            channel_max=None,  # IGNORE
+            **block_kwargs):
+        super().__init__()
+        # assert img_resolution == 512
+
+        use_fp16 = sr_num_fp16_res > 0
+        self.input_resolution = 128
+        self.sr_antialias = sr_antialias
+        self.block0 = SynthesisBlock(channels,
+                                     256,
+                                     w_dim=512,
+                                     resolution=256,
+                                     img_channels=3,
+                                     is_last=False,
+                                     use_fp16=use_fp16,
+                                     conv_clamp=(256 if use_fp16 else None),
+                                     **block_kwargs)
+        self.block1 = SynthesisBlock(256,
+                                     128,
+                                     w_dim=512,
+                                     resolution=512,
+                                     img_channels=3,
+                                     is_last=True,
+                                     use_fp16=use_fp16,
+                                     conv_clamp=(256 if use_fp16 else None),
+                                     **block_kwargs)
+
+    def forward(self, rgb, x, ws, base_x=None, **block_kwargs):
+        ws = ws[:, -1:, :].repeat(1, 3, 1)  # BS 3 512
+
+        # st()
+        if x.shape[-1] != self.input_resolution:  # resize 64 => 128
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=self.sr_antialias)
+            rgb = torch.nn.functional.interpolate(rgb,
+                                                  size=(self.input_resolution,
+                                                        self.input_resolution),
+                                                  mode='bilinear',
+                                                  align_corners=False,
+                                                  antialias=self.sr_antialias)
+
+        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
+        # print(f'device={self.block0.conv1.weight.device}')
+        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
+        # print(f'device={self.block1.conv1.weight.device}')
+        return rgb
+
+
+#----------------------------------------------------------------------------
diff --git a/nsr/train_nv_util.py b/nsr/train_nv_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..b98302889e808e9cbc1fe73c2e9b42ce21841fda
--- /dev/null
+++ b/nsr/train_nv_util.py
@@ -0,0 +1,3049 @@
+import copy
+import cv2
+import einops
+from collections import defaultdict
+import matplotlib.pyplot as plt
+import random
+# import emd
+import pytorch3d.loss
+# import imageio.v3
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+from einops import rearrange
+import webdataset as wds
+
+from nsr.camera_utils import generate_input_camera, uni_mesh_path
+import point_cloud_utils as pcu
+import traceback
+import blobfile as bf
+from datasets.g_buffer_objaverse import focal2fov, fov2focal
+import math
+import imageio
+import numpy as np
+# from sympy import O
+import torch
+from torch.autograd import Function
+import torch.nn.functional as F
+import torch as th
+import open3d as o3d
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+import pytorch3d.ops
+
+from torch.profiler import profile, record_function, ProfilerActivity
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+from guided_diffusion.train_util import (calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+
+from datasets.g_buffer_objaverse import unity2blender, unity2blender_th, PostProcess
+
+from nsr.volumetric_rendering.ray_sampler import RaySampler
+from utils.mesh_util import post_process_mesh, to_cam_open3d_compat
+from .camera_utils import LookAtPoseSampler, FOV_to_intrinsics
+from nsr.camera_utils import generate_input_camera, uni_mesh_path, sample_uniform_cameras_on_sphere 
+
+from utils.gs_utils.graphics_utils import getWorld2View2, getProjectionMatrix, getView2World
+from utils.general_utils import matrix_to_quaternion
+from utils.mesh_util import post_process_mesh, to_cam_open3d_compat, smooth_mesh
+from datasets.g_buffer_objaverse import focal2fov, fov2focal
+
+from .train_util import TrainLoop3DRec
+import kornia
+
+
+@th.autocast(device_type='cuda', dtype=th.float16, enabled=False)
+def psnr(input, target, max_val):
+    return kornia.metrics.psnr(input, target, max_val)
+
+
+def calc_emd(output, gt, eps=0.005, iterations=50):
+    import utils.emd.emd_module as emd
+    emd_loss = emd.emdModule()
+    dist, _ = emd_loss(output, gt, eps, iterations)
+    emd_out = torch.sqrt(dist).mean(1)
+    return emd_out
+
+
+class TrainLoop3DRecNV(TrainLoop3DRec):
+    # supervise the training of novel view
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+        self.rec_cano = True
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # return super().forward_backward(batch, *args, **kwargs)
+
+        self.mp_trainer_rec.zero_grad()
+        batch_size = batch['img_to_encoder'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            # st()
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            # ! concat novel-view? next version. also add self reconstruction, patch-based loss in the next version. verify novel-view prediction first.
+
+            # wrap forward within amp
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                target_nvs = {}
+                target_cano = {}
+
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                pred = self.rec_model(
+                    latent=latent,
+                    c=micro['nv_c'],  # predict novel view here
+                    behaviour='triplane_dec')
+
+                for k, v in micro.items():
+                    if k[:2] == 'nv':
+                        orig_key = k.replace('nv_', '')
+                        target_nvs[orig_key] = v
+                        target_cano[orig_key] = micro[orig_key]
+
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, fg_mask = self.loss_class(
+                        pred,
+                        target_nvs,
+                        step=self.step + self.resume_step,
+                        test_mode=False,
+                        return_fg_mask=True,
+                        conf_sigma_l1=None,
+                        conf_sigma_percl=None)
+                    log_rec3d_loss_dict(loss_dict)
+
+                if self.rec_cano:
+
+                    pred_cano = self.rec_model(latent=latent,
+                                               c=micro['c'],
+                                               behaviour='triplane_dec')
+
+                    with self.rec_model.no_sync():  # type: ignore
+
+                        fg_mask = target_cano['depth_mask'].unsqueeze(
+                            1).repeat_interleave(3, 1).float()
+
+                        loss_cano, loss_cano_dict = self.loss_class.calc_2d_rec_loss(
+                            pred_cano['image_raw'],
+                            target_cano['img'],
+                            fg_mask,
+                            step=self.step + self.resume_step,
+                            test_mode=False,
+                        )
+
+                    loss = loss + loss_cano
+
+                    # remove redundant log
+                    log_rec3d_loss_dict({
+                        f'cano_{k}': v
+                        for k, v in loss_cano_dict.items()
+                        #  if "loss" in k
+                    })
+
+            self.mp_trainer_rec.backward(loss)
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                if self.rec_cano:
+                    self.log_img(micro, pred, pred_cano)
+                else:
+                    self.log_img(micro, pred, None)
+
+    @th.inference_mode()
+    def log_img(self, micro, pred, pred_cano):
+        # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+        def norm_depth(pred_depth):  # to [-1,1]
+            # pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            return -(pred_depth * 2 - 1)
+
+        pred_img = pred['image_raw']
+        gt_img = micro['img']
+
+        # infer novel view also
+        # if self.loss_class.opt.symmetry_loss:
+        #     pred_nv_img = nvs_pred
+        # else:
+        # ! replace with novel view prediction
+
+        # ! log another novel-view prediction
+        # pred_nv_img = self.rec_model(
+        #     img=micro['img_to_encoder'],
+        #     c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+        # if 'depth' in micro:
+        gt_depth = micro['depth']
+        if gt_depth.ndim == 3:
+            gt_depth = gt_depth.unsqueeze(1)
+        gt_depth = norm_depth(gt_depth)
+        # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+        #                                           gt_depth.min())
+        # if True:
+        fg_mask = pred['image_mask'] * 2 - 1  # 0-1
+        input_fg_mask = pred_cano['image_mask'] * 2 - 1  # 0-1
+        if 'image_depth' in pred:
+            pred_depth = norm_depth(pred['image_depth'])
+            pred_nv_depth = norm_depth(pred_cano['image_depth'])
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+            pred_nv_depth = th.zeros_like(gt_depth)
+
+        if 'image_sr' in pred:
+            if pred['image_sr'].shape[-1] == 512:
+                pred_img = th.cat([self.pool_512(pred_img), pred['image_sr']],
+                                  dim=-1)
+                gt_img = th.cat([self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                pred_depth = self.pool_512(pred_depth)
+                gt_depth = self.pool_512(gt_depth)
+
+            elif pred['image_sr'].shape[-1] == 256:
+                pred_img = th.cat([self.pool_256(pred_img), pred['image_sr']],
+                                  dim=-1)
+                gt_img = th.cat([self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                pred_depth = self.pool_256(pred_depth)
+                gt_depth = self.pool_256(gt_depth)
+
+            else:
+                pred_img = th.cat([self.pool_128(pred_img), pred['image_sr']],
+                                  dim=-1)
+                gt_img = th.cat([self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                gt_depth = self.pool_128(gt_depth)
+                pred_depth = self.pool_128(pred_depth)
+        else:
+            gt_img = self.pool_64(gt_img)
+            gt_depth = self.pool_64(gt_depth)
+
+        pred_vis = th.cat([
+            pred_img,
+            pred_depth.repeat_interleave(3, dim=1),
+            fg_mask.repeat_interleave(3, dim=1),
+        ],
+                          dim=-1)  # B, 3, H, W
+
+        pred_vis_nv = th.cat([
+            pred_cano['image_raw'],
+            pred_nv_depth.repeat_interleave(3, dim=1),
+            input_fg_mask.repeat_interleave(3, dim=1),
+        ],
+                             dim=-1)  # B, 3, H, W
+
+        pred_vis = th.cat([pred_vis, pred_vis_nv], dim=-2)  # cat in H dim
+
+        gt_vis = th.cat([
+            gt_img,
+            gt_depth.repeat_interleave(3, dim=1),
+            th.zeros_like(gt_img)
+        ],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+        if 'conf_sigma' in pred:
+            gt_vis = th.cat([gt_vis, fg_mask], dim=-1)  # placeholder
+
+        # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+        vis = th.cat([gt_vis, pred_vis], dim=-2)
+        # .permute(
+        #     0, 2, 3, 1).cpu()
+        vis_tensor = torchvision.utils.make_grid(vis, nrow=vis.shape[-1] //
+                                                 64)  # HWC
+        torchvision.utils.save_image(
+            vis_tensor,
+            f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+            value_range=(-1, 1),
+            normalize=True)
+        # vis = vis.numpy() * 127.5 + 127.5
+        # vis = vis.clip(0, 255).astype(np.uint8)
+
+        # Image.fromarray(vis).save(
+        #     f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+        logger.log('log vis to: ',
+                   f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+        # self.writer.add_image(f'images',
+        #                       vis,
+        #                       self.step + self.resume_step,
+        #                       dataformats='HWC')
+
+
+# return pred
+
+
+class TrainLoop3DRecNVPatch(TrainLoop3DRecNV):
+    # add patch rendering
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+        # the rendrer
+        self.eg3d_model = self.rec_model.module.decoder.triplane_decoder  # type: ignore
+        # self.rec_cano = False
+        self.rec_cano = True
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # add patch sampling
+
+        self.mp_trainer_rec.zero_grad()
+        batch_size = batch['img_to_encoder'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            # ! sample rendering patch
+            target = {
+                **self.eg3d_model(
+                    c=micro['nv_c'],  # type: ignore
+                    ws=None,
+                    planes=None,
+                    sample_ray_only=True,
+                    fg_bbox=micro['nv_bbox']),  # rays o / dir
+            }
+
+            patch_rendering_resolution = self.eg3d_model.rendering_kwargs[
+                'patch_rendering_resolution']  # type: ignore
+            cropped_target = {
+                k:
+                th.empty_like(v)
+                [..., :patch_rendering_resolution, :patch_rendering_resolution]
+                if k not in [
+                    'ins_idx', 'img_to_encoder', 'img_sr', 'nv_img_to_encoder',
+                    'nv_img_sr', 'c'
+                ] else v
+                for k, v in micro.items()
+            }
+
+            # crop according to uv sampling
+            for j in range(micro['img'].shape[0]):
+                top, left, height, width = target['ray_bboxes'][
+                    j]  # list of tuple
+                # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+                for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+                    # target[key][i:i+1] = torchvision.transforms.functional.crop(
+                    # cropped_target[key][
+                    #     j:j + 1] = torchvision.transforms.functional.crop(
+                    #         micro[key][j:j + 1], top, left, height, width)
+
+                    cropped_target[f'{key}'][  # ! no nv_ here
+                        j:j + 1] = torchvision.transforms.functional.crop(
+                            micro[f'nv_{key}'][j:j + 1], top, left, height,
+                            width)
+
+            # target.update(cropped_target)
+
+            # wrap forward within amp
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                # target_nvs = {}
+                # target_cano = {}
+
+                latent = self.rec_model(img=micro['img_to_encoder'],
+                                        behaviour='enc_dec_wo_triplane')
+
+                pred_nv = self.rec_model(
+                    latent=latent,
+                    c=micro['nv_c'],  # predict novel view here
+                    behaviour='triplane_dec',
+                    ray_origins=target['ray_origins'],
+                    ray_directions=target['ray_directions'],
+                )
+
+                # ! directly retrieve from target
+                # for k, v in target.items():
+                #     if k[:2] == 'nv':
+                #         orig_key = k.replace('nv_', '')
+                #         target_nvs[orig_key] = v
+                #         target_cano[orig_key] = target[orig_key]
+
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, _ = self.loss_class(pred_nv,
+                                                         cropped_target,
+                                                         step=self.step +
+                                                         self.resume_step,
+                                                         test_mode=False,
+                                                         return_fg_mask=True,
+                                                         conf_sigma_l1=None,
+                                                         conf_sigma_percl=None)
+                    log_rec3d_loss_dict(loss_dict)
+
+                if self.rec_cano:
+
+                    cano_target = {
+                        **self.eg3d_model(
+                            c=micro['c'],  # type: ignore
+                            ws=None,
+                            planes=None,
+                            sample_ray_only=True,
+                            fg_bbox=micro['bbox']),  # rays o / dir
+                    }
+
+                    cano_cropped_target = {
+                        k: th.empty_like(v)
+                        for k, v in cropped_target.items()
+                    }
+
+                    for j in range(micro['img'].shape[0]):
+                        top, left, height, width = cano_target['ray_bboxes'][
+                            j]  # list of tuple
+                        # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+                        for key in ('img', 'depth_mask',
+                                    'depth'):  # type: ignore
+                            # target[key][i:i+1] = torchvision.transforms.functional.crop(
+                            cano_cropped_target[key][
+                                j:j +
+                                1] = torchvision.transforms.functional.crop(
+                                    micro[key][j:j + 1], top, left, height,
+                                    width)
+
+                    # cano_target.update(cano_cropped_target)
+
+                    pred_cano = self.rec_model(
+                        latent=latent,
+                        c=micro['c'],
+                        behaviour='triplane_dec',
+                        ray_origins=cano_target['ray_origins'],
+                        ray_directions=cano_target['ray_directions'],
+                    )
+
+                    with self.rec_model.no_sync():  # type: ignore
+
+                        fg_mask = cano_cropped_target['depth_mask'].unsqueeze(
+                            1).repeat_interleave(3, 1).float()
+
+                        loss_cano, loss_cano_dict = self.loss_class.calc_2d_rec_loss(
+                            pred_cano['image_raw'],
+                            cano_cropped_target['img'],
+                            fg_mask,
+                            step=self.step + self.resume_step,
+                            test_mode=False,
+                        )
+
+                    loss = loss + loss_cano
+
+                    # remove redundant log
+                    log_rec3d_loss_dict({
+                        f'cano_{k}': v
+                        for k, v in loss_cano_dict.items()
+                        #  if "loss" in k
+                    })
+
+            self.mp_trainer_rec.backward(loss)
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                self.log_patch_img(cropped_target, pred_nv, pred_cano)
+
+    @th.inference_mode()
+    def log_patch_img(self, micro, pred, pred_cano):
+        # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+        def norm_depth(pred_depth):  # to [-1,1]
+            # pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            return -(pred_depth * 2 - 1)
+
+        pred_img = pred['image_raw']
+        gt_img = micro['img']
+
+        # infer novel view also
+        # if self.loss_class.opt.symmetry_loss:
+        #     pred_nv_img = nvs_pred
+        # else:
+        # ! replace with novel view prediction
+
+        # ! log another novel-view prediction
+        # pred_nv_img = self.rec_model(
+        #     img=micro['img_to_encoder'],
+        #     c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+        # if 'depth' in micro:
+        gt_depth = micro['depth']
+        if gt_depth.ndim == 3:
+            gt_depth = gt_depth.unsqueeze(1)
+        gt_depth = norm_depth(gt_depth)
+        # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+        #                                           gt_depth.min())
+        # if True:
+        fg_mask = pred['image_mask'] * 2 - 1  # 0-1
+        input_fg_mask = pred_cano['image_mask'] * 2 - 1  # 0-1
+        if 'image_depth' in pred:
+            pred_depth = norm_depth(pred['image_depth'])
+            pred_cano_depth = norm_depth(pred_cano['image_depth'])
+        else:
+            pred_depth = th.zeros_like(gt_depth)
+            pred_cano_depth = th.zeros_like(gt_depth)
+
+        # if 'image_sr' in pred:
+        #     if pred['image_sr'].shape[-1] == 512:
+        #         pred_img = th.cat([self.pool_512(pred_img), pred['image_sr']],
+        #                           dim=-1)
+        #         gt_img = th.cat([self.pool_512(micro['img']), micro['img_sr']],
+        #                         dim=-1)
+        #         pred_depth = self.pool_512(pred_depth)
+        #         gt_depth = self.pool_512(gt_depth)
+
+        #     elif pred['image_sr'].shape[-1] == 256:
+        #         pred_img = th.cat([self.pool_256(pred_img), pred['image_sr']],
+        #                           dim=-1)
+        #         gt_img = th.cat([self.pool_256(micro['img']), micro['img_sr']],
+        #                         dim=-1)
+        #         pred_depth = self.pool_256(pred_depth)
+        #         gt_depth = self.pool_256(gt_depth)
+
+        #     else:
+        #         pred_img = th.cat([self.pool_128(pred_img), pred['image_sr']],
+        #                           dim=-1)
+        #         gt_img = th.cat([self.pool_128(micro['img']), micro['img_sr']],
+        #                         dim=-1)
+        #         gt_depth = self.pool_128(gt_depth)
+        #         pred_depth = self.pool_128(pred_depth)
+        # else:
+        #     gt_img = self.pool_64(gt_img)
+        #     gt_depth = self.pool_64(gt_depth)
+
+        pred_vis = th.cat([
+            pred_img,
+            pred_depth.repeat_interleave(3, dim=1),
+            fg_mask.repeat_interleave(3, dim=1),
+        ],
+                          dim=-1)  # B, 3, H, W
+
+        pred_vis_nv = th.cat([
+            pred_cano['image_raw'],
+            pred_cano_depth.repeat_interleave(3, dim=1),
+            input_fg_mask.repeat_interleave(3, dim=1),
+        ],
+                             dim=-1)  # B, 3, H, W
+
+        pred_vis = th.cat([pred_vis, pred_vis_nv], dim=-2)  # cat in H dim
+
+        gt_vis = th.cat([
+            gt_img,
+            gt_depth.repeat_interleave(3, dim=1),
+            th.zeros_like(gt_img)
+        ],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+        # if 'conf_sigma' in pred:
+        #     gt_vis = th.cat([gt_vis, fg_mask], dim=-1)  # placeholder
+
+        # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+        # st()
+        vis = th.cat([gt_vis, pred_vis], dim=-2)
+        # .permute(
+        #     0, 2, 3, 1).cpu()
+        vis_tensor = torchvision.utils.make_grid(vis, nrow=vis.shape[-1] //
+                                                 64)  # HWC
+        torchvision.utils.save_image(
+            vis_tensor,
+            f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+            value_range=(-1, 1),
+            normalize=True)
+
+        logger.log('log vis to: ',
+                   f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+        # self.writer.add_image(f'images',
+        #                       vis,
+        #                       self.step + self.resume_step,
+        #                       dataformats='HWC')
+
+
+class TrainLoop3DRecNVPatchSingleForward(TrainLoop3DRecNVPatch):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # add patch sampling
+
+        self.mp_trainer_rec.zero_grad()
+        batch_size = batch['img_to_encoder'].shape[0]
+
+        batch.pop('caption')  # not required
+        batch.pop('ins')  # not required
+        batch.pop('nv_caption')  # not required
+        batch.pop('nv_ins')  # not required
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v[i:i + self.microbatch]
+                for k, v in batch.items()
+            }
+
+            # ! sample rendering patch
+            target = {
+                **self.eg3d_model(
+                    c=micro['nv_c'],  # type: ignore
+                    ws=None,
+                    planes=None,
+                    sample_ray_only=True,
+                    fg_bbox=micro['nv_bbox']),  # rays o / dir
+            }
+
+            patch_rendering_resolution = self.eg3d_model.rendering_kwargs[
+                'patch_rendering_resolution']  # type: ignore
+            cropped_target = {
+                k:
+                th.empty_like(v)
+                [..., :patch_rendering_resolution, :patch_rendering_resolution]
+                if k not in [
+                    'ins_idx', 'img_to_encoder', 'img_sr', 'nv_img_to_encoder',
+                    'nv_img_sr', 'c', 'caption', 'nv_caption'
+                ] else v
+                for k, v in micro.items()
+            }
+
+            # crop according to uv sampling
+            for j in range(micro['img'].shape[0]):
+                top, left, height, width = target['ray_bboxes'][
+                    j]  # list of tuple
+                # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+                for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+                    # target[key][i:i+1] = torchvision.transforms.functional.crop(
+                    # cropped_target[key][
+                    #     j:j + 1] = torchvision.transforms.functional.crop(
+                    #         micro[key][j:j + 1], top, left, height, width)
+
+                    cropped_target[f'{key}'][  # ! no nv_ here
+                        j:j + 1] = torchvision.transforms.functional.crop(
+                            micro[f'nv_{key}'][j:j + 1], top, left, height,
+                            width)
+
+            # ! cano view loss
+            # cano_target = {
+            #     **self.eg3d_model(
+            #         c=micro['c'],  # type: ignore
+            #         ws=None,
+            #         planes=None,
+            #         sample_ray_only=True,
+            #         fg_bbox=micro['bbox']),  # rays o / dir
+            # }
+
+            # cano_cropped_target = {
+            #     k: th.empty_like(v)
+            #     for k, v in cropped_target.items()
+            # }
+
+            # for j in range(micro['img'].shape[0]):
+            #     top, left, height, width = cano_target['ray_bboxes'][
+            #         j]  # list of tuple
+            #     # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+            #     for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+            #         # target[key][i:i+1] = torchvision.transforms.functional.crop(
+            #         cano_cropped_target[key][
+            #             j:j + 1] = torchvision.transforms.functional.crop(
+            #                 micro[key][j:j + 1], top, left, height, width)
+
+            # ! vit no amp
+            latent = self.rec_model(img=micro['img_to_encoder'].to(self.dtype),
+                                    behaviour='enc_dec_wo_triplane')
+
+            # wrap forward within amp
+            with th.autocast(device_type='cuda',
+                            #  dtype=th.float16,
+                             dtype=th.bfloat16, # avoid NAN
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                # c = th.cat([micro['nv_c'], micro['c']]),  # predict novel view here
+                # c = th.cat([micro['nv_c'].repeat(3, 1), micro['c']]),  # predict novel view here
+                instance_mv_num = batch_size // 4  # 4 pairs by default
+                # instance_mv_num = 4
+                # ! roll views for multi-view supervision
+                c = th.cat([
+                    micro['nv_c'].roll(instance_mv_num * i, dims=0)
+                    for i in range(1, 4)
+                ]
+                           # + [micro['c']]
+                           )  # predict novel view here
+
+                ray_origins = th.cat(
+                    [
+                        target['ray_origins'].roll(instance_mv_num * i, dims=0)
+                        for i in range(1, 4)
+                    ]
+                    # + [cano_target['ray_origins'] ]
+                    ,
+                    0)
+
+                ray_directions = th.cat([
+                    target['ray_directions'].roll(instance_mv_num * i, dims=0)
+                    for i in range(1, 4)
+                ]
+                                        # + [cano_target['ray_directions'] ]
+                                        )
+
+                pred_nv_cano = self.rec_model(
+                    # latent=latent.expand(2,),
+                    latent={
+                        'latent_after_vit': # ! triplane for rendering
+                        # latent['latent_after_vit'].repeat(2, 1, 1, 1)
+                        latent['latent_after_vit'].repeat(3, 1, 1, 1)
+                    },
+                    c=c,
+                    behaviour='triplane_dec',
+                    # ray_origins=target['ray_origins'],
+                    # ray_directions=target['ray_directions'],
+                    ray_origins=ray_origins,
+                    ray_directions=ray_directions,
+                )
+
+                pred_nv_cano.update(
+                    latent
+                )  # torchvision.utils.save_image(pred_nv_cano['image_raw'], 'pred.png', normalize=True)
+                # gt = {
+                #     k: th.cat([v, cano_cropped_target[k]], 0)
+                #     for k, v in cropped_target.items()
+                # }
+                gt = {
+                    k:
+                    th.cat(
+                        [
+                            v.roll(instance_mv_num * i, dims=0)
+                            for i in range(1, 4)
+                        ]
+                        # + [cano_cropped_target[k] ]
+                        ,
+                        0)
+                    for k, v in cropped_target.items()
+                }  # torchvision.utils.save_image(gt['img'], 'gt.png', normalize=True)
+
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, _ = self.loss_class(
+                        pred_nv_cano,
+                        gt,  # prepare merged data
+                        step=self.step + self.resume_step,
+                        test_mode=False,
+                        return_fg_mask=True,
+                        conf_sigma_l1=None,
+                        conf_sigma_percl=None)
+                    log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(loss)
+
+            # for name, p in self.rec_model.named_parameters():
+            #     if p.grad is None:
+            #         logger.log(f"found rec unused param: {name}")
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                micro_bs = micro['img_to_encoder'].shape[0]
+                self.log_patch_img( # record one cano view and one novel view
+                    cropped_target,
+                    {
+                        k: pred_nv_cano[k][-micro_bs:]
+                        for k in ['image_raw', 'image_depth', 'image_mask']
+                    },
+                    {
+                        k: pred_nv_cano[k][:micro_bs]
+                        for k in ['image_raw', 'image_depth', 'image_mask']
+                    },
+                )
+
+    def eval_loop(self):
+        return super().eval_loop()
+
+    @th.inference_mode()
+    # def eval_loop(self, c_list:list):
+    def eval_novelview_loop_old(self, camera=None):
+        # novel view synthesis given evaluation camera trajectory
+
+        all_loss_dict = []
+        novel_view_micro = {}
+
+        # ! randomly inference an instance
+
+        export_mesh = True
+        if export_mesh:
+            Path(f'{logger.get_dir()}/FID_Cals/').mkdir(parents=True,
+                                                        exist_ok=True)
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+
+        batch = {}
+        # if camera is not None:
+        #     # batch['c'] = camera.to(batch['c'].device())
+        #     batch['c'] = camera.clone()
+        # else:
+        #     batch =
+
+        for eval_idx, render_reference in enumerate(tqdm(self.eval_data)):
+
+            if eval_idx > 500:
+                break
+
+            video_out = imageio.get_writer(
+                f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}_{eval_idx}.mp4',
+                mode='I',
+                fps=25,
+                codec='libx264')
+
+            with open(
+                    f'{logger.get_dir()}/triplane_{self.step+self.resume_step}_{eval_idx}_caption.txt',
+                    'w') as f:
+                f.write(render_reference['caption'])
+
+            for key in ['ins', 'bbox', 'caption']:
+                if key in render_reference:
+                    render_reference.pop(key)
+
+            real_flag = False
+            mv_flag = False  # TODO, use full-instance for evaluation? Calculate the metrics.
+            if render_reference['c'].shape[:2] == (1, 40):
+                real_flag = True
+                # real img monocular reconstruction
+                # compat lst for enumerate
+                render_reference = [{
+                    k: v[0][idx:idx + 1]
+                    for k, v in render_reference.items()
+                } for idx in range(40)]
+
+            elif render_reference['c'].shape[0] == 8:
+                mv_flag = True
+
+                render_reference = {
+                    k: v[:4]
+                    for k, v in render_reference.items()
+                }
+
+                # save gt
+                torchvision.utils.save_image(
+                    render_reference[0:4]['img'],
+                    logger.get_dir() + '/FID_Cals/{}_inp.png'.format(eval_idx),
+                    padding=0,
+                    normalize=True,
+                    value_range=(-1, 1),
+                )
+                # torchvision.utils.save_image(render_reference[4:8]['img'],
+                #     logger.get_dir() + '/FID_Cals/{}_inp2.png'.format(eval_idx),
+                #     padding=0,
+                #     normalize=True,
+                #     value_range=(-1,1),
+                # )
+
+            else:
+                # compat lst for enumerate
+                st()
+                render_reference = [{
+                    k: v[idx:idx + 1]
+                    for k, v in render_reference.items()
+                } for idx in range(40)]
+
+                # ! single-view version
+                render_reference[0]['img_to_encoder'] = render_reference[14][
+                    'img_to_encoder']  # encode side view
+                render_reference[0]['img'] = render_reference[14][
+                    'img']  # encode side view
+
+                # save gt
+                torchvision.utils.save_image(
+                    render_reference[0]['img'],
+                    logger.get_dir() + '/FID_Cals/{}_gt.png'.format(eval_idx),
+                    padding=0,
+                    normalize=True,
+                    value_range=(-1, 1))
+
+            # ! TODO, merge with render_video_given_triplane later
+            for i, batch in enumerate(render_reference):
+                # for i in range(0, 8, self.microbatch):
+                # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+                micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+                st()
+                if i == 0:
+                    if mv_flag:
+                        novel_view_micro = None
+                    else:
+                        novel_view_micro = {
+                            k:
+                            v[0:1].to(dist_util.dev()).repeat_interleave(
+                                # v[14:15].to(dist_util.dev()).repeat_interleave(
+                                micro['img'].shape[0],
+                                0) if isinstance(v, th.Tensor) else v[0:1]
+                            for k, v in batch.items()
+                        }
+
+                else:
+                    if i == 1:
+
+                        # ! output mesh
+                        if export_mesh:
+
+                            # ! get planes first
+                            # self.latent_name = 'latent_normalized'  # normalized triplane latent
+
+                            # ddpm_latent = {
+                            #     self.latent_name: planes,
+                            # }
+                            # ddpm_latent.update(self.rec_model(latent=ddpm_latent, behaviour='decode_after_vae_no_render'))
+
+                            # mesh_size = 512
+                            # mesh_size = 256
+                            mesh_size = 384
+                            # mesh_size = 320
+                            # mesh_thres = 3 # TODO, requires tuning
+                            # mesh_thres = 5 # TODO, requires tuning
+                            mesh_thres = 10  # TODO, requires tuning
+                            import mcubes
+                            import trimesh
+                            dump_path = f'{logger.get_dir()}/mesh/'
+
+                            os.makedirs(dump_path, exist_ok=True)
+
+                            grid_out = self.rec_model(
+                                latent=pred,
+                                grid_size=mesh_size,
+                                behaviour='triplane_decode_grid',
+                            )
+
+                            vtx, faces = mcubes.marching_cubes(
+                                grid_out['sigma'].squeeze(0).squeeze(
+                                    -1).cpu().numpy(), mesh_thres)
+                            vtx = vtx / (mesh_size - 1) * 2 - 1
+
+                            # vtx_tensor = th.tensor(vtx, dtype=th.float32, device=dist_util.dev()).unsqueeze(0)
+                            # vtx_colors = self.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].squeeze(0).cpu().numpy()  # (0, 1)
+                            # vtx_colors = (vtx_colors * 255).astype(np.uint8)
+
+                            # mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+                            mesh = trimesh.Trimesh(
+                                vertices=vtx,
+                                faces=faces,
+                            )
+
+                            mesh_dump_path = os.path.join(
+                                dump_path, f'{eval_idx}.ply')
+                            mesh.export(mesh_dump_path, 'ply')
+
+                            print(f"Mesh dumped to {dump_path}")
+                            del grid_out, mesh
+                            th.cuda.empty_cache()
+                            # return
+                            # st()
+
+                    # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                    novel_view_micro = {
+                        k:
+                        v[0:1].to(dist_util.dev()).repeat_interleave(
+                            micro['img'].shape[0], 0)
+                        for k, v in novel_view_micro.items()
+                    }
+
+                pred = self.rec_model(img=novel_view_micro['img_to_encoder'],
+                                      c=micro['c'])  # pred: (B, 3, 64, 64)
+                # target = {
+                #     'img': micro['img'],
+                #     'depth': micro['depth'],
+                #     'depth_mask': micro['depth_mask']
+                # }
+                # targe
+
+                # if not export_mesh:
+                if not real_flag:
+                    _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+                    all_loss_dict.append(loss_dict)
+
+                # ! move to other places, add tensorboard
+
+                # pred_vis = th.cat([
+                #     pred['image_raw'],
+                #     -pred['image_depth'].repeat_interleave(3, dim=1)
+                # ],
+                #                   dim=-1)
+
+                # normalize depth
+                # if True:
+                pred_depth = pred['image_depth']
+                pred_depth = (pred_depth - pred_depth.min()) / (
+                    pred_depth.max() - pred_depth.min())
+                if 'image_sr' in pred:
+
+                    if pred['image_sr'].shape[-1] == 512:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_512(pred['image_raw']), pred['image_sr'],
+                            self.pool_512(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                    elif pred['image_sr'].shape[-1] == 256:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_256(pred['image_raw']), pred['image_sr'],
+                            self.pool_256(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                    else:
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_128(pred['image_raw']),
+                            self.pool_128(pred['image_sr']),
+                            self.pool_128(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                else:
+                    # pred_vis = th.cat([
+                    #     self.pool_64(micro['img']), pred['image_raw'],
+                    #     pred_depth.repeat_interleave(3, dim=1)
+                    # ],
+                    #                   dim=-1)  # B, 3, H, W
+
+                    pooled_depth = self.pool_128(pred_depth).repeat_interleave(
+                        3, dim=1)
+                    pred_vis = th.cat(
+                        [
+                            # self.pool_128(micro['img']),
+                            self.pool_128(novel_view_micro['img']
+                                          ),  # use the input here
+                            self.pool_128(pred['image_raw']),
+                            pooled_depth,
+                        ],
+                        dim=-1)  # B, 3, H, W
+
+                vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+                vis = vis * 127.5 + 127.5
+                vis = vis.clip(0, 255).astype(np.uint8)
+
+                if export_mesh:
+                    # save image
+                    torchvision.utils.save_image(
+                        pred['image_raw'],
+                        logger.get_dir() +
+                        '/FID_Cals/{}_{}.png'.format(eval_idx, i),
+                        padding=0,
+                        normalize=True,
+                        value_range=(-1, 1))
+
+                    torchvision.utils.save_image(
+                        pooled_depth,
+                        logger.get_dir() +
+                        '/FID_Cals/{}_{}_dpeth.png'.format(eval_idx, i),
+                        padding=0,
+                        normalize=True,
+                        value_range=(0, 1))
+
+                # st()
+
+                for j in range(vis.shape[0]):
+                    video_out.append_data(vis[j])
+
+            video_out.close()
+
+        # if not export_mesh:
+        if not real_flag or mv_flag:
+            val_scores_for_logging = calc_average_loss(all_loss_dict)
+            with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+                      'a') as f:
+                json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+            # * log to tensorboard
+            for k, v in val_scores_for_logging.items():
+                self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+                                       self.step + self.resume_step)
+
+        del video_out
+        # del pred_vis
+        # del pred
+
+        th.cuda.empty_cache()
+
+    @th.inference_mode()
+    # def eval_loop(self, c_list:list):
+    def eval_novelview_loop(self, camera=None, save_latent=False):
+        # novel view synthesis given evaluation camera trajectory
+        if save_latent:  # for diffusion learning
+            latent_dir = Path(f'{logger.get_dir()}/latent_dir')
+            latent_dir.mkdir(exist_ok=True, parents=True)
+
+            # wds_path = os.path.join(logger.get_dir(), 'latent_dir',
+            #                         f'wds-%06d.tar')
+            # sink = wds.ShardWriter(wds_path, start_shard=0)
+
+        # eval_batch_size = 20
+        # eval_batch_size = 1
+        eval_batch_size = 40  # ! for i23d
+
+        latent_rec_statistics = False
+
+        for eval_idx, micro in enumerate(tqdm(self.eval_data)):
+
+            # if eval_idx > 500:
+            #     break
+
+            latent = self.rec_model(
+                img=micro['img_to_encoder'],
+                behaviour='encoder_vae')  # pred: (B, 3, 64, 64)
+            # torchvision.utils.save_image(micro['img'], 'inp.jpg')
+            if micro['img'].shape[0] == 40:
+                assert eval_batch_size == 40
+
+            if save_latent:
+                # np.save(f'{logger.get_dir()}/latent_dir/{eval_idx}.npy', latent[self.latent_name].cpu().numpy())
+
+                latent_save_dir = f'{logger.get_dir()}/latent_dir/{micro["ins"][0]}'
+                Path(latent_save_dir).mkdir(parents=True, exist_ok=True)
+
+                np.save(f'{latent_save_dir}/latent.npy',
+                        latent[self.latent_name][0].cpu().numpy())
+                assert all([
+                    micro['ins'][0] == micro['ins'][i]
+                    for i in range(micro['c'].shape[0])
+                ])  # ! assert same instance
+
+                # for i in range(micro['img'].shape[0]):
+
+                #     compressed_sample = {
+                #         'latent':latent[self.latent_name][0].cpu().numpy(), # 12 32 32
+                #         'caption': micro['caption'][0].encode('utf-8'),
+                #         'ins': micro['ins'][0].encode('utf-8'),
+                #         'c': micro['c'][i].cpu().numpy(),
+                #         'img': micro['img'][i].cpu().numpy() # 128x128, for diffusion log
+                #     }
+
+                #     sink.write({
+                #         "__key__": f"sample_{eval_idx*eval_batch_size+i:07d}",
+                #         'sample.pyd': compressed_sample
+                #     })
+
+            if latent_rec_statistics:
+                gen_imgs = self.render_video_given_triplane(
+                    latent[self.latent_name],
+                    self.rec_model,  # compatible with join_model
+                    name_prefix=f'{self.step + self.resume_step}_{eval_idx}',
+                    save_img=False,
+                    render_reference={'c': micro['c']},
+                    save_mesh=False,
+                    render_reference_length=4,
+                    return_gen_imgs=True)
+                rec_psnr = psnr((micro['img'] / 2 + 0.5),
+                                (gen_imgs.cpu() / 2 + 0.5), 1.0)
+                with open(
+                        os.path.join(logger.get_dir(),
+                                     'four_view_rec_psnr.json'), 'a') as f:
+                    json.dump(
+                        {
+                            f'{eval_idx}': {
+                                'ins': micro["ins"][0],
+                                'psnr': rec_psnr.item(),
+                            }
+                        }, f)
+                #  save to json
+
+            elif eval_idx < 30:
+                # if False:
+                self.render_video_given_triplane(
+                    latent[self.latent_name],
+                    self.rec_model,  # compatible with join_model
+                    name_prefix=f'{self.step + self.resume_step}_{micro["ins"][0].split("/")[0]}_{eval_idx}',
+                    save_img=False,
+                    render_reference={'c': camera},
+                    save_mesh=True)
+
+
+class TrainLoop3DRecNVPatchSingleForwardMV(TrainLoop3DRecNVPatchSingleForward):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+
+    def forward_backward(self, batch, behaviour='g_step', *args, **kwargs):
+        # add patch sampling
+
+        if behaviour == 'g_step':
+            self.mp_trainer_rec.zero_grad()
+        else:
+            self.mp_trainer_disc.zero_grad()
+
+        batch_size = batch['img_to_encoder'].shape[0]
+
+        batch.pop('caption')  # not required
+        batch.pop('ins')  # not required
+        batch.pop('nv_caption')  # not required
+        batch.pop('nv_ins')  # not required
+
+        if '__key__' in batch.keys():
+            batch.pop('__key__')
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k:
+                v[i:i + self.microbatch].to(dist_util.dev()) if isinstance(
+                    v, th.Tensor) else v[i:i + self.microbatch]
+                for k, v in batch.items()
+            }
+
+            # ! sample rendering patch
+            # nv_c = th.cat([micro['nv_c'], micro['c']])
+            nv_c = th.cat([micro['nv_c'], micro['c']])
+            # nv_c = micro['nv_c']
+            target = {
+                **self.eg3d_model(
+                    c=nv_c,  # type: ignore
+                    ws=None,
+                    planes=None,
+                    sample_ray_only=True,
+                    fg_bbox=th.cat([micro['nv_bbox'], micro['bbox']])),  # rays o / dir
+            }
+
+            patch_rendering_resolution = self.eg3d_model.rendering_kwargs[
+                'patch_rendering_resolution']  # type: ignore
+            cropped_target = {
+                k:
+                th.empty_like(v).repeat_interleave(2, 0)
+                # th.empty_like(v).repeat_interleave(1, 0)
+                [..., :patch_rendering_resolution, :patch_rendering_resolution]
+                if k not in [
+                    'ins_idx', 'img_to_encoder', 'img_sr', 'nv_img_to_encoder',
+                    'nv_img_sr', 'c', 'caption', 'nv_caption'
+                ] else v
+                for k, v in micro.items()
+            }
+
+            # crop according to uv sampling
+            for j in range(2 * self.microbatch):
+                top, left, height, width = target['ray_bboxes'][
+                    j]  # list of tuple
+                # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+                for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+
+                    if j < self.microbatch:
+                        cropped_target[f'{key}'][  # ! no nv_ here
+                            j:j + 1] = torchvision.transforms.functional.crop(
+                                micro[f'nv_{key}'][j:j + 1], top, left, height,
+                                width)
+                    else:
+                        cropped_target[f'{key}'][  # ! no nv_ here
+                            j:j + 1] = torchvision.transforms.functional.crop(
+                                micro[f'{key}'][j - self.microbatch:j -
+                                                self.microbatch + 1], top,
+                                left, height, width)
+
+            # for j in range(batch_size, 2*batch_size, 1):
+            #     top, left, height, width = target['ray_bboxes'][
+            #         j]  # list of tuple
+            #     # for key in ('img', 'depth_mask', 'depth', 'depth_mask_sr'): # type: ignore
+            #     for key in ('img', 'depth_mask', 'depth'):  # type: ignore
+
+            #         cropped_target[f'{key}'][  # ! no nv_ here
+            #             j:j + 1] = torchvision.transforms.functional.crop(
+            #                 micro[f'{key}'][j-batch_size:j-batch_size + 1], top, left, height,
+            #                 width)
+
+            # wrap forward within amp
+            with th.autocast(device_type='cuda',
+                             dtype=self.dtype,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                # c = th.cat([micro['nv_c'], micro['c']]),  # predict novel view here
+                # c = th.cat([micro['nv_c'].repeat(3, 1), micro['c']]),  # predict novel view here
+                # instance_mv_num = batch_size // 4  # 4 pairs by default
+                # instance_mv_num = 4
+                # ! roll views for multi-view supervision
+                # c = micro['nv_c']
+
+                # ! vit no amp
+                latent = self.rec_model(img=micro['img_to_encoder'].to(self.dtype),
+                                        behaviour='enc_dec_wo_triplane')
+
+                # # ! disable amp in rendering and loss
+                # with th.autocast(device_type='cuda',
+                #                 dtype=th.float16,
+                #                 enabled=False):
+
+                ray_origins = target['ray_origins']
+                ray_directions = target['ray_directions']
+
+                pred_nv_cano = self.rec_model(
+                    # latent=latent.expand(2,),
+                    latent={
+                        'latent_after_vit':  # ! triplane for rendering
+                        # latent['latent_after_vit'].repeat_interleave(4, dim=0).repeat(2,1,1,1)  # NV=4
+                        latent['latent_after_vit'].repeat_interleave(6, dim=0).repeat(2,1,1,1)  # NV=6
+                        # latent['latent_after_vit'].repeat_interleave(10, dim=0).repeat(2,1,1,1)  # NV=4
+                        # latent['latent_after_vit'].repeat_interleave(8, dim=0)  # NV=4
+                    },
+                    c=nv_c,
+                    behaviour='triplane_dec',
+                    ray_origins=ray_origins,
+                    ray_directions=ray_directions,
+                )
+
+                pred_nv_cano.update(
+                    latent
+                )  # torchvision.utils.save_image(pred_nv_cano['image_raw'], 'pred.png', normalize=True)
+                gt = cropped_target
+
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, _ = self.loss_class(
+                        pred_nv_cano,
+                        gt,  # prepare merged data
+                        step=self.step + self.resume_step,
+                        test_mode=False,
+                        return_fg_mask=True,
+                        behaviour=behaviour,
+                        conf_sigma_l1=None,
+                        conf_sigma_percl=None, 
+                        # dtype=self.dtype
+                        )
+                    log_rec3d_loss_dict(loss_dict)
+
+            if behaviour == 'g_step':
+                self.mp_trainer_rec.backward(loss)
+            else:
+                self.mp_trainer_disc.backward(loss)
+
+            # for name, p in self.rec_model.named_parameters():
+            #     if p.grad is None:
+            #         logger.log(f"found rec unused param: {name}")
+            # torchvision.utils.save_image(cropped_target['img'], 'gt.png', normalize=True)
+            # torchvision.utils.save_image( pred_nv_cano['image_raw'], 'pred.png', normalize=True)
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0 and i == 0 and behaviour == 'g_step':
+                try:
+                    torchvision.utils.save_image(
+                        th.cat(
+                            [cropped_target['img'], pred_nv_cano['image_raw']
+                             ], ),
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+                        normalize=True, nrow=6*2)
+
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+                except Exception as e:
+                    logger.log(e)
+
+                # micro_bs = micro['img_to_encoder'].shape[0]
+                # self.log_patch_img( # record one cano view and one novel view
+                #     cropped_target,
+                #     {
+                #         k: pred_nv_cano[k][0:1]
+                #         for k in ['image_raw', 'image_depth', 'image_mask']
+                #     },
+                #     {
+                #         k: pred_nv_cano[k][1:2]
+                #         for k in ['image_raw', 'image_depth', 'image_mask']
+                #     },
+                # )
+
+    # def save(self):
+    #     return super().save()
+
+
+class TrainLoop3DRecNVPatchSingleForwardMVAdvLoss(
+        TrainLoop3DRecNVPatchSingleForwardMV):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+
+        # create discriminator
+        disc_params = self.loss_class.get_trainable_parameters()
+
+        self.mp_trainer_disc = MixedPrecisionTrainer(
+            model=self.loss_class.discriminator,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='disc',
+            use_amp=use_amp,
+            model_params=disc_params)
+
+        # st() # check self.lr
+        self.opt_disc = AdamW(
+            self.mp_trainer_disc.master_params,
+            lr=self.lr,  # follow sd code base
+            betas=(0, 0.999),
+            eps=1e-8)
+
+        # TODO, is loss cls already in the DDP?
+        if self.use_ddp:
+            self.ddp_disc = DDP(
+                self.loss_class.discriminator,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            self.ddp_disc = self.loss_class.discriminator
+
+    # def run_st
+
+    # def run_step(self, batch, *args):
+    #     self.forward_backward(batch)
+    #     took_step = self.mp_trainer_rec.optimize(self.opt)
+    #     if took_step:
+    #         self._update_ema()
+    #     self._anneal_lr()
+    #     self.log_step()
+
+    def save(self, mp_trainer=None, model_name='rec'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, mp_trainer.master_params)
+
+        dist.barrier()
+
+    # ! load disc
+    def _load_and_sync_parameters(self, submodule_name=''):
+        super()._load_and_sync_parameters(submodule_name)
+        # load disc
+
+        resume_checkpoint = self.resume_checkpoint.replace(
+            'rec', 'disc')  # * default behaviour
+        if os.path.exists(resume_checkpoint):
+            if dist_util.get_rank() == 0:
+                logger.log(
+                    f"loading disc model from checkpoint: {resume_checkpoint}..."
+                )
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                model_state_dict = self.loss_class.discriminator.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys():
+                        if v.size() == model_state_dict[k].size():
+                            model_state_dict[k] = v
+                            # model_state_dict[k].copy_(v)
+                        else:
+                            logger.log('!!!! partially load: ', k, ": ",
+                                       v.size(), "state_dict: ",
+                                       model_state_dict[k].size())
+
+            if dist_util.get_world_size() > 1:
+                # dist_util.sync_params(self.model.named_parameters())
+                dist_util.sync_params(
+                    self.loss_class.get_trainable_parameters())
+                logger.log('synced disc params')
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step':
+            self.forward_backward(batch, behaviour='g_step')
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step':
+            self.forward_backward(batch, behaviour='d_step')
+            _ = self.mp_trainer_disc.optimize(self.opt_disc)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self, batch=None):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step')
+
+            if self.step % 1000 == 0:
+                dist_util.synchronize()
+                if self.step % 5000 == 0:
+                    th.cuda.empty_cache()  # avoid memory leak
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                if dist_util.get_rank() == 0:
+                    try:
+                        self.eval_loop()
+                    except Exception as e:
+                        logger.log(e)
+                dist_util.synchronize()
+
+            # if self.step % self.save_interval == 0 and self.step != 0:
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_disc,
+                          self.mp_trainer_disc.model_name)
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                logger.log('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step -
+                        1) % self.save_interval != 0 and self.step != 1:
+                    self.save()
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        # if (self.step - 1) % self.save_interval != 0 and self.step != 1:
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()  # save rec
+            self.save(self.mp_trainer_disc, self.mp_trainer_disc.model_name)
+
+
+class TrainLoop3DRecNVPatchSingleForwardMV_NoCrop(
+        TrainLoop3DRecNVPatchSingleForwardMV):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 num_frames=4,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+
+        self.num_frames = num_frames
+        self.ray_sampler = RaySampler()
+
+        print(self.opt)
+
+        # ! requires tuning
+        N = 768 # hyp param, overfitting now
+        # self.scale_expected_threshold = (1 / (N/2)) ** 0.5 * 0.45
+        self.scale_expected_threshold = 0.0075
+        self.latent_name = 'latent_normalized'  # normalized triplane latent
+
+
+        # to transform to 3dgs
+        self.gs_bg_color=th.tensor([1,1,1], dtype=th.float32, device=dist_util.dev())
+        self.post_process = PostProcess(
+            384,
+            384,
+            imgnet_normalize=True,
+            plucker_embedding=True,
+            decode_encode_img_only=False,
+            mv_input=True,
+            split_chunk_input=16,
+            duplicate_sample=True,
+            append_depth=False,
+            append_xyz=False,
+            gs_cam_format=True,
+            orthog_duplicate=False,
+            frame_0_as_canonical=False,
+            pcd_path='pcd_path',
+            load_pcd=True,
+            split_chunk_size=16,
+        )
+
+        self.zfar = 100.0
+        self.znear = 0.01
+
+
+    # def _init_optim_groups(self, kwargs):
+    #     return super()._init_optim_groups({**kwargs, 'ignore_encoder': True}) # freeze MVEncoder to accelerate training.
+
+    def forward_backward(self, batch, behaviour='g_step', *args, **kwargs):
+        # add patch sampling
+
+        self.mp_trainer_rec.zero_grad()
+        batch_size = batch['img_to_encoder'].shape[0]
+
+        batch.pop('caption')  # not required
+        ins = batch.pop('ins')  # not required
+
+        if '__key__' in batch.keys():
+            batch.pop('__key__')
+
+        assert isinstance(batch['c'], dict)
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {}
+
+            for k, v in batch.items():  # grad acc
+                if isinstance(v, th.Tensor):
+                    micro[k] = v[i:i + self.microbatch].to(dist_util.dev())
+                elif isinstance(v, list):
+                    micro[k] = v[i:i + self.microbatch]
+                elif isinstance(v, dict):  #
+                    assert k in ['c', 'nv_c']
+                    micro[k] = {
+                        key:
+                        value[i:i + self.microbatch].to(dist_util.dev()) if
+                        isinstance(value, th.Tensor) else value  # can be float
+                        for key, value in v.items()
+                    }
+
+            assert micro['img_to_encoder'].shape[1] == 15
+            micro['normal'] = micro['img_to_encoder'][:, 3:6]
+            micro['nv_normal'] = micro['nv_img_to_encoder'][:, 3:6]
+
+            # ! concat nv_c to render N+N views
+
+            indices = np.random.permutation(self.num_frames)
+            indices, indices_nv = indices[:4], indices[-4:] # make sure thorough pose converage.
+            # indices, indices_nv = indices[:2], indices[-2:] # ! 2+2 views for supervision, as in gs-lrm. 
+            # indices_nv = np.random.permutation(self.num_frames)[:6] # randomly pick 4+4 views for supervision.
+
+            # indices = np.arange(self.num_frames)
+            # indices_nv = np.arange(self.num_frames)
+
+            nv_c = {}
+            for key in micro['c'].keys():
+                if isinstance(micro['c'][key], th.Tensor):
+                    nv_c[key] = th.cat([micro['c'][key][:, indices], micro['nv_c'][key][:, indices_nv]],
+                                       1)  # B 2V ...
+                else:
+                    nv_c[key] = micro['c'][key]  # float, will remove later
+
+            target = {}
+
+            for key in ('img', 'depth_mask', 'depth', 'normal',):  # type: ignore
+                # st()
+                target[key] = th.cat([
+                    rearrange(micro[key], '(B V) ... -> B V ...', V=self.num_frames)[:, indices],
+                    rearrange(micro[f'nv_{key}'], '(B V) ... -> B V ...', V=self.num_frames)[:, indices_nv]
+                    # rearrange(micro[key][:, indices], '(B V) ... -> B V ...', V=4),
+                    # rearrange(micro[f'nv_{key}'][:, indices], '(B V) ... -> B V ...', V=4)
+                ], 1)  # B 2*V H W
+                target[key] = rearrange(target[key],
+                                        'B V ... -> (B V) ...')  # concat
+
+            # st()
+
+            # wrap forward within amp
+            with th.autocast(device_type='cuda',
+                             dtype=self.dtype,
+                             enabled=self.mp_trainer_rec.use_amp):
+                
+                # ! vit no amp
+                # with profile(activities=[
+                #     ProfilerActivity.CUDA], record_shapes=True) as prof:
+                #     with record_function("get_gs"):
+                latent = self.rec_model(
+                    img=micro['img_to_encoder'].to(self.dtype),
+                    behaviour='enc_dec_wo_triplane',
+                    c=micro['c'],
+                    pcd=micro['fps_pcd'], # send in pcd for surface reference.
+                )  # send in input-view C since pixel-aligned gaussians required
+
+                # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+                gaussians, query_pcd_xyz = latent['gaussians'], latent['query_pcd_xyz']
+                # query_pcd_xyz = latent['query_pcd_xyz']
+
+                # if self.loss_class.opt.rand_aug_bg and random.random()>0.9:
+                if self.loss_class.opt.rand_aug_bg:
+                    bg_color=torch.randint(0,255,(3,), device=dist_util.dev()) / 255.0
+                else:
+                    bg_color=torch.tensor([1,1,1], dtype=torch.float32, device=dist_util.dev())
+                
+                def visualize_latent_activations(latent, b_idx=0, write=False, ):
+
+                    def normalize_latent_plane(latent_plane):
+                        avg_p1 = latent_plane.detach().cpu().numpy().mean(0, keepdims=0)
+                        avg_p1 = (avg_p1 - avg_p1.min()) / (avg_p1.max() - avg_p1.min())
+                        # return avg_p1
+                        return ((avg_p1).clip(0,1)*255.0).astype(np.uint8)
+
+                    p1, p2, p3 = (normalize_latent_plane(latent_plane) for latent_plane in (latent[b_idx, 0:4], latent[b_idx,4:8], latent[b_idx,8:12]))
+
+                    if write:
+                        plt.imsave(os.path.join(logger.get_dir(), f'{self.step}_{b_idx}_1.jpg'), p1)
+                        plt.imsave(os.path.join(logger.get_dir(), f'{self.step}_{b_idx}_2.jpg'), p2)
+                        plt.imsave(os.path.join(logger.get_dir(), f'{self.step}_{b_idx}_3.jpg'), p3)
+                        # imageio.imwrite(os.path.join(logger.get_dir(), f'{b_idx}_1.jpg'), p1)
+                        # imageio.imwrite(os.path.join(logger.get_dir(), f'{b_idx}_2.jpg'), p2)
+                        # imageio.imwrite(os.path.join(logger.get_dir(), f'{b_idx}_3.jpg'), p3)
+
+                    return p1, p2, p3
+
+
+                # with profile(activities=[ProfilerActivity.CUDA, ProfilerActivity.CPU,], record_shapes=True) as prof:
+                #     # ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
+                #     with record_function("rendering"):
+
+                pred_nv_cano = self.rec_model(
+                    latent=latent,
+                    # latent={
+                    #     'gaussians': latent['gaussians'].repeat_interleave(2,0)
+                    # },
+                    c=nv_c,
+                    behaviour='triplane_dec',
+                    bg_color=bg_color,
+                )
+
+                fine_scale_key = list(pred_nv_cano.keys())[-1]
+                
+                # st()
+                fine_gaussians = latent[fine_scale_key]
+                fine_gaussians_opa = fine_gaussians[..., 3:4]
+
+                # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+
+                # st() # torchvision.utils.save_image(pred_nv_cano['image_raw'][0], 'pred.jpg', normalize=True, value_range=(-1,1))
+
+                if self.loss_class.opt.rand_aug_bg:
+                    # bg_color
+                    alpha_mask = target['depth_mask'].float().unsqueeze(1) # B 1 H W
+                    target['img'] = target['img'] * alpha_mask + (bg_color.reshape(1,3,1,1) * 2 - 1) * (1-alpha_mask)
+                
+                target['depth_mask'] = target['depth_mask'].unsqueeze(1)
+                target['depth'] = target['depth'].unsqueeze(1)
+
+                multiscale_target = defaultdict(dict)
+                multiscale_pred = defaultdict(dict)
+
+
+                for idx, (gaussian_wavelet_key, gaussian_wavelet) in enumerate(pred_nv_cano.items()):
+                    gs_output_size = pred_nv_cano[gaussian_wavelet_key]['image_raw'].shape[-1]
+                    for k in gaussian_wavelet.keys():
+                        pred_nv_cano[gaussian_wavelet_key][k] = rearrange(
+                            gaussian_wavelet[k], 'B V ... -> (B V) ...')  # match GT shape order
+
+                    # if idx == 0: # only KL calculation in scale 0
+                    if gaussian_wavelet_key == fine_scale_key:
+                        pred_nv_cano[gaussian_wavelet_key].update(
+                            {
+                                k: latent[k] for k in ['posterior']
+                            }
+                        )  # ! for KL supervision
+
+                    # ! prepare target according to the wavelet size
+                    for k in target.keys():
+
+                        if target[key].shape[-1] == gs_output_size:
+                            multiscale_target[gaussian_wavelet_key][k] = target[k]
+                        else:
+
+                            if k in ('depth', 'normal'):
+                                mode = 'nearest'
+                            else:
+                                mode='bilinear'
+
+                            multiscale_target[gaussian_wavelet_key][k] = F.interpolate(target[k], size=(gs_output_size, gs_output_size), mode=mode)
+
+                # st()
+
+                # st()
+                # torchvision.utils.save_image(target['img'], 'gt.jpg', normalize=True, value_range=(-1,1))
+                # torchvision.utils.save_image(pred_nv_cano['image_raw'], 'pred.jpg', normalize=True, value_range=(-1,1))
+                # torchvision.utils.save_image(micro['img'], 'inp_gt.jpg', normalize=True, value_range=(-1,1))
+                # torchvision.utils.save_image(micro['nv_img'], 'nv_gt.jpg', normalize=True, value_range=(-1,1))
+
+                # if self.loss_class.opt.rand_aug_bg:
+                #     # bg_color
+                #     alpha_mask = target['depth_mask'].float().unsqueeze(1) # B 1 H W
+                #     target['img'] = target['img'] * alpha_mask + (bg_color.reshape(1,3,1,1) * 2 - 1) * (1-alpha_mask)
+
+                lod_num = len(pred_nv_cano.keys())
+                random_scale_for_lpips = random.choice(list(pred_nv_cano.keys()))
+
+                with self.rec_model.no_sync():  # type: ignore
+                #     with profile(activities=[
+                #         ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
+                    # with record_function("loss"):
+
+                    loss = th.tensor(0., device=dist_util.dev())
+                    loss_dict = {}
+
+                    if behaviour == 'd_step':
+
+                        loss_scale, loss_dict_scale, _ = self.loss_class(
+                            pred_nv_cano[random_scale_for_lpips],
+                            multiscale_target[random_scale_for_lpips],  # prepare merged data
+                            step=self.step + self.resume_step,
+                            test_mode=False,
+                            return_fg_mask=True,
+                            behaviour=behaviour,
+                            conf_sigma_l1=None,
+                            conf_sigma_percl=None, 
+                            ignore_kl=True,  # only calculate once
+                            ignore_lpips=True, # lpips on each lod
+                            ignore_d_loss=False)
+                        
+                        loss = loss + loss_scale
+                        loss_dict.update(
+                            {
+                                f"{gaussian_wavelet_key.replace('gaussians_', '')}/{loss_key}": loss_v for loss_key, loss_v in loss_dict_scale.items()
+                            }
+                        )
+
+                    else:
+
+                        for scale_idx, gaussian_wavelet_key in enumerate(pred_nv_cano.keys()): # ! multi-scale gs rendering supervision
+
+                            loss_scale, loss_dict_scale, _ = self.loss_class(
+                                pred_nv_cano[gaussian_wavelet_key],
+                                multiscale_target[gaussian_wavelet_key],  # prepare merged data
+                                step=self.step + self.resume_step,
+                                test_mode=False,
+                                return_fg_mask=True,
+                                behaviour=behaviour,
+                                conf_sigma_l1=None,
+                                conf_sigma_percl=None, 
+                                ignore_kl=gaussian_wavelet_key!=fine_scale_key,  # only calculate once
+                                ignore_lpips=gaussian_wavelet_key!=random_scale_for_lpips, # lpips on each lod
+                                ignore_d_loss=gaussian_wavelet_key!=fine_scale_key)
+                            
+                            loss = loss + loss_scale
+                            loss_dict.update(
+                                {
+                                    f"{gaussian_wavelet_key.replace('gaussians_', '')}/{loss_key}": loss_v for loss_key, loss_v in loss_dict_scale.items()
+                                }
+                            )
+
+                pos = latent['pos']
+                opacity = gaussians[..., 3:4]
+                scaling = gaussians[..., 4:6] # 2dgs here
+
+                if self.step % self.log_interval == 0 and dist_util.get_rank(
+                ) == 0:
+                    with th.no_grad():  # save idx 0 here
+                        try:
+                            self.writer.add_histogram("scene/opacity_hist",
+                                                    opacity[0][:],
+                                                    self.step + self.resume_step)
+                            self.writer.add_histogram("scene/scale_hist",
+                                                    scaling[0][:],
+                                                    self.step + self.resume_step)
+                        except Exception as e:
+                            logger.log(e)
+
+                if behaviour == 'g_step':
+
+                    # ! 2dgs loss
+                    # debugging now, open it from the beginning
+                    # if (self.step + self.resume_step) >= 2000 and self.loss_class.opt.lambda_normal > 0:
+                    surf_normal = multiscale_target[fine_scale_key]['normal'] * multiscale_target[fine_scale_key]['depth_mask'] # foreground supervision only.
+
+                    # ! hard-coded
+                    # rend_normal = pred_nv_cano['rend_normal'] # ! supervise disk normal with GT normal here instead; 
+                    # st()
+                    rend_normal = pred_nv_cano[fine_scale_key]['rend_normal']
+                    rend_dist = pred_nv_cano[fine_scale_key]['dist']
+
+                    if self.loss_class.opt.lambda_scale_reg > 0:
+                        scale_reg = (scaling-self.scale_expected_threshold).square().mean() * self.loss_class.opt.lambda_scale_reg 
+                        loss = loss + scale_reg
+                        loss_dict.update({'loss_scale_reg': scale_reg})
+                    
+                    if self.loss_class.opt.lambda_opa_reg > 0:
+                        # small_base_opa = latent['gaussians_base_opa']
+                        opa_reg = (-self.loss_class.beta_mvp_base_dist.log_prob(latent['gaussians_base_opa'].clamp(min=1/255, max=0.99)).mean()) * self.loss_class.opt.lambda_opa_reg
+                        # ! also on the fine stage
+                        opa_reg_fine = (-self.loss_class.beta_mvp_base_dist.log_prob(fine_gaussians_opa.clamp(min=1/255, max=0.99)).mean()) * self.loss_class.opt.lambda_opa_reg
+                        # opa_reg = (1-latent['gaussians_base_opa'].mean() ) * self.loss_class.opt.lambda_opa_reg
+                        loss = loss + opa_reg + opa_reg_fine
+                        loss_dict.update({'loss_opa_reg': opa_reg, 'loss_opa_reg_fine': opa_reg_fine})
+
+
+                    if (self.step + self.resume_step) >= 35000 and self.loss_class.opt.lambda_normal > 0:
+                    # if (self.step + self.resume_step) >= 2000 and self.loss_class.opt.lambda_normal > 0:
+                        # surf_normal = unity2blender_th(surf_normal) # ! g-buffer normal system is different
+
+                        normal_error = (1 - (rend_normal * surf_normal).sum(dim=1)) # B H W
+                        # normal_loss = self.loss_class.opt.lambda_normal * (normal_error.sum() / target['depth_mask'].sum()) # average with fg area ratio
+                        normal_loss = self.loss_class.opt.lambda_normal * normal_error.mean()
+
+                        loss = loss + normal_loss
+
+                        loss_dict.update({'loss_normal': normal_loss})
+
+                    # if (self.step + self.resume_step) >= 1500 and self.loss_class.opt.lambda_dist > 0:
+                    if (self.step + self.resume_step) >= 15000 and self.loss_class.opt.lambda_dist > 0:
+                    # if (self.step + self.resume_step) >= 300 and self.loss_class.opt.lambda_dist > 0:
+                        dist_loss = self.loss_class.opt.lambda_dist * (rend_dist).mean()
+                        loss = loss + dist_loss
+                        loss_dict.update({'loss_dist': dist_loss})
+
+                    if self.loss_class.opt.pruning_ot_lambda > 0:
+                        # for now, save and analyze first
+                        # selected_pts_mask_scaling = th.where(th.max(scaling, dim=-1).values < 0.01 * 0.9, True, False)
+                        selected_pts_mask_scaling = th.where(
+                            th.max(scaling, dim=-1).values > 0.05 * 0.9, True,
+                            False)
+                        # selected_pts_mask_opacity = th.where(opacity[..., 0] < 0.1, True, False) # B N
+                        selected_pts_mask_opacity = th.where(
+                            opacity[..., 0] < 0.01, True,
+                            False)  # 0.005 in the original 3dgs setting
+
+                        selected_scaling_pts = pos[0][selected_pts_mask_scaling[0]]
+                        selected_opacity_pts = pos[0][selected_pts_mask_opacity[0]]
+
+                        pcu.save_mesh_v(
+                            f'tmp/voxel/cd/10/scaling_masked_pts_0.05.ply',
+                            selected_scaling_pts.detach().cpu().numpy(),
+                        )
+
+                        pcu.save_mesh_v(
+                            f'tmp/voxel/cd/10/opacity_masked_pts_0.01.ply',
+                            selected_opacity_pts.detach().cpu().numpy(),
+                        )
+
+                        # st()
+                        # pass
+
+                    if self.loss_class.opt.cd_lambda > 0:
+                        # fuse depth to 3D point cloud to supervise the gaussians
+                        B = latent['pos'].shape[0]
+                        # c = micro['c']
+                        # H = micro['depth'].shape[-1]
+                        # V = 4
+                        # # ! prepare 3D xyz ground truth
+
+                        # cam2world_matrix = c['orig_c2w'][:, :, :16].reshape(
+                        #     B * V, 4, 4)
+                        # intrinsics = c['orig_pose'][:, :,
+                        #                             16:25].reshape(B * V, 3, 3)
+
+                        # # ! already in the world space after ray_sampler()
+                        # ray_origins, ray_directions = self.ray_sampler(  # shape:
+                        #     cam2world_matrix, intrinsics, H // 2)[:2]
+
+                        # # depth = th.nn.functional.interpolate(micro['depth'].unsqueeze(1), (128,128), mode='nearest')[:, 0] # since each view has 128x128 Gaussians
+                        # # depth = th.nn.functional.interpolate(micro['depth'].unsqueeze(1), (128,128), mode='nearest')[:, 0] # since each view has 128x128 Gaussians
+                        # depth_128 = th.nn.functional.interpolate(
+                        #     micro['depth'].unsqueeze(1), (128, 128),
+                        #     mode='nearest'
+                        # )[:, 0]  # since each view has 128x128 Gaussians
+                        # depth = depth_128.reshape(B * V, -1).unsqueeze(-1)
+                        # # depth = micro['depth'].reshape(B*V, -1).unsqueeze(-1)
+
+                        # gt_pos = ray_origins + depth * ray_directions  # BV HW 3, already in the world space
+                        # gt_pos = rearrange(gt_pos,
+                        #                    '(B V) N C -> B (V N) C',
+                        #                    B=B,
+                        #                    V=V)
+                        # gt_pos = gt_pos.clip(-0.45, 0.45)
+
+                        # TODO
+                        gt_pos = micro[
+                            'fps_pcd']  # all the same, will update later.
+
+                        # ! use online here
+                        # gt_pos = query_pcd_xyz
+
+                        cd_loss = pytorch3d.loss.chamfer_distance(
+                            gt_pos, latent['pos']
+                        )[0] * self.loss_class.opt.cd_lambda  # V=4 GT for now. Test with V=8 GT later.
+                        # st()
+
+                        # for vis
+                        if False:
+                            torchvision.utils.save_image(micro['img'],
+                                                        'gt.jpg',
+                                                        value_range=(-1, 1),
+                                                        normalize=True)
+                            with th.no_grad():
+                                for b in range(B):
+                                    pcu.save_mesh_v(
+                                        f'tmp/voxel/cd/10/again_pred-{b}.ply',
+                                        latent['pos'][b].detach().cpu().numpy(),
+                                    )
+                                    # pcu.save_mesh_v(
+                                    #     f'tmp/voxel/cd/10/again-gt-{b}.ply',
+                                    #     gt_pos[b].detach().cpu().numpy(),
+                                    # )
+                        # st()
+
+                        loss = loss + cd_loss
+                        loss_dict.update({'loss_cd': cd_loss})
+
+                    elif self.loss_class.opt.xyz_lambda > 0:
+                        '''
+                        B = latent['per_view_pos'].shape[0] // 4
+                        V = 4
+                        c = micro['c']
+                        H = micro['depth'].shape[-1]
+                        # ! prepare 3D xyz ground truth
+
+                        cam2world_matrix = c['orig_c2w'][:, :, :16].reshape(
+                            B * V, 4, 4)
+                        intrinsics = c['orig_pose'][:, :,
+                                                    16:25].reshape(B * V, 3, 3)
+
+                        # ! already in the world space after ray_sampler()
+                        ray_origins, ray_directions = self.ray_sampler(  # shape: 
+                            cam2world_matrix, intrinsics, H // 2)[:2]
+                        # self.gs.output_size,)[:2]
+                        # depth = rearrange(micro['depth'], '(B V) H W -> ')
+                        depth_128 = th.nn.functional.interpolate(
+                            micro['depth'].unsqueeze(1), (128, 128),
+                            mode='nearest'
+                        )[:, 0]  # since each view has 128x128 Gaussians
+                        depth = depth_128.reshape(B * V, -1).unsqueeze(-1)
+                        fg_mask = th.nn.functional.interpolate(
+                            micro['depth_mask'].unsqueeze(1).to(th.uint8),
+                            (128, 128),
+                            mode='nearest').squeeze(1)  # B*V H W
+                        fg_mask = fg_mask.reshape(B * V, -1).unsqueeze(-1)
+                        gt_pos = ray_origins + depth * ray_directions  # BV HW 3, already in the world space
+                        # st()
+                        gt_pos = fg_mask * gt_pos.clip(
+                            -0.45, 0.45)  # g-buffer objaverse range
+                        pred = fg_mask * latent['per_view_pos']
+
+                        # for vis
+                        if True:
+                            torchvision.utils.save_image(micro['img'],
+                                                        'gt.jpg',
+                                                        value_range=(-1, 1),
+                                                        normalize=True)
+                            with th.no_grad():
+                                gt_pos_vis = rearrange(gt_pos,
+                                                    '(B V) N C -> B V N C',
+                                                    B=B,
+                                                    V=V)
+                                pred_pos_vis = rearrange(pred,
+                                                        '(B V) N C -> B V N C',
+                                                        B=B,
+                                                        V=V)
+                                # save
+
+                                for b in range(B):
+                                    for v in range(V):
+                                        # pcu.save_mesh_v(f'tmp/dust3r/add3dsupp-pred-{b}-{v}.ply',
+                                        #                 pred_pos_vis[b][v].detach().cpu().numpy(),)
+                                        # pcu.save_mesh_v(f'tmp/dust3r/add3dsupp-gt-{b}-{v}.ply',
+                                        #                 gt_pos_vis[b][v].detach().cpu().numpy(),)
+                                        pcu.save_mesh_v(
+                                            f'tmp/lambda50/no3dsupp-pred-{b}-{v}.ply',
+                                            pred_pos_vis[b]
+                                            [v].detach().cpu().numpy(),
+                                        )
+                                        pcu.save_mesh_v(
+                                            f'tmp/lambda50/no3dsupp-gt-{b}-{v}.ply',
+                                            gt_pos_vis[b]
+                                            [v].detach().cpu().numpy(),
+                                        )
+                        st()
+
+                        xyz_loss = th.nn.functional.mse_loss(
+                            gt_pos, pred
+                        ) * self.loss_class.opt.xyz_lambda  # ! 15% nonzero points
+                        loss = loss + xyz_loss
+                        '''
+
+                        # ! directly gs center supervision with l1 loss, follow LION VAE
+
+                        # xyz_loss = th.nn.functional.l1_loss(
+                        #     query_pcd_xyz, pred
+                        # ) * self.loss_class.opt.xyz_lambda  # ! 15% nonzero points
+                        xyz_loss = self.loss_class.criterion_xyz(query_pcd_xyz, latent['pos']) * self.loss_class.opt.xyz_lambda
+                        loss = loss + xyz_loss
+
+                        # only calculate foreground gt_pos here?
+                        loss_dict.update({'loss_xyz': xyz_loss})
+
+                    elif self.loss_class.opt.emd_lambda > 0:
+                        # rand_pt_size = 4096  # K value. Input Error! The size of the point clouds should be a multiple of 1024.
+                        pred = latent['pos']
+                        rand_pt_size = min(2048, max(pred.shape[1], 1024))  # K value. Input Error! The size of the point clouds should be a multiple of 1024.
+
+                        if micro['fps_pcd'].shape[0] == pred.shape[0]:
+                            gt_point = micro['fps_pcd']
+                        else:  # overfit memory dataset
+                            gt_point = micro[
+                                'fps_pcd'][::
+                                        4]  # consecutive 4 views are from the same ID
+
+                        B, gt_point_N = gt_point.shape[:2]
+                        # random sample pred points
+                        # sampled_pred =
+                        # rand_pt_idx = torch.randint(high=pred.shape[1]-gt_point_N, size=(B,))
+
+
+                        # pcu.save_mesh_v( f'tmp/voxel/emd/gt-half.ply', gt_point[0, ::4].detach().cpu().numpy(),)
+
+                        # for b in range(gt_point.shape[0]):
+                        #     pcu.save_mesh_v( f'{logger.get_dir()}/gt-{b}.ply', gt_point[b].detach().cpu().numpy(),)
+
+                        #     pcu.save_mesh_v( f'{logger.get_dir()}/pred-{b}.ply', pred[b].detach().cpu().numpy(),)
+
+                        # pcu.save_mesh_v( f'0.ply', latent['pos'][0].detach().cpu().numpy())
+                        # st()  
+
+                        if self.loss_class.opt.fps_sampling:  # O(N*K). reduce K later.
+
+                            if self.loss_class.opt.subset_fps_sampling:
+                                rand_pt_size = 1024  # for faster calculation
+                                # ! uniform sampling with randomness
+                                # sampled_gt_pts_for_emd_loss = gt_point[:, random.randint(0,9)::9][:, :1024] # direct uniform downsample to the K size
+                                # sampled_gt_pts_for_emd_loss = gt_point[:, random.randint(0,9)::4][:, :1024] # direct uniform downsample to the K size
+
+                                rand_perm = torch.randperm(
+                                    gt_point.shape[1]
+                                )[:rand_pt_size]  # shuffle the xyz before downsample - fps sampling
+                                sampled_gt_pts_for_emd_loss = gt_point[:, rand_perm]
+
+                                # sampled_gt_pts_for_emd_loss = gt_point[:, ::4]
+                                # sampled_pred_pts_for_emd_loss = pred[:, ::32]
+                                # sampled_gt_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                #     gt_point[:, ::4], K=rand_pt_size)[0] # V4
+
+                                if self.loss_class.opt.subset_half_fps_sampling:
+                                    # sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                    #     pred, K=rand_pt_size)[0] # V5
+                                    rand_perm = torch.randperm(
+                                        pred.shape[1]
+                                    )[:4096]  # shuffle the xyz before downsample - fps sampling
+                                    sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                        pred[:, rand_perm],
+                                        K=rand_pt_size)[0]  # improve randomness
+                                else:
+                                    sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                        pred[:, ::4], K=rand_pt_size)[0]  # V5
+
+                                # rand_perm = torch.randperm(pred.shape[1]) # shuffle the xyz before downsample - fps sampling
+                                # sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                #     pred[:, rand_perm][:, ::4], K=rand_pt_size)[0] # rand perm before downsampling, V6
+
+                                # sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                #     pred[:, rand_perm][:, ::8], K=rand_pt_size)[0] # rand perm before downsampling, V7
+
+                                # sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                #     pred[:, self.step%2::4], K=rand_pt_size)[0] # rand perm before downsampling, V8, based on V50
+
+                            else:
+                                sampled_gt_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                    gt_point, K=rand_pt_size)[0]
+
+                                # if self.loss_class.opt.subset_half_fps_sampling:
+                                # rand_pt_size = 4096 # K value. Input Error! The size of the point clouds should be a multiple of 1024.
+
+                                sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                    pred, K=rand_pt_size)[0]
+
+                                # else:
+                                #     sampled_pred_pts_for_emd_loss = pytorch3d.ops.sample_farthest_points(
+                                #         pred, K=rand_pt_size)[0]
+
+                        else:  # random sampling
+                            rand_pt_idx_pred = torch.randint(high=pred.shape[1] -
+                                                            rand_pt_size,
+                                                            size=(1, ))[0]
+                            rand_pt_idx_gt = torch.randint(high=gt_point.shape[1] -
+                                                        rand_pt_size,
+                                                        size=(1, ))[0]
+
+                            sampled_pred_pts_for_emd_loss = pred[:,
+                                                                rand_pt_idx_pred:
+                                                                rand_pt_idx_pred +
+                                                                rand_pt_size, ...]
+                            sampled_gt_pts_for_emd_loss = gt_point[:,
+                                                                rand_pt_idx_gt:
+                                                                rand_pt_idx_gt +
+                                                                rand_pt_size,
+                                                                ...]
+
+                        # only calculate foreground gt_pos here?
+
+                        emd_loss = calc_emd(sampled_gt_pts_for_emd_loss,
+                                            sampled_pred_pts_for_emd_loss).mean(
+                                            ) * self.loss_class.opt.emd_lambda
+                        loss = loss + emd_loss
+                        loss_dict.update({'loss_emd': emd_loss})
+
+                    if self.loss_class.opt.commitment_loss_lambda > 0:
+                        ellipsoid_vol = torch.prod(scaling, dim=-1, keepdim=True) / ((0.01 * 0.9)**3) # * (4/3*torch.pi). normalized vol
+                        commitment = ellipsoid_vol * opacity
+                        to_be_pruned_ellipsoid_idx = commitment < (3/4)**3 * 0.9 # those points shall have larger vol*opacity contribution
+                        commitment_loss = -commitment[to_be_pruned_ellipsoid_idx].mean() * self.loss_class.opt.commitment_loss_lambda
+                        
+                        loss = loss + commitment_loss
+                        loss_dict.update({'loss_commitment': commitment_loss})
+                        loss_dict.update({'loss_commitment_opacity': opacity.mean()})
+                        loss_dict.update({'loss_commitment_vol': ellipsoid_vol.mean()})
+
+
+                log_rec3d_loss_dict(loss_dict)
+
+            # self.mp_trainer_rec.backward(loss)
+            if behaviour == 'g_step':
+                self.mp_trainer_rec.backward(loss)
+            else:
+                self.mp_trainer_disc.backward(loss)
+
+            # for name, p in self.rec_model.named_parameters():
+            #     if p.grad is None:
+            #         logger.log(f"found rec unused param: {name}")
+
+            #     print(name, p.grad.mean(), p.grad.abs().max())
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0 and i == 0 and behaviour=='g_step':
+            # if dist_util.get_rank() == 0 and self.step % 1 == 0 and i == 0:
+                try:
+                    torchvision.utils.save_image(
+                        th.cat([target['img'][::1], pred_nv_cano[fine_scale_key]['image_raw'][::1]], ),
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+                        normalize=True, value_range=(-1,1),nrow=len(indices)*2)
+
+                    # save depth and normal and alpha
+                    torchvision.utils.save_image(
+                        th.cat([surf_normal[::1], rend_normal[::1]], ),
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_normal_new.jpg',
+                        normalize=True, value_range=(-1,1), nrow=len(indices)*2)
+
+                    torchvision.utils.save_image(
+                        th.cat([target['depth'][::1], pred_nv_cano[fine_scale_key]['image_depth'][::1]], ),
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_depth.jpg',
+                        normalize=True, nrow=len(indices)*2)
+
+                    # torchvision.utils.save_image( pred_nv_cano['image_depth'][::1], f'{logger.get_dir()}/{self.step+self.resume_step}_depth.jpg', normalize=True, nrow=len(indices)*2)
+
+                    torchvision.utils.save_image(
+                        th.cat([target['depth_mask'][::1], pred_nv_cano[fine_scale_key]['image_mask'][::1]], ),
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_alpha.jpg',
+                        normalize=True, value_range=(0,1), nrow=len(indices)*2)
+
+
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+                except Exception as e:
+                    logger.log('Exception when saving log: ', e)
+
+            # if self.step % 2500 == 0:
+            #     th.cuda.empty_cache() # free vram
+
+    @torch.no_grad()
+    def export_mesh_from_2dgs(self, all_rgbs, all_depths, all_alphas, cam_pathes, latent_save_dir):
+        # https://github.com/autonomousvision/LaRa/blob/main/evaluation.py
+        n_thread = 1 # avoid TSDF cpu hanging bug.
+        os.environ["MKL_NUM_THREADS"] = f"{n_thread}" 
+        os.environ["NUMEXPR_NUM_THREADS"] = f"{n_thread}" 
+        os.environ["OMP_NUM_THREADS"] = f"4" 
+        os.environ["VECLIB_MAXIMUM_THREADS"] = f"{n_thread}" 
+        os.environ["OPENBLAS_NUM_THREADS"] = f"{n_thread}" 
+
+        # copied from: https://github.com/hbb1/2d-gaussian-splatting/blob/19eb5f1e091a582e911b4282fe2832bac4c89f0f/render.py#L23
+        logger.log("exporting mesh ...")
+
+        # for g-objv
+        aabb = [-0.45,-0.45,-0.45,0.45,0.45,0.45]
+        self.aabb = np.array(aabb).reshape(2,3)*1.1
+
+        name = f'{latent_save_dir}/mesh_raw.obj'
+        mesh = self.extract_mesh_bounded(all_rgbs, all_depths, all_alphas, cam_pathes)
+        
+        o3d.io.write_triangle_mesh(name, mesh)
+        logger.log("mesh saved at {}".format(name))
+        mesh_post = smooth_mesh(mesh)
+        o3d.io.write_triangle_mesh(name.replace('_raw.obj', '.obj'), mesh_post)
+        logger.log("mesh post processed saved at {}".format(name.replace('.obj', '_post.obj')))
+
+    def get_source_cw2wT(self, source_cameras_view_to_world):
+        return matrix_to_quaternion(
+            source_cameras_view_to_world[:3, :3].transpose(0, 1))
+
+
+    def c_to_3dgs_format(self, pose):
+        # TODO, switch to torch version (batched later)
+
+        c2w = pose[:16].reshape(4, 4)  # 3x4
+
+        # ! load cam
+        w2c = np.linalg.inv(c2w)
+        R = np.transpose(
+            w2c[:3, :3])  # R is stored transposed due to 'glm' in CUDA code
+        T = w2c[:3, 3]
+        fx = pose[16]
+        FovX = focal2fov(fx, 1)
+        FovY = focal2fov(fx, 1)
+
+        tanfovx = math.tan(FovX * 0.5)
+        tanfovy = math.tan(FovY * 0.5)
+
+        assert tanfovx == tanfovy
+
+        trans = np.array([0.0, 0.0, 0.0])
+        scale = 1.0
+
+        world_view_transform = torch.tensor(getWorld2View2(R, T, trans,
+                                                           scale)).transpose(
+                                                               0, 1)
+        projection_matrix = getProjectionMatrix(znear=self.znear,
+                                                zfar=self.zfar,
+                                                fovX=FovX,
+                                                fovY=FovY).transpose(0, 1)
+        full_proj_transform = (world_view_transform.unsqueeze(0).bmm(
+            projection_matrix.unsqueeze(0))).squeeze(0)
+        camera_center = world_view_transform.inverse()[3, :3]
+
+        view_world_transform = torch.tensor(getView2World(R, T, trans,
+                                                          scale)).transpose(
+                                                              0, 1)
+
+        # item.update(viewpoint_cam=[viewpoint_cam])
+        c = {}
+        c["source_cv2wT_quat"] = self.get_source_cw2wT(view_world_transform)
+        c.update(
+            projection_matrix=projection_matrix, # K
+            cam_view=world_view_transform,  # world_view_transform
+            cam_view_proj=full_proj_transform,  # full_proj_transform
+            cam_pos=camera_center,
+            tanfov=tanfovx,  # TODO, fix in the renderer
+            # orig_c2w=c2w,
+            # orig_w2c=w2c,
+            orig_pose=torch.from_numpy(pose),
+            orig_c2w=torch.from_numpy(c2w),
+            orig_w2c=torch.from_numpy(w2c),
+            # tanfovy=tanfovy,
+        )
+
+        return c  # dict for gs rendering
+
+
+    @torch.no_grad()
+    def extract_mesh_bounded(self, rgbmaps, depthmaps, alpha_maps, cam_pathes, voxel_size=0.004, sdf_trunc=0.02, depth_trunc=3, alpha_thres=0.08, mask_backgrond=False):
+        """
+        Perform TSDF fusion given a fixed depth range, used in the paper.
+        
+        voxel_size: the voxel size of the volume
+        sdf_trunc: truncation value
+        depth_trunc: maximum depth range, should depended on the scene's scales
+        mask_backgrond: whether to mask backgroud, only works when the dataset have masks
+
+        return o3d.mesh
+        """
+
+        if self.aabb is not None: # as in lara.
+            center = self.aabb.mean(0)
+            # radius = np.linalg.norm(self.aabb[1] - self.aabb[0]) * 0.5
+            radius = np.linalg.norm(self.aabb[1] - self.aabb[0]) * 0.5
+            # voxel_size = radius / 256
+            voxel_size = radius / 192 # less holes
+            # sdf_trunc = voxel_size * 16 # less holes, slower integration
+            sdf_trunc = voxel_size * 12 # 
+            print("using aabb")
+
+        volume = o3d.pipelines.integration.ScalableTSDFVolume(
+            voxel_length= voxel_size,
+            sdf_trunc=sdf_trunc,
+            color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8
+        )
+
+        print("Running tsdf volume integration ...")
+        print(f'voxel_size: {voxel_size}')
+        print(f'sdf_trunc: {sdf_trunc}')
+        # print(f'depth_truc: {depth_trunc}')
+
+        # render_reference = th.load('eval_pose.pt', map_location='cpu').numpy()
+
+        # ! use uni_mesh_path, from Lara, Chen et al, ECCV 24'
+
+        # '''
+
+        # for i, cam_o3d in tqdm(enumerate(to_cam_open3d(self.viewpoint_stack)), desc="TSDF integration progress"):
+        for i, cam in tqdm(enumerate(cam_pathes), desc="TSDF integration progress"):
+            # rgb = self.rgbmaps[i]
+            # depth = self.depthmaps[i]
+            cam = self.c_to_3dgs_format(cam)
+            cam_o3d = to_cam_open3d_compat(cam)
+
+            rgb = rgbmaps[i][0]
+            depth = depthmaps[i][0]
+            alpha = alpha_maps[i][0]
+
+            # if we have mask provided, use it
+            # if mask_backgrond and (self.viewpoint_stack[i].gt_alpha_mask is not None):
+            #     depth[(self.viewpoint_stack[i].gt_alpha_mask < 0.5)] = 0
+            
+            depth[(alpha < alpha_thres)] = 0
+            if self.aabb is not None:
+                campos = cam['cam_pos'].cpu().numpy()
+                depth_trunc = np.linalg.norm(campos - center, axis=-1) + radius
+
+            # make open3d rgbd
+            rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
+                o3d.geometry.Image(np.asarray(np.clip(rgb.permute(1,2,0).cpu().numpy(), 0.0, 1.0) * 255, order="C", dtype=np.uint8)),
+                o3d.geometry.Image(np.asarray(depth.permute(1,2,0).cpu().numpy(), order="C")),
+                depth_trunc = depth_trunc, 
+                convert_rgb_to_intensity=False,
+                depth_scale = 1.0
+            )
+
+            volume.integrate(rgbd, intrinsic=cam_o3d.intrinsic, extrinsic=cam_o3d.extrinsic)
+
+        mesh = volume.extract_triangle_mesh()
+        return mesh
+
+
+
+    @th.inference_mode()
+    def eval_novelview_loop(self, camera=None, save_latent=False):
+        # novel view synthesis given evaluation camera trajectory
+        if save_latent:  # for diffusion learning
+            latent_dir = Path(f'{logger.get_dir()}/latent_dir')
+            latent_dir.mkdir(exist_ok=True, parents=True)
+
+        render_reference=uni_mesh_path(10)
+
+        for eval_idx, micro in enumerate(tqdm(self.eval_data)):
+
+            latent_save_dir = f'{logger.get_dir()}/latent_dir/{micro["ins"][0]}'
+
+            all_latent_file = sorted(Path(latent_save_dir).glob('*.npz') )
+            if len(all_latent_file) == 0:
+                save_prefix = 0
+            else:
+                save_prefix = int(all_latent_file[-1].stem[-1] ) + 1
+
+            Path(latent_save_dir).mkdir(parents=True, exist_ok=True)
+
+            with th.autocast(device_type='cuda',
+                             dtype=self.dtype,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                # st() # check whether more c info available
+                latent = self.rec_model(
+                    img=micro['img_to_encoder'].to(self.dtype),
+                    behaviour='enc_dec_wo_triplane',
+                    c=micro['c'],
+                    pcd=micro['fps_pcd'], # send in pcd for surface reference.
+                )  # send in input-view C since pixel-aligned gaussians required
+
+            # fine_scale_key = list(pred.keys())[-1]
+            # fine_scale_key = 'gaussians_upsampled_2'
+            fine_scale_key = 'gaussians_upsampled_3'
+            export_mesh = True # for debug
+
+            if True:
+
+                # if eval_idx < 1500 and eval_idx % 3 == 0:
+                if eval_idx < 1500:
+                    all_rgbs, all_depths, all_alphas=self.render_gs_video_given_latent(
+                        latent,
+                        self.rec_model,  # compatible with join_model
+                        name_prefix=f'{self.step + self.resume_step}_{micro["ins"][0].split("/")[0]}_{eval_idx}',
+                        save_img=False,
+                        render_reference=render_reference,
+                        export_mesh=False)
+
+                    if export_mesh:
+                        self.export_mesh_from_2dgs(all_rgbs, all_depths, all_alphas, render_reference, latent_save_dir)
+
+                # ! B=2 here
+                np.savez_compressed(f'{latent_save_dir}/latent-{save_prefix}.npz', 
+                    latent_normalized=latent['latent_normalized'].cpu().numpy(),
+                    query_pcd_xyz=latent['query_pcd_xyz'].cpu().numpy()
+                )
+
+                # st()
+                for scale in ['gaussians_upsampled', 'gaussians_base', 'gaussians_upsampled_2', 'gaussians_upsampled_3']:
+                    np.save(f'{latent_save_dir}/{scale}.npy', latent[scale].cpu().numpy())
+            
+
+    @th.inference_mode()
+    def render_gs_video_given_latent(self,
+                                    ddpm_latent,
+                                    rec_model,
+                                    name_prefix='0',
+                                    save_img=False, 
+                                    render_reference=None, 
+                                    export_mesh=False):
+
+        all_rgbs, all_depths, all_alphas = [], [], []
+
+        # batch_size, L, C = planes.shape
+
+        # ddpm_latent = { self.latent_name: planes[..., :-3] * self.triplane_scaling_divider,  # kl-reg latent
+        #                 'query_pcd_xyz': self.pcd_unnormalize_fn(planes[..., -3:]) }
+        
+        # ddpm_latent.update(rec_model(latent=ddpm_latent, behaviour='decode_gs_after_vae_no_render')) 
+        
+
+        # assert render_reference is None
+            # render_reference = self.eval_data # compat
+        # else: # use train_traj
+
+        # for key in ['ins', 'bbox', 'caption']:
+        #     if key in render_reference:
+        #         render_reference.pop(key)
+
+        # render_reference = [ { k:v[idx:idx+1] for k, v in render_reference.items() } for idx in range(40) ]
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/gs_{name_prefix}.mp4',
+            mode='I',
+            fps=15,
+            codec='libx264')
+
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+        for i, micro_c in enumerate(tqdm(render_reference)):
+            # micro = {
+            #     k: v.to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+            #     for k, v in batch.items() 
+            # }
+
+            c = self.post_process.c_to_3dgs_format(micro_c)
+            for k in c.keys(): # to cuda
+                if isinstance(c[k], th.Tensor) and k != 'tanfov':
+                    c[k] = c[k].unsqueeze(0).unsqueeze(0).to(dist_util.dev()) # actually, could render 40 views together.
+            c['tanfov'] = th.tensor(c['tanfov']).to(dist_util.dev())
+
+            pred = rec_model(
+                img=None,
+                c=c, # TODO, to dict
+                latent=ddpm_latent, # render gs
+                behaviour='triplane_dec',
+                bg_color=self.gs_bg_color,
+                render_all_scale=True,
+                )
+
+            fine_scale_key = list(pred.keys())[-1]
+
+            all_rgbs.append(einops.rearrange(pred[fine_scale_key]['image'], 'B V ... -> (B V) ...'))
+            all_depths.append(einops.rearrange(pred[fine_scale_key]['depth'], 'B V ... -> (B V) ...'))
+            all_alphas.append(einops.rearrange(pred[fine_scale_key]['alpha'], 'B V ... -> (B V) ...'))
+
+            # st()
+            # fine_scale_key = list(pred.keys())[-1]
+            all_pred_vis = {}
+            for key in pred.keys():
+                pred_scale = pred[key] # only show finest result here
+                for k in pred_scale.keys():
+                    pred_scale[k] = einops.rearrange(pred_scale[k], 'B V ... -> (B V) ...') # merge 
+                
+                pred_vis = self._make_vis_img(pred_scale)
+
+                vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+                vis = vis * 127.5 + 127.5
+                vis = vis.clip(0, 255).astype(np.uint8)
+
+                all_pred_vis[key] = vis
+            
+            # all_pred_vis_concat = np.concatenate([cv2.resize(all_pred_vis[k][0], (384*3, 384)) for k in ['gaussians_base', 'gaussians_upsampled', 'gaussians_upsampled_2']], axis=0)
+            # all_pred_vis_concat = np.concatenate([cv2.resize(all_pred_vis[k][0], (256*3, 256)) for k in ['gaussians_base', 'gaussians_upsampled',]], axis=0)
+            # all_pred_vis_concat = np.concatenate([cv2.resize(all_pred_vis[k][0], (384*3, 384)) for k in all_pred_vis.keys()], axis=0)
+            all_pred_vis_concat = np.concatenate([cv2.resize(all_pred_vis[k][0], (512*3, 512)) for k in all_pred_vis.keys()], axis=0)
+
+            # for j in range(vis.shape[0]):  
+            video_out.append_data(all_pred_vis_concat)
+
+        video_out.close()
+
+        print('logged video to: ',
+            f'{logger.get_dir()}/triplane_{name_prefix}.mp4')
+
+        del video_out, pred, pred_vis, vis
+        return all_rgbs, all_depths, all_alphas
+
+    @th.no_grad()
+    def _make_vis_img(self, pred):
+
+        # if True:
+        pred_depth = pred['image_depth']
+        pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                        pred_depth.min())
+
+        pred_depth = pred_depth.cpu()[0].permute(1, 2, 0).numpy()
+        pred_depth = (plt.cm.viridis(pred_depth[..., 0])[..., :3]) * 2 - 1
+        pred_depth = th.from_numpy(pred_depth).to(
+            pred['image_raw'].device).permute(2, 0, 1).unsqueeze(0)
+
+        gen_img = pred['image_raw']
+        rend_normal = pred['rend_normal']
+
+        pred_vis = th.cat(
+            [
+                gen_img,
+                rend_normal,
+                pred_depth,
+            ],
+            dim=-1)  # B, 3, H, W
+        
+        return pred_vis
+
+
+class TrainLoop3DRecNVPatchSingleForwardMV_NoCrop_adv(TrainLoop3DRecNVPatchSingleForwardMV_NoCrop):
+    def __init__(self, *, rec_model, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, load_submodule_name='', ignore_resume_opt=False, model_name='rec', use_amp=False, num_frames=4, **kwargs):
+        super().__init__(rec_model=rec_model, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, load_submodule_name=load_submodule_name, ignore_resume_opt=ignore_resume_opt, model_name=model_name, use_amp=use_amp, num_frames=num_frames, **kwargs)
+
+        # create discriminator
+        # ! copied from ln3diff tri-plane version
+        disc_params = self.loss_class.get_trainable_parameters()
+
+        self.mp_trainer_disc = MixedPrecisionTrainer(
+            model=self.loss_class.discriminator,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='disc',
+            use_amp=use_amp,
+            model_params=disc_params)
+
+        # st() # check self.lr
+        self.opt_disc = AdamW(
+            self.mp_trainer_disc.master_params,
+            lr=self.lr,  # follow sd code base
+            betas=(0, 0.999),
+            eps=1e-8)
+
+        # TODO, is loss cls already in the DDP?
+        if self.use_ddp:
+            self.ddp_disc = DDP(
+                self.loss_class.discriminator,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            self.ddp_disc = self.loss_class.discriminator
+
+    def save(self, mp_trainer=None, model_name='rec'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, mp_trainer.master_params)
+
+        dist.barrier()
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step':
+            self.forward_backward(batch, behaviour='g_step')
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step':
+            self.forward_backward(batch, behaviour='d_step')
+            _ = self.mp_trainer_disc.optimize(self.opt_disc)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self, batch=None):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step')
+
+            if self.step % 1000 == 0:
+                dist_util.synchronize()
+                if self.step % 5000 == 0:
+                    th.cuda.empty_cache()  # avoid memory leak
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                if dist_util.get_rank() == 0:
+                    try:
+                        self.eval_loop()
+                    except Exception as e:
+                        logger.log(e)
+                dist_util.synchronize()
+
+            # if self.step % self.save_interval == 0 and self.step != 0:
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_disc,
+                          self.mp_trainer_disc.model_name)
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                logger.log('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step -
+                        1) % self.save_interval != 0 and self.step != 1:
+                    self.save()
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        # if (self.step - 1) % self.save_interval != 0 and self.step != 1:
+        if (self.step - 1) % self.save_interval != 0:
+            try:
+                self.save()  # save rec
+                self.save(self.mp_trainer_disc, self.mp_trainer_disc.model_name)
+            except Exception as e:
+                logger.log(e)
+
+    # ! load disc
+    def _load_and_sync_parameters(self, submodule_name=''):
+        super()._load_and_sync_parameters(submodule_name)
+        # load disc
+
+        resume_checkpoint = self.resume_checkpoint.replace(
+            'rec', 'disc')  # * default behaviour
+        if os.path.exists(resume_checkpoint):
+            if dist_util.get_rank() == 0:
+                logger.log(
+                    f"loading disc model from checkpoint: {resume_checkpoint}..."
+                )
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                model_state_dict = self.loss_class.discriminator.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys():
+                        if v.size() == model_state_dict[k].size():
+                            model_state_dict[k] = v
+                            # model_state_dict[k].copy_(v)
+                        else:
+                            logger.log('!!!! partially load: ', k, ": ",
+                                       v.size(), "state_dict: ",
+                                       model_state_dict[k].size())
+
+            if dist_util.get_world_size() > 1:
+                # dist_util.sync_params(self.model.named_parameters())
+                dist_util.sync_params(
+                    self.loss_class.get_trainable_parameters())
+                logger.log('synced disc params')
+
diff --git a/nsr/train_util.py b/nsr/train_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d03ae75cd51c3e4c85f771740d0c97a1363af4cd
--- /dev/null
+++ b/nsr/train_util.py
@@ -0,0 +1,1980 @@
+import copy
+import matplotlib.pyplot as plt
+import mcubes
+import trimesh
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+
+import traceback
+from nsr.gs import GaussianRenderer
+from nsr.gs_surfel import GaussianRenderer2DGS
+import blobfile as bf
+import imageio
+import numpy as np
+# from sympy import O
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm, trange
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+from guided_diffusion.train_util import (calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+
+from .camera_utils import LookAtPoseSampler, FOV_to_intrinsics
+
+# from ..guided_diffusion.train_util import TrainLoop
+
+
+def flip_yaw(pose_matrix):
+    flipped = pose_matrix.clone()
+    flipped[:, 0, 1] *= -1
+    flipped[:, 0, 2] *= -1
+    flipped[:, 1, 0] *= -1
+    flipped[:, 2, 0] *= -1
+    flipped[:, 0, 3] *= -1
+    # st()
+    return flipped
+
+
+# basic reconstruction model
+class TrainLoopBasic:
+
+    def __init__(
+            self,
+            *,
+            rec_model,
+            loss_class,
+            # diffusion,
+            data,
+            eval_data,
+            batch_size,
+            microbatch,
+            lr,
+            ema_rate,
+            log_interval,
+            eval_interval,
+            save_interval,
+            resume_checkpoint,
+            use_fp16=False,
+            fp16_scale_growth=1e-3,
+            # schedule_sampler=None,
+            weight_decay=0.0,
+            lr_anneal_steps=0,
+            iterations=10001,
+            load_submodule_name='',
+            ignore_resume_opt=False,
+            model_name='rec',
+            use_amp=False,
+            compile=False,
+            **kwargs):
+        self.pool_512 = th.nn.AdaptiveAvgPool2d((512, 512))
+        self.pool_256 = th.nn.AdaptiveAvgPool2d((256, 256))
+        self.pool_128 = th.nn.AdaptiveAvgPool2d((128, 128))
+        self.pool_64 = th.nn.AdaptiveAvgPool2d((64, 64))
+        self.rec_model = rec_model
+        self.loss_class = loss_class
+        # self.diffusion = diffusion
+        # self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
+        self.data = data
+        self.eval_data = eval_data
+        self.batch_size = batch_size
+        self.microbatch = microbatch if microbatch > 0 else batch_size
+        self.lr = lr
+        self.ema_rate = ([ema_rate] if isinstance(ema_rate, float) else
+                         [float(x) for x in ema_rate.split(",")])
+        self.log_interval = log_interval
+        self.eval_interval = eval_interval
+        self.save_interval = save_interval
+        self.iterations = iterations
+        self.resume_checkpoint = resume_checkpoint
+        self.use_fp16 = use_fp16
+        self.fp16_scale_growth = fp16_scale_growth
+        self.weight_decay = weight_decay
+        self.lr_anneal_steps = lr_anneal_steps
+
+        self.step = 0
+        self.resume_step = 0
+        # self.global_batch = self.batch_size * dist.get_world_size()
+        self.global_batch = self.batch_size * dist_util.get_world_size()
+
+        self.sync_cuda = th.cuda.is_available()
+
+        # self._load_and_sync_parameters(load_submodule_name)
+        self._load_and_sync_parameters()
+
+        # ! force bf16
+        # https://zhuanlan.zhihu.com/p/671165275
+        self.dtype = th.float32
+
+        if use_amp: 
+            if th.cuda.get_device_capability(0)[0] < 8:
+                self.dtype = th.float16 # e.g., v100
+            else:
+                self.dtype = th.bfloat16 # e.g., a100 / a6000
+
+        self.mp_trainer_rec = MixedPrecisionTrainer(
+            model=self.rec_model,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name=model_name,
+            use_amp=use_amp)
+        self.writer = SummaryWriter(log_dir=f'{logger.get_dir()}/runs')
+
+        self.opt = AdamW(self._init_optim_groups(kwargs))
+
+        if dist_util.get_rank() == 0:
+            logger.log(self.opt)
+
+        if self.resume_step:
+            if not ignore_resume_opt:
+                self._load_optimizer_state()
+            else:
+                logger.warn("Ignoring optimizer state from checkpoint.")
+            # Model was resumed, either due to a restart or a checkpoint
+            # being specified at the command line.
+            # self.ema_params = [
+            #     self._load_ema_parameters(rate, load_submodule_name) for rate in self.ema_rate
+            # ]
+
+            self.ema_params = [
+                self._load_ema_parameters(
+                    rate,
+                    self.rec_model,
+                    self.mp_trainer_rec,
+                    model_name=self.mp_trainer_rec.model_name)
+                for rate in self.ema_rate
+            ]
+        else:
+            self.ema_params = [
+                copy.deepcopy(self.mp_trainer_rec.master_params)
+                for _ in range(len(self.ema_rate))
+            ]
+
+        # compile
+        self.compile = compile
+        if compile:
+            logger.log('compiling... ignore vit_decoder')
+            self.model = th.compile(self.model)
+
+            # # self.rec_model.encoder  = th.compile(self.rec_model.encoder)
+            # self.rec_model.decoder.decoder_pred = th.compile(
+            #     self.rec_model.decoder.decoder_pred)
+            # # self.rec_model.decoder.triplane_decoder  = th.compile(self.rec_model.decoder.triplane_decoder)
+            # for module_k, sub_module in self.rec_model.decoder.superresolution.items(
+            # ):
+            #     self.rec_model.decoder.superresolution[module_k] = th.compile(
+            #         sub_module)
+
+        if th.cuda.is_available():
+            self.use_ddp = True
+
+            self.rec_model = th.nn.SyncBatchNorm.convert_sync_batchnorm(
+                self.rec_model)
+
+            self.rec_model = DDP(
+                self.rec_model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            if dist_util.get_world_size() > 1:
+                logger.warn("Distributed training requires CUDA. "
+                            "Gradients will not be synchronized properly!")
+            self.use_ddp = False
+            self.rec_model = self.rec_model
+
+        self.novel_view_poses = None
+        th.cuda.empty_cache()
+
+    def _init_optim_groups(self, kwargs):
+        raise NotImplementedError('')
+
+    def _load_and_sync_parameters(self, submodule_name=''):
+        # resume_checkpoint, self.resume_step = find_resume_checkpoint() or self.resume_checkpoint
+        resume_checkpoint = self.resume_checkpoint  # * default behaviour
+        # logger.log('resume_checkpoint', resume_checkpoint, self.resume_checkpoint)
+
+        if resume_checkpoint:
+            self.resume_step = parse_resume_step_from_filename(
+                resume_checkpoint)
+            if dist_util.get_rank() == 0:
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                if submodule_name != '':
+                    model_state_dict = getattr(self.rec_model,
+                                               submodule_name).state_dict()
+                    if dist_util.get_rank() == 0:
+                        logger.log('loading submodule: ', submodule_name)
+                else:
+                    model_state_dict = self.rec_model.state_dict()
+
+                # model = self.rec_model
+
+                # for k, v in resume_state_dict.items():
+                #     if k in model_state_dict.keys() and v.size(
+                #     ) == model_state_dict[k].size():
+                #         model_state_dict[k] = v
+                #     else:
+                #         logger.log('!!!! ignore key: ', k, ": ", v.size())
+
+                for k, v in resume_state_dict.items():
+                    if '._orig_mod' in k:  # prefix in torch.compile
+                        k = k.replace('._orig_mod', '')
+                    if k in model_state_dict.keys():
+                        if v.size() == model_state_dict[k].size():
+                            model_state_dict[k] = v
+                            # model_state_dict[k].copy_(v)
+                        else:
+                            # if k == 'encoder.conv_in.weight':
+                            if False:
+                                model_state_dict[k][:, :v.shape[1]] = v
+                                model_state_dict[k][:, v.shape[1]:] = 0
+
+                                logger.log('!!!! partially load: ', k, ": ",
+                                           v.size(), "state_dict: ",
+                                           model_state_dict[k].size())
+
+                            # if v.ndim > 1:
+                            #     model_state_dict[k][:v.shape[0], :v.shape[1], ...] = v # load the decoder
+                            #     model_state_dict[k][v.shape[0]:, v.shape[1]:, ...] = 0
+                            # else:
+                            #     model_state_dict[k][:v.shape[0], ...] = v # load the decoder
+                            #     model_state_dict[k][v.shape[0]:, ...] = 0
+                            # logger.log('!!!! size mismatch, partially load: ', k, ": ", v.size(), "state_dict: ", model_state_dict[k].size())
+                            else:
+                                logger.log('!!!! size mismatch, ignore: ', k,
+                                           ": ", v.size(), "state_dict: ",
+                                           model_state_dict[k].size())
+
+                    # elif 'decoder.vit_decoder.blocks' in k:
+                    #     # st()
+                    #     # load from 2D ViT pre-trained into 3D ViT blocks.
+                    #     assert len(model.decoder.vit_decoder.blocks[0].vit_blks
+                    #                ) == 2  # assert depth=2 here.
+                    #     fusion_ca_depth = len(
+                    #         model.decoder.vit_decoder.blocks[0].vit_blks)
+                    #     vit_subblk_index = int(k.split('.')[3])
+                    #     vit_blk_keyname = ('.').join(k.split('.')[4:])
+                    #     fusion_blk_index = vit_subblk_index // fusion_ca_depth
+                    #     fusion_blk_subindex = vit_subblk_index % fusion_ca_depth
+                    #     model_state_dict[
+                    #         f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'] = v
+                    #     logger.log('load 2D ViT weight: {}'.format(
+                    #         f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'
+                    #     ))
+
+                    else:
+                        logger.log(
+                            '!!!! ignore key, not in the model_state_dict: ',
+                            k, ": ", v.size())
+
+                logger.log('model loading finished')
+
+                if submodule_name != '':
+                    getattr(self.rec_model,
+                            submodule_name).load_state_dict(model_state_dict,
+                                                            strict=True)
+                else:
+                    self.rec_model.load_state_dict(model_state_dict,
+                                                   strict=False)
+                    #    strict=True)
+
+        if dist_util.get_world_size() > 1:
+            # dist_util.sync_params(self.model.named_parameters())
+            dist_util.sync_params(self.rec_model.parameters())
+            logger.log('synced params')
+
+    def _load_ema_parameters(self,
+                             rate,
+                             model=None,
+                             mp_trainer=None,
+                             model_name='ddpm'):
+
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+        if model is None:
+            model = self.rec_model
+
+        ema_params = copy.deepcopy(mp_trainer.master_params)
+
+        # main_checkpoint, _ = find_resume_checkpoint(
+        #     self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        main_checkpoint = self.resume_checkpoint
+        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step,
+                                             rate, model_name)
+        if ema_checkpoint and model_name == 'ddpm':
+
+            if dist_util.get_rank() == 0:
+
+                if not Path(ema_checkpoint).exists():
+                    logger.log(
+                        f"failed to load EMA from checkpoint: {ema_checkpoint}, not exist"
+                    )
+                    return
+
+                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                state_dict = dist_util.load_state_dict(
+                    ema_checkpoint, map_location=map_location)
+
+                model_ema_state_dict = model.state_dict()
+
+                for k, v in state_dict.items():
+                    if k in model_ema_state_dict.keys() and v.size(
+                    ) == model_ema_state_dict[k].size():
+                        model_ema_state_dict[k] = v
+
+                    elif 'IN' in k and getattr(model, 'decomposed_IN', False):
+                        model_ema_state_dict[k.replace(
+                            'IN', 'IN.IN')] = v  # decomposed IN
+
+                    else:
+                        logger.log('ignore key: ', k, ": ", v.size())
+
+                ema_params = mp_trainer.state_dict_to_master_params(
+                    model_ema_state_dict)
+
+                del state_dict
+
+        # logger.log('ema mark 3, ', model_name, )
+
+        # ! debugging, remove to check which key fails.
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(ema_params)
+
+        # logger.log('ema mark 4, ', model_name, )
+        # del ema_params
+        return ema_params
+
+    def _load_optimizer_state(self):
+        main_checkpoint, _ = find_resume_checkpoint()
+        if self.resume_checkpoint == '':
+            main_checkpoint, _ = find_resume_checkpoint()
+        else:
+            main_checkpoint = self.resume_checkpoint
+        opt_checkpoint = bf.join(bf.dirname(main_checkpoint),
+                                 f"opt{self.resume_step:07}.pt")
+        # st()
+        if bf.exists(opt_checkpoint):
+            logger.log(
+                f"loading optimizer state from checkpoint: {opt_checkpoint}")
+
+            map_location = {
+                'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+            }  # configure map_location properly
+
+            try:
+                state_dict = dist_util.load_state_dict(opt_checkpoint,
+                                                    map_location=map_location)
+                self.opt.load_state_dict(state_dict)
+            except Exception as e:
+                logger.log(e)
+
+            # self.opt.load_state_dict({k: v for k, v in state_dict.items() if k in self.opt.state_dict()})
+
+            del state_dict
+        else:
+            logger.log('optimizer state load fail: {}'.format(opt_checkpoint))
+
+    def run_loop(self, batch=None):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            # if isinstance(self.data, list):
+            #     if self.step <= self.data[2]:
+            #         batch = next(self.data[1])
+            #     else:
+            #         batch = next(self.data[0])
+            # else:
+
+            # for _ in trange(10000): # io profiling
+            batch = next(self.data) #  | 56/10000 [00:13<40:48,  4.06it/s]
+
+            # batch = next(self.data)
+            # ! comment out
+            # if self.novel_view_poses is None:
+            #     self.novel_view_poses = th.roll(batch['c'], 1, 0).to(
+            #         dist_util.dev())  # save for eval visualization use
+
+            self.run_step(batch)
+
+            if self.step % 1000 == 0:
+                dist_util.synchronize()
+                th.cuda.empty_cache()  # avoid memory leak
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                try:
+                    out = logger.dumpkvs()
+                    # * log to tensorboard
+                    for k, v in out.items():
+                        self.writer.add_scalar(f'Loss/{k}', v,
+                                            self.step + self.resume_step)
+                except Exception as e: # disk no quota
+                    logger.log(e)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                # if self.step % self.eval_interval == 0 and (self.step +
+                #                                             self.resume_step) != 0:
+                # if self.step % self.eval_interval == 0:  # ! for debugging
+                # if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    try:
+                        self.eval_loop()
+                    except Exception as e:
+                        logger.log(e)
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0 and self.step != 0:
+                self.save()
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                logger.log('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step -
+                        1) % self.save_interval != 0 and self.step != 1:
+                    self.save()
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0 and self.step != 1:
+            self.save()
+
+    @th.no_grad()
+    def eval_loop(self):
+        raise NotImplementedError('')
+
+    def run_step(self, batch, *args):
+        self.forward_backward(batch)
+        took_step = self.mp_trainer_rec.optimize(self.opt)
+        if took_step:
+            self._update_ema()
+        self._anneal_lr()
+        self.log_step()
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # th.cuda.empty_cache()
+        raise NotImplementedError('')
+
+    def _update_ema(self):
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            update_ema(params, self.mp_trainer_rec.master_params, rate=rate)
+
+    def _anneal_lr(self):
+        if not self.lr_anneal_steps:
+            return
+        frac_done = (self.step + self.resume_step) / self.lr_anneal_steps
+        lr = self.lr * (1 - frac_done)
+        for param_group in self.opt.param_groups:
+            param_group["lr"] = lr
+
+    def log_step(self):
+        logger.logkv("step", self.step + self.resume_step)
+        logger.logkv("samples",
+                     (self.step + self.resume_step + 1) * self.global_batch)
+
+    def save(self):
+
+        def save_checkpoint(rate, params):
+            state_dict = self.mp_trainer_rec.master_params_to_state_dict(
+                params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {rate}...")
+                if not rate:
+                    filename = f"model_rec{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_rec_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        try:
+            save_checkpoint(
+                0, self.mp_trainer_rec.master_params)  # avoid OOM when saving ckpt
+            for rate, params in zip(self.ema_rate, self.ema_params):
+                save_checkpoint(rate, params)
+
+            # ! save optimizer
+            if dist.get_rank() == 0:
+                with bf.BlobFile(
+                        bf.join(get_blob_logdir(),
+                                f"opt{(self.step+self.resume_step):07d}.pt"),
+                        "wb",
+                ) as f:
+                    th.save(self.opt.state_dict(), f)
+
+        except Exception as e: # disk quota exceed
+            logger.log(e)
+
+        th.cuda.empty_cache()
+
+        dist.barrier()
+
+
+class TrainLoop3DRec(TrainLoopBasic):
+
+    def __init__(
+            self,
+            *,
+            rec_model,
+            loss_class,
+            # diffusion,
+            data,
+            eval_data,
+            batch_size,
+            microbatch,
+            lr,
+            ema_rate,
+            log_interval,
+            eval_interval,
+            save_interval,
+            resume_checkpoint,
+            use_fp16=False,
+            fp16_scale_growth=1e-3,
+            # schedule_sampler=None,
+            weight_decay=0.0,
+            lr_anneal_steps=0,
+            iterations=10001,
+            load_submodule_name='',
+            ignore_resume_opt=False,
+            model_name='rec',
+            use_amp=False,
+            compile=False,
+            **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         compile=compile,
+                         **kwargs)
+
+        # self.rec_model = self.ddp_model
+        # self._prepare_nvs_pose() # for eval novelview visualization
+        th.cuda.empty_cache()
+
+        self.triplane_scaling_divider = 1.0
+        self.latent_name = 'latent_normalized_2Ddiffusion'  # normalized triplane latent
+        self.render_latent_behaviour = 'decode_after_vae'  # directly render using triplane operations
+
+    @th.inference_mode()
+    def render_video_given_triplane(self,
+                                    planes,
+                                    rec_model,
+                                    name_prefix='0',
+                                    save_img=False,
+                                    render_reference=None,
+                                    save_mesh=False, render_reference_length=40, 
+                                    return_gen_imgs=False):
+
+        planes *= self.triplane_scaling_divider  # if setting clip_denoised=True, the sampled planes will lie in [-1,1]. Thus, values beyond [+- std] will be abandoned in this version. Move to IN for later experiments.
+
+        # sr_w_code = getattr(self.ddp_rec_model.module.decoder, 'w_avg', None)
+        # sr_w_code = None
+        batch_size = planes.shape[0]
+
+        # if sr_w_code is not None:
+        #     sr_w_code = sr_w_code.reshape(1, 1,
+        #                                   -1).repeat_interleave(batch_size, 0)
+
+        # used during diffusion sampling inference
+        # if not save_img:
+
+        # ! mesh
+
+        if planes.shape[1] == 16:  # ffhq/car
+            ddpm_latent = {
+                self.latent_name: planes[:, :12],
+                'bg_plane': planes[:, 12:16],
+            }
+        else:
+            ddpm_latent = {
+                self.latent_name: planes,
+            }
+
+        ddpm_latent.update(
+            rec_model(latent=ddpm_latent,
+                      behaviour='decode_after_vae_no_render'))
+
+        # if export_mesh:
+        # if True:
+        if save_mesh:
+            # mesh_size = 512
+            mesh_size = 192
+            # mesh_size = 384
+            # mesh_size = 320
+            # mesh_thres = 3 # TODO, requires tuning
+            # mesh_thres = 5 # TODO, requires tuning
+            mesh_thres = 10  # TODO, requires tuning
+            dump_path = f'{logger.get_dir()}/mesh/'
+
+            os.makedirs(dump_path, exist_ok=True)
+
+            grid_out = rec_model(
+                latent=ddpm_latent,
+                grid_size=mesh_size,
+                behaviour='triplane_decode_grid',
+            )
+
+            vtx, faces = mcubes.marching_cubes(
+                grid_out['sigma'].squeeze(0).squeeze(-1).cpu().numpy(),
+                mesh_thres)
+            vtx = vtx / (mesh_size - 1) * 2 - 1
+
+            # vtx_tensor = th.tensor(vtx, dtype=th.float32, device=dist_util.dev()).unsqueeze(0)
+            # vtx_colors = self.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].squeeze(0).cpu().numpy()  # (0, 1)
+            # vtx_colors = (vtx_colors * 255).astype(np.uint8)
+
+            # mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+            mesh = trimesh.Trimesh(
+                vertices=vtx,
+                faces=faces,
+            )
+
+            mesh_dump_path = os.path.join(dump_path, f'{name_prefix}.ply')
+            mesh.export(mesh_dump_path, 'ply')
+
+            print(f"Mesh dumped to {dump_path}")
+            del grid_out, mesh
+            th.cuda.empty_cache()
+            # return
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_{name_prefix}.mp4',
+            mode='I',
+            fps=15,
+            codec='libx264')
+
+        if planes.shape[1] == 16:  # ffhq/car
+            ddpm_latent = {
+                self.latent_name: planes[:, :12],
+                'bg_plane': planes[:, 12:16],
+            }
+        else:
+            ddpm_latent = {
+                self.latent_name: planes,
+            }
+
+        ddpm_latent.update(
+            rec_model(latent=ddpm_latent,
+                      behaviour='decode_after_vae_no_render'))
+
+        # planes = planes.repeat_interleave(micro['c'].shape[0], 0)
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        # micro_batchsize = 2
+        # micro_batchsize = batch_size
+
+        if render_reference is None:
+            render_reference = self.eval_data  # compat
+        else:  # use train_traj
+            for key in ['ins', 'bbox', 'caption']:
+                if key in render_reference:
+                    render_reference.pop(key)
+            # render_reference.pop('bbox')
+            # render_reference.pop('caption')
+
+            # compat lst for enumerate
+            render_reference = [{
+                k: v[idx:idx + 1]
+                for k, v in render_reference.items()
+            } for idx in range(render_reference_length)]
+
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+        if return_gen_imgs:
+            gen_imgs = []
+        for i, batch in enumerate(tqdm(render_reference)):
+            micro = {
+                k: v.to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+            # micro = {'c': batch['c'].to(dist_util.dev()).repeat_interleave(batch_size, 0)}
+
+            # all_pred = []
+            pred = rec_model(
+                img=None,
+                c=micro['c'],
+                latent=ddpm_latent,
+                # latent={
+                #     # k: v.repeat_interleave(micro['c'].shape[0], 0) if v is not None else None
+                #     k: v.repeat_interleave(micro['c'].shape[0], 0) if v is not None else None
+                #     for k, v in ddpm_latent.items()
+                # },
+                behaviour='triplane_dec')
+
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            # save viridis_r depth
+            pred_depth = pred_depth.cpu()[0].permute(1, 2, 0).numpy()
+            pred_depth = (plt.cm.viridis(pred_depth[..., 0])[..., :3]) * 2 - 1
+            pred_depth = th.from_numpy(pred_depth).to(
+                pred['image_raw'].device).permute(2, 0, 1).unsqueeze(0)
+            # st()
+            # pred_depth =
+
+            if 'image_sr' in pred:
+
+                gen_img = pred['image_sr']
+
+                if pred['image_sr'].shape[-1] == 512:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), gen_img,
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                elif pred['image_sr'].shape[-1] == 128:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']), pred['image_sr'],
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+            else:
+                gen_img = pred['image_raw']
+                if return_gen_imgs:
+                    gen_imgs.append(gen_img)
+
+                pred_vis = th.cat(
+                    [
+                        # self.pool_128(micro['img']),
+                        self.pool_128(gen_img),
+                        # self.pool_128(pred_depth.repeat_interleave(3, dim=1))
+                        self.pool_128(pred_depth)
+                    ],
+                    dim=-1)  # B, 3, H, W
+
+            if save_img:
+                for batch_idx in range(gen_img.shape[0]):
+                    sampled_img = Image.fromarray(
+                        (gen_img[batch_idx].permute(1, 2, 0).cpu().numpy() *
+                         127.5 + 127.5).clip(0, 255).astype(np.uint8))
+                    if sampled_img.size != (512, 512):
+                        sampled_img = sampled_img.resize(
+                            (128, 128), Image.HAMMING)  # for shapenet
+                    sampled_img.save(logger.get_dir() +
+                                     '/FID_Cals/{}_{}.png'.format(
+                                         int(name_prefix) * batch_size +
+                                         batch_idx, i))
+                    # print('FID_Cals/{}_{}.png'.format(int(name_prefix)*batch_size+batch_idx, i))
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            # if vis.shape[0] > 1:
+            #     vis = np.concatenate(np.split(vis, vis.shape[0], axis=0),
+            #                          axis=-3)
+
+            # if not save_img:
+            for j in range(vis.shape[0]
+                           ):  # ! currently only export one plane at a time
+                video_out.append_data(vis[j])
+
+        # if not save_img:
+        video_out.close()
+        del video_out
+        print('logged video to: ',
+              f'{logger.get_dir()}/triplane_{name_prefix}.mp4')
+
+        if return_gen_imgs:
+            return th.cat(gen_imgs)
+
+        del vis, pred_vis, micro, pred,
+
+    def _init_optim_groups(self, kwargs):
+        optim_groups = []
+        if kwargs.get('decomposed', False):  # AE
+            if not kwargs.get('ignore_encoder'):
+                optim_groups += [
+                # vit encoder
+                {
+                    'name': 'encoder',
+                    'params': self.mp_trainer_rec.model.encoder.parameters(),
+                    'lr': kwargs['encoder_lr'],
+                    'weight_decay': kwargs['encoder_weight_decay']
+                },
+                ]
+
+            optim_groups += [
+
+                # vit decoder backbone
+                {
+                    'name':
+                    'decoder.vit_decoder',
+                    'params':
+                    self.mp_trainer_rec.model.decoder.vit_decoder.parameters(),
+                    'lr':
+                    kwargs['vit_decoder_lr'],
+                    'weight_decay':
+                    kwargs['vit_decoder_wd']
+                },
+            ]
+
+            # gs rendering no MLP
+            if not (
+            isinstance(
+                    self.mp_trainer_rec.model.decoder.triplane_decoder,
+                    GaussianRenderer) or 
+            isinstance(
+                    self.mp_trainer_rec.model.decoder.triplane_decoder,
+                    GaussianRenderer2DGS)):
+
+
+                optim_groups.append(
+                    # triplane decoder, may include bg synthesis network
+                    {
+                        'name':
+                        'decoder.triplane_decoder',
+                        'params':
+                        self.mp_trainer_rec.model.decoder.triplane_decoder.
+                        parameters(),
+                        'lr':
+                        kwargs['triplane_decoder_lr'],
+                        # 'weight_decay': self.weight_decay
+                    }, )
+
+            if self.mp_trainer_rec.model.decoder.superresolution is not None:
+                optim_groups.append({
+                    'name':
+                    'decoder.superresolution',
+                    'params':
+                    self.mp_trainer_rec.model.decoder.superresolution.
+                    parameters(),
+                    'lr':
+                    kwargs['super_resolution_lr'],
+                })
+
+            if self.mp_trainer_rec.model.dim_up_mlp is not None:
+                optim_groups.append({
+                    'name':
+                    'dim_up_mlp',
+                    'params':
+                    self.mp_trainer_rec.model.dim_up_mlp.parameters(),
+                    'lr':
+                    kwargs['encoder_lr'],
+                    # 'weight_decay':
+                    # self.weight_decay
+                })
+
+            # add 3D aware operators
+            if self.mp_trainer_rec.model.decoder.decoder_pred_3d is not None:
+                optim_groups.append({
+                    'name':
+                    'decoder_pred_3d',
+                    'params':
+                    self.mp_trainer_rec.model.decoder.decoder_pred_3d.
+                    parameters(),
+                    'lr':
+                    kwargs['vit_decoder_lr'],
+                    'weight_decay':
+                    kwargs['vit_decoder_wd']
+                })
+
+            if self.mp_trainer_rec.model.decoder.transformer_3D_blk is not None:
+                optim_groups.append({
+                    'name':
+                    'decoder_transformer_3D_blk',
+                    'params':
+                    self.mp_trainer_rec.model.decoder.transformer_3D_blk.
+                    parameters(),
+                    'lr':
+                    kwargs['vit_decoder_lr'],
+                    'weight_decay':
+                    kwargs['vit_decoder_wd']
+                })
+
+            if self.mp_trainer_rec.model.decoder.logvar is not None:
+                optim_groups.append({
+                    'name':
+                    'decoder_logvar',
+                    'params':
+                    self.mp_trainer_rec.model.decoder.logvar,
+                    'lr':
+                    kwargs['vit_decoder_lr'],
+                    'weight_decay':
+                    kwargs['vit_decoder_wd']
+                })
+
+            if self.mp_trainer_rec.model.decoder.decoder_pred is not None:
+                optim_groups.append(
+                    # MLP triplane SR
+                    {
+                        'name':
+                        'decoder.decoder_pred',
+                        'params':
+                        self.mp_trainer_rec.model.decoder.decoder_pred.
+                        parameters(),
+                        'lr':
+                        kwargs['vit_decoder_lr'],
+                        # 'weight_decay': 0
+                        'weight_decay':
+                        kwargs['vit_decoder_wd']
+                    }, )
+
+            if self.mp_trainer_rec.model.confnet is not None:
+                optim_groups.append({
+                    'name':
+                    'confnet',
+                    'params':
+                    self.mp_trainer_rec.model.confnet.parameters(),
+                    'lr':
+                    1e-5,  # as in unsup3d
+                })
+
+            # self.opt = AdamW(optim_groups)
+
+            if dist_util.get_rank() == 0:
+                logger.log('using independent optimizer for each components')
+        else:
+            optim_groups = [
+                dict(name='mp_trainer.master_params',
+                     params=self.mp_trainer_rec.master_params,
+                     lr=self.lr,
+                     weight_decay=self.weight_decay)
+            ]
+
+        logger.log(optim_groups)
+
+        return optim_groups
+
+    @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    def eval_novelview_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        all_loss_dict = []
+        novel_view_micro = {}
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i == 0:
+                novel_view_micro = {
+                    k:
+                    v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    if isinstance(v, th.Tensor) else v[0:1]
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k:
+                    v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            pred = self.rec_model(img=novel_view_micro['img_to_encoder'],
+                                  c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+            # targe
+
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            if 'image_sr' in pred:
+
+                if pred['image_sr'].shape[-1] == 512:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), pred['image_sr'],
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                elif pred['image_sr'].shape[-1] == 256:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_256(pred['image_raw']), pred['image_sr'],
+                        self.pool_256(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                else:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred['image_sr']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+            else:
+                # pred_vis = th.cat([
+                #     self.pool_64(micro['img']), pred['image_raw'],
+                #     pred_depth.repeat_interleave(3, dim=1)
+                # ],
+                #                   dim=-1)  # B, 3, H, W
+
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']),
+                    self.pool_128(pred['image_raw']),
+                    self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+                  'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+                                   self.step + self.resume_step)
+        del video_out
+        # del pred_vis
+        # del pred
+
+        th.cuda.empty_cache()
+
+    # @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    @th.inference_mode()
+    def eval_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+        all_loss_dict = []
+        self.rec_model.eval()
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {
+                k: v.to(dist_util.dev()) if isinstance(v, th.Tensor) else v
+                for k, v in batch.items()
+            }
+
+            pred = self.rec_model(img=micro['img_to_encoder'],
+                                  c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+
+            # if last_batch or not self.use_ddp:
+            #     loss, loss_dict = self.loss_class(pred, target)
+            # else:
+            #     with self.ddp_model.no_sync():  # type: ignore
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+            # gt_vis = th.cat([micro['img'], micro['img']], dim=-1) # TODO, fail to load depth. range [0, 1]
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+            # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(1,2,0).cpu().numpy() # ! pred in range[-1, 1]
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+
+                if pred['image_sr'].shape[-1] == 512:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), pred['image_sr'],
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                elif pred['image_sr'].shape[-1] == 256:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_256(pred['image_raw']), pred['image_sr'],
+                        self.pool_256(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                else:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred['image_sr']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']),
+                    self.pool_128(pred['image_raw']),
+                    self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores.json'), 'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/Rec/{k}', v,
+                                   self.step + self.resume_step)
+
+        th.cuda.empty_cache()
+        # if 'SuperresolutionHybrid8X' in self.rendering_kwargs: # ffhq/afhq
+        #     self.eval_novelview_loop_trajectory()
+        # else:
+        self.eval_novelview_loop()
+        self.rec_model.train()
+
+    @th.inference_mode()
+    def eval_novelview_loop_trajectory(self):
+        # novel view synthesis given evaluation camera trajectory
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            video_out = imageio.get_writer(
+                f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}_batch_{i}.mp4',
+                mode='I',
+                fps=60,
+                codec='libx264')
+
+            for idx, c in enumerate(self.all_nvs_params):
+                pred = self.rec_model(img=micro['img_to_encoder'],
+                                      c=c.unsqueeze(0).repeat_interleave(
+                                          micro['img'].shape[0],
+                                          0))  # pred: (B, 3, 64, 64)
+                #   c=micro['c'])  # pred: (B, 3, 64, 64)
+
+                # normalize depth
+                # if True:
+                pred_depth = pred['image_depth']
+                pred_depth = (pred_depth - pred_depth.min()) / (
+                    pred_depth.max() - pred_depth.min())
+                if 'image_sr' in pred:
+
+                    if pred['image_sr'].shape[-1] == 512:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_512(pred['image_raw']), pred['image_sr'],
+                            self.pool_512(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                    elif pred['image_sr'].shape[-1] == 256:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_256(pred['image_raw']), pred['image_sr'],
+                            self.pool_256(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                    else:
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_128(pred['image_raw']),
+                            self.pool_128(pred['image_sr']),
+                            self.pool_128(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                else:
+
+                    # st()
+                    pred_vis = th.cat([
+                        self.pool_128(micro['img']),
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    # ! cooncat h dim
+                    pred_vis = pred_vis.permute(0, 2, 3, 1).flatten(0,
+                                                                    1)  # H W 3
+
+                # vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+                # vis = pred_vis.permute(1,2,0).cpu().numpy()
+                vis = pred_vis.cpu().numpy()
+                vis = vis * 127.5 + 127.5
+                vis = vis.clip(0, 255).astype(np.uint8)
+
+                # for j in range(vis.shape[0]):
+                # video_out.append_data(vis[j])
+                video_out.append_data(vis)
+
+            video_out.close()
+
+        th.cuda.empty_cache()
+
+    def _prepare_nvs_pose(self):
+
+        device = dist_util.dev()
+
+        fov_deg = 18.837  # for ffhq/afhq
+        intrinsics = FOV_to_intrinsics(fov_deg, device=device)
+
+        all_nvs_params = []
+
+        pitch_range = 0.25
+        yaw_range = 0.35
+        num_keyframes = 10  # how many nv poses to sample from
+        w_frames = 1
+
+        cam_pivot = th.Tensor(
+            self.rendering_kwargs.get('avg_camera_pivot')).to(device)
+        cam_radius = self.rendering_kwargs.get('avg_camera_radius')
+
+        for frame_idx in range(num_keyframes):
+
+            cam2world_pose = LookAtPoseSampler.sample(
+                3.14 / 2 + yaw_range * np.sin(2 * 3.14 * frame_idx /
+                                              (num_keyframes * w_frames)),
+                3.14 / 2 - 0.05 +
+                pitch_range * np.cos(2 * 3.14 * frame_idx /
+                                     (num_keyframes * w_frames)),
+                cam_pivot,
+                radius=cam_radius,
+                device=device)
+
+            camera_params = th.cat(
+                [cam2world_pose.reshape(-1, 16),
+                 intrinsics.reshape(-1, 9)], 1)
+
+            all_nvs_params.append(camera_params)
+
+        self.all_nvs_params = th.cat(all_nvs_params, 0)
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # th.cuda.empty_cache()
+        self.mp_trainer_rec.zero_grad()
+        batch_size = batch['img_to_encoder'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # wrap forward within amp
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(img=micro['img_to_encoder'],
+                                      c=micro['c'])  # pred: (B, 3, 64, 64)
+                target = micro
+
+                # ! only enable in ffhq dataset
+                conf_sigma_percl = None
+                conf_sigma_percl_flip = None
+                if 'conf_sigma' in pred:
+                    # all_conf_sigma_l1, all_conf_sigma_percl = pred['conf_sigma']
+                    # all_conf_sigma_l1  = pred['conf_sigma']
+                    all_conf_sigma_l1 = th.nn.functional.interpolate(
+                        pred['conf_sigma'],
+                        size=pred['image_raw'].shape[-2:],
+                        mode='bilinear'
+                    )  # dynamically resize to target img size
+                    conf_sigma_l1 = all_conf_sigma_l1[:, :1]
+                    conf_sigma_l1_flip = all_conf_sigma_l1[:, 1:]
+                    # conf_sigma_percl = all_conf_sigma_percl[:,:1]
+                    # conf_sigma_percl_flip = all_conf_sigma_percl[:,1:]
+                else:
+                    conf_sigma = None
+                    conf_sigma_l1 = None
+                    conf_sigma_l1_flip = None
+
+                with self.rec_model.no_sync():  # type: ignore
+                    loss, loss_dict, fg_mask = self.loss_class(
+                        pred,
+                        target,
+                        step=self.step + self.resume_step,
+                        test_mode=False,
+                        return_fg_mask=True,
+                        conf_sigma_l1=conf_sigma_l1,
+                        conf_sigma_percl=conf_sigma_percl)
+
+                if self.loss_class.opt.symmetry_loss:
+                    loss_dict['conf_sigma_log'] = conf_sigma_l1.log()
+                    pose, intrinsics = micro['c'][:, :16].reshape(
+                        -1, 4, 4), micro['c'][:, 16:]
+                    flipped_pose = flip_yaw(pose)
+                    mirror_c = th.cat(
+                        [flipped_pose.reshape(-1, 16), intrinsics], -1)
+
+                    nvs_pred = self.rec_model(latent={
+                        k: v
+                        for k, v in pred.items() if 'latent' in k
+                    },
+                                              c=mirror_c,
+                                              behaviour='triplane_dec',
+                                              return_raw_only=True)
+
+                    # concat data for supervision
+                    nvs_gt = {
+                        k: th.flip(target[k], [-1])
+                        for k in
+                        ['img']  # fliplr leads to wrong color; B 3 H W shape
+                    }
+                    flipped_fg_mask = th.flip(fg_mask, [-1])
+
+                    # if 'conf_sigma' in pred:
+                    #     conf_sigma = th.flip(pred['conf_sigma'], [-1])
+                    #     conf_sigma = th.nn.AdaptiveAvgPool2d(fg_mask.shape[-2:])(conf_sigma) # dynamically resize to target img size
+                    # else:
+                    #     conf_sigma=None
+
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss_symm, loss_dict_symm = self.loss_class.calc_2d_rec_loss(
+                            nvs_pred['image_raw'],
+                            nvs_gt['img'],
+                            flipped_fg_mask,
+                            # test_mode=True,
+                            test_mode=False,
+                            step=self.step + self.resume_step,
+                            # conf_sigma=conf_sigma,
+                            conf_sigma_l1=conf_sigma_l1_flip,
+                            conf_sigma_percl=conf_sigma_percl_flip)
+                        # )
+                        loss += (loss_symm * 1.0)  # as in unsup3d
+                        # loss += (loss_symm * 0.5) # as in unsup3d
+                        # loss += (loss_symm * 0.01) # as in unsup3d
+                        # if conf_sigma is not None:
+                        #     loss += th.nn.functional.mse_loss(conf_sigma, flipped_fg_mask) * 0.001 # a log that regularizes all confidence to 1
+                        for k, v in loss_dict_symm.items():
+                            loss_dict[f'{k}_symm'] = v
+                        loss_dict[
+                            'flip_conf_sigma_log'] = conf_sigma_l1_flip.log()
+
+                # ! add density-reg in eg3d, tv-loss
+
+                if self.loss_class.opt.density_reg > 0 and self.step % self.loss_class.opt.density_reg_every == 0:
+
+                    initial_coordinates = th.rand(
+                        (batch_size, 1000, 3),
+                        device=dist_util.dev()) * 2 - 1  # [-1, 1]
+                    perturbed_coordinates = initial_coordinates + th.randn_like(
+                        initial_coordinates
+                    ) * self.loss_class.opt.density_reg_p_dist
+                    all_coordinates = th.cat(
+                        [initial_coordinates, perturbed_coordinates], dim=1)
+
+                    sigma = self.rec_model(
+                        latent=pred['latent'],
+                        coordinates=all_coordinates,
+                        directions=th.randn_like(all_coordinates),
+                        behaviour='triplane_renderer',
+                    )['sigma']
+
+                    sigma_initial = sigma[:, :sigma.shape[1] // 2]
+                    sigma_perturbed = sigma[:, sigma.shape[1] // 2:]
+
+                    TVloss = th.nn.functional.l1_loss(
+                        sigma_initial,
+                        sigma_perturbed) * self.loss_class.opt.density_reg
+
+                    loss_dict.update(dict(tv_loss=TVloss))
+                    loss += TVloss
+
+            self.mp_trainer_rec.backward(loss)
+            log_rec3d_loss_dict(loss_dict)
+
+            # for name, p in self.rec_model.named_parameters():
+            #     if p.grad is None:
+            #         logger.log(f"found rec unused param: {name}")
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    def norm_depth(pred_depth):  # to [-1,1]
+                        # pred_depth = pred['image_depth']
+                        pred_depth = (pred_depth - pred_depth.min()) / (
+                            pred_depth.max() - pred_depth.min())
+                        return -(pred_depth * 2 - 1)
+
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    # infer novel view also
+                    if self.loss_class.opt.symmetry_loss:
+                        pred_nv_img = nvs_pred
+                    else:
+                        pred_nv_img = self.rec_model(
+                            img=micro['img_to_encoder'],
+                            c=self.novel_view_poses)  # pred: (B, 3, 64, 64)
+
+                    # if 'depth' in micro:
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = norm_depth(gt_depth)
+                    # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                    #                                           gt_depth.min())
+                    # if True:
+                    fg_mask = pred['image_mask'] * 2 - 1  # 0-1
+                    nv_fg_mask = pred_nv_img['image_mask'] * 2 - 1  # 0-1
+                    if 'image_depth' in pred:
+                        pred_depth = norm_depth(pred['image_depth'])
+                        pred_nv_depth = norm_depth(pred_nv_img['image_depth'])
+                    else:
+                        pred_depth = th.zeros_like(gt_depth)
+                        pred_nv_depth = th.zeros_like(gt_depth)
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+                    else:
+                        gt_img = self.pool_128(gt_img)
+                        gt_depth = self.pool_128(gt_depth)
+
+                    pred_vis = th.cat([
+                        pred_img,
+                        pred_depth.repeat_interleave(3, dim=1),
+                        fg_mask.repeat_interleave(3, dim=1),
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    if 'conf_sigma' in pred:
+                        conf_sigma_l1 = (1 / (conf_sigma_l1 + 1e-7)
+                                         ).repeat_interleave(3, dim=1) * 2 - 1
+                        pred_vis = th.cat([
+                            pred_vis,
+                            conf_sigma_l1,
+                        ], dim=-1)  # B, 3, H, W
+
+                    pred_vis_nv = th.cat([
+                        pred_nv_img['image_raw'],
+                        pred_nv_depth.repeat_interleave(3, dim=1),
+                        nv_fg_mask.repeat_interleave(3, dim=1),
+                    ],
+                                         dim=-1)  # B, 3, H, W
+
+                    if 'conf_sigma' in pred:
+                        # conf_sigma_for_vis = (1/conf_sigma).repeat_interleave(3, dim=1)
+                        # conf_sigma_for_vis = (conf_sigma_for_vis / conf_sigma_for_vis.max() ) * 2 - 1 # normalize to [-1,1]
+                        conf_sigma_for_vis_flip = (
+                            1 / (conf_sigma_l1_flip + 1e-7)).repeat_interleave(
+                                3, dim=1) * 2 - 1
+                        pred_vis_nv = th.cat(
+                            [
+                                pred_vis_nv,
+                                conf_sigma_for_vis_flip,
+                                # th.cat([conf_sigma_for_vis, flipped_fg_mask*2-1], -1)
+                            ],
+                            dim=-1)  # B, 3, H, W
+
+                    pred_vis = th.cat([pred_vis, pred_vis_nv],
+                                      dim=-2)  # cat in H dim
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img,
+                            gt_depth.repeat_interleave(3, dim=1),
+                            th.zeros_like(gt_img)
+                        ],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    if 'conf_sigma' in pred:
+                        gt_vis = th.cat([gt_vis, fg_mask],
+                                        dim=-1)  # placeholder
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    # st()
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+                    # .permute(
+                    #     0, 2, 3, 1).cpu()
+                    vis_tensor = torchvision.utils.make_grid(
+                        vis, nrow=vis.shape[-1] // 64)  # HWC
+                    torchvision.utils.save_image(
+                        vis_tensor,
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg',
+                        value_range=(-1, 1),
+                        normalize=True)
+                    # vis = vis.numpy() * 127.5 + 127.5
+                    # vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # Image.fromarray(vis).save(
+                    #     f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}.jpg')
+
+                    # self.writer.add_image(f'images',
+                    #                       vis,
+                    #                       self.step + self.resume_step,
+                    #                       dataformats='HWC')
+            return pred
+
+
+class TrainLoop3DTriplaneRec(TrainLoop3DRec):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 compile=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         compile=compile,
+                         **kwargs)
+
+    @th.inference_mode()
+    def eval_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+        all_loss_dict = []
+        self.rec_model.eval()
+
+        device = dist_util.dev()
+
+        # to get intrinsics
+        demo_pose = next(self.data)
+        intrinsics = demo_pose['c'][0][16:25].to(device)
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=24,
+            bitrate='10M',
+            codec='libx264')
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        # for i, batch in enumerate(tqdm(self.eval_data)):
+
+        cam_pivot = th.tensor([0, 0, 0], device=dist_util.dev())
+        cam_radius = 1.8
+
+        pitch_range = 0.45
+        yaw_range = 3.14  # 0.35
+        frames = 72
+
+        # TODO, use PanoHead trajectory
+        # for frame_idx in range(frames):
+
+        for pose_idx, (angle_y, angle_p) in enumerate(
+                # zip(np.linspace(-0.4, 0.4, 72), [-0.2] * 72)):
+                # zip(np.linspace(-1.57, 1.57, 72), [-1.57] * 72)):
+                # zip(np.linspace(0,3.14, 72), [0] * 72)): # check canonical pose
+                zip([0.2] * 72, np.linspace(-3.14, 3.14, 72))):
+
+            # cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.cos(2 * 3.14 * frame_idx / (frames)),
+            #                                         3.14/2 -0.05 + pitch_range * np.sin(2 * 3.14 * frame_idx / (frames)),
+            #                                         cam_pivot,
+            #                                         radius=cam_radius, device=device)
+
+            cam2world_pose = LookAtPoseSampler.sample(
+                np.pi / 2 + angle_y,
+                np.pi / 2 + angle_p,
+                # angle_p,
+                cam_pivot,
+                #    horizontal_stddev=0.1, # 0.25
+                #    vertical_stddev=0.125, # 0.35,
+                radius=cam_radius,
+                device=device)
+
+            camera_params = th.cat(
+                [cam2world_pose.reshape(-1, 16),
+                 intrinsics.reshape(-1, 9)], 1).to(dist_util.dev())
+
+            # micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+            micro = {'c': camera_params}
+
+            pred = self.rec_model(c=micro['c'])
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            pred_vis = th.cat([
+                self.pool_128(pred['image_raw']),
+                self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+            ],
+                              dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        self.rec_model.train()
+
+
+class TrainLoop3DRecTrajVis(TrainLoop3DRec):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 load_submodule_name='',
+                 ignore_resume_opt=False,
+                 model_name='rec',
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp,
+                         **kwargs)
+        self.rendering_kwargs = self.rec_model.module.decoder.triplane_decoder.rendering_kwargs  # type: ignore
+        self._prepare_nvs_pose()  # for eval novelview visualization
+
+    @th.inference_mode()
+    def eval_novelview_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            video_out = imageio.get_writer(
+                f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}_batch_{i}.mp4',
+                mode='I',
+                fps=60,
+                codec='libx264')
+
+            for idx, c in enumerate(self.all_nvs_params):
+                pred = self.rec_model(img=micro['img_to_encoder'],
+                                      c=c.unsqueeze(0).repeat_interleave(
+                                          micro['img'].shape[0],
+                                          0))  # pred: (B, 3, 64, 64)
+                #   c=micro['c'])  # pred: (B, 3, 64, 64)
+
+                # normalize depth
+                # if True:
+                pred_depth = pred['image_depth']
+                pred_depth = (pred_depth - pred_depth.min()) / (
+                    pred_depth.max() - pred_depth.min())
+                if 'image_sr' in pred:
+
+                    if pred['image_sr'].shape[-1] == 512:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_512(pred['image_raw']), pred['image_sr'],
+                            self.pool_512(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                    elif pred['image_sr'].shape[-1] == 256:
+
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_256(pred['image_raw']), pred['image_sr'],
+                            self.pool_256(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                    else:
+                        pred_vis = th.cat([
+                            micro['img_sr'],
+                            self.pool_128(pred['image_raw']),
+                            self.pool_128(pred['image_sr']),
+                            self.pool_128(pred_depth).repeat_interleave(3,
+                                                                        dim=1)
+                        ],
+                                          dim=-1)
+
+                else:
+
+                    # st()
+                    pred_vis = th.cat([
+                        self.pool_128(micro['img']),
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    # ! cooncat h dim
+                    pred_vis = pred_vis.permute(0, 2, 3, 1).flatten(0,
+                                                                    1)  # H W 3
+
+                # vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+                # vis = pred_vis.permute(1,2,0).cpu().numpy()
+                vis = pred_vis.cpu().numpy()
+                vis = vis * 127.5 + 127.5
+                vis = vis.clip(0, 255).astype(np.uint8)
+
+                # for j in range(vis.shape[0]):
+                # video_out.append_data(vis[j])
+                video_out.append_data(vis)
+
+            video_out.close()
+
+        th.cuda.empty_cache()
+
+    def _prepare_nvs_pose(self):
+
+        device = dist_util.dev()
+
+        fov_deg = 18.837  # for ffhq/afhq
+        intrinsics = FOV_to_intrinsics(fov_deg, device=device)
+
+        all_nvs_params = []
+
+        pitch_range = 0.25
+        yaw_range = 0.35
+        num_keyframes = 10  # how many nv poses to sample from
+        w_frames = 1
+
+        cam_pivot = th.Tensor(
+            self.rendering_kwargs.get('avg_camera_pivot')).to(device)
+        cam_radius = self.rendering_kwargs.get('avg_camera_radius')
+
+        for frame_idx in range(num_keyframes):
+
+            cam2world_pose = LookAtPoseSampler.sample(
+                3.14 / 2 + yaw_range * np.sin(2 * 3.14 * frame_idx /
+                                              (num_keyframes * w_frames)),
+                3.14 / 2 - 0.05 +
+                pitch_range * np.cos(2 * 3.14 * frame_idx /
+                                     (num_keyframes * w_frames)),
+                cam_pivot,
+                radius=cam_radius,
+                device=device)
+
+            camera_params = th.cat(
+                [cam2world_pose.reshape(-1, 16),
+                 intrinsics.reshape(-1, 9)], 1)
+
+            all_nvs_params.append(camera_params)
+
+        self.all_nvs_params = th.cat(all_nvs_params, 0)
diff --git a/nsr/train_util_DiT.py b/nsr/train_util_DiT.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbf5cf53bf44c1e6c2047c2e22d594e0bf9687a6
--- /dev/null
+++ b/nsr/train_util_DiT.py
@@ -0,0 +1,1040 @@
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+
+# from ..guided_diffusion.train_util import TrainLoop
+
+# use_amp=True
+use_amp = False
+if use_amp:
+    logger.log('DiT using AMP')
+
+from .train_util_diffusion import TrainLoop3DDiffusion
+import dnnlib
+
+
+class TrainLoop3DDiffusionDiT(TrainLoop3DDiffusion):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 schedule_sampler=None,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 ignore_resume_opt=False,
+                 freeze_ae=False,
+                 denoised_ae=True,
+                 triplane_scaling_divider=10,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         **kwargs)
+
+    def eval_ddpm_sample(self):
+
+        args = dnnlib.EasyDict(
+            dict(batch_size=1,
+                 image_size=224,
+                 denoise_in_channels=24,
+                 clip_denoised=True,
+                 class_cond=False,
+                 use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        for i in range(1):
+            triplane_sample = sample_fn(
+                self.ddp_model,
+                (args.batch_size, args.denoise_in_channels, args.image_size,
+                 args.image_size),  # 
+                clip_denoised=args.clip_denoised,
+                model_kwargs=model_kwargs,
+            )  # B 8 H W*3
+
+            # print(triplane_sample.shape)
+
+            # B, C, H, W = triplane_sample.shape
+            # triplane_sample = triplane_sample.reshape(B, C, H, W//3, 3).permute(0,1,4,2,3) # c*3 order
+            # triplane_sample.reshape(B, -1, H, W//3) # B 24 H W
+
+            self.render_video_given_triplane(
+                triplane_sample,
+                name_prefix=f'{self.step + self.resume_step}_{i}')
+
+
+class TrainLoop3DDiffusionDiTOverfit(TrainLoop):
+
+    def __init__(
+            self,
+            *,
+            # model,
+            rec_model,
+            denoise_model,
+            diffusion,
+            loss_class,
+            data,
+            eval_data,
+            batch_size,
+            microbatch,
+            lr,
+            ema_rate,
+            log_interval,
+            eval_interval,
+            save_interval,
+            resume_checkpoint,
+            use_fp16=False,
+            fp16_scale_growth=0.001,
+            schedule_sampler=None,
+            weight_decay=0,
+            lr_anneal_steps=0,
+            iterations=10001,
+            ignore_resume_opt=False,
+            freeze_ae=False,
+            denoised_ae=True,
+            triplane_scaling_divider=10,
+            use_amp=False,
+            **kwargs):
+
+        super().__init__(model=denoise_model,
+                         diffusion=diffusion,
+                         data=data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         lr_anneal_steps=lr_anneal_steps,
+                         weight_decay=weight_decay,
+                         use_amp=use_amp)
+
+        # self.accelerator = Accelerator()
+
+        self.pool_512 = th.nn.AdaptiveAvgPool2d((512, 512))
+        self.pool_128 = th.nn.AdaptiveAvgPool2d((128, 128))
+        self.loss_class = loss_class
+        self.rec_model = rec_model
+        self.eval_interval = eval_interval
+        self.eval_data = eval_data
+        self.iterations = iterations
+        # self.triplane_std = 10
+        self.triplane_scaling_divider = triplane_scaling_divider
+
+        self._load_and_sync_parameters(model=self.rec_model, model_name='rec')
+
+        # * for loading EMA
+        self.mp_trainer_rec = MixedPrecisionTrainer(
+            model=self.rec_model,
+            use_fp16=self.use_fp16,
+            use_amp=use_amp,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='rec',
+        )
+        self.denoised_ae = denoised_ae
+        if not freeze_ae:
+            self.opt_rec = AdamW(self._init_optim_groups(kwargs))
+        else:
+            print('!! freezing AE !!')
+
+        if dist_util.get_rank() == 0:
+            self.writer = SummaryWriter(log_dir=f'{logger.get_dir()}/runs')
+            print(self.opt)
+            if not freeze_ae:
+                print(self.opt_rec)
+
+        # if not freeze_ae:
+        if self.resume_step:
+            if not ignore_resume_opt:
+                self._load_optimizer_state()
+            else:
+                logger.warn("Ignoring optimizer state from checkpoint.")
+            # Model was resumed, either due to a restart or a checkpoint
+            # being specified at the command line.
+            # if not freeze_ae:
+            #     self.ema_params_rec = [
+            #         self._load_ema_parameters(
+            #             rate,
+            #             self.rec_model,
+            #             self.mp_trainer_rec,
+            #             model_name=self.mp_trainer_rec.model_name)
+            #         for rate in self.ema_rate
+            #     ]
+            # else:
+            self.ema_params_rec = [
+                self._load_ema_parameters(
+                    rate,
+                    self.rec_model,
+                    self.mp_trainer_rec,
+                    model_name=self.mp_trainer_rec.model_name)
+                for rate in self.ema_rate
+            ]
+        else:
+            if not freeze_ae:
+                self.ema_params_rec = [
+                    copy.deepcopy(self.mp_trainer_rec.master_params)
+                    for _ in range(len(self.ema_rate))
+                ]
+
+        if self.use_ddp is True:
+            self.rec_model = th.nn.SyncBatchNorm.convert_sync_batchnorm(
+                self.rec_model)
+            self.ddp_rec_model = DDP(
+                self.rec_model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+                # find_unused_parameters=True,
+            )
+        else:
+            self.ddp_rec_model = self.rec_model
+
+        if freeze_ae:
+            self.ddp_rec_model.eval()
+            self.ddp_rec_model.requires_grad_(False)
+        self.freeze_ae = freeze_ae
+
+        # if use_amp:
+
+    def _init_optim_groups(self, kwargs):
+        optim_groups = [
+            # vit encoder
+            {
+                'name': 'vit_encoder',
+                'params': self.mp_trainer_rec.model.encoder.parameters(),
+                'lr': kwargs['encoder_lr'],
+                'weight_decay': kwargs['encoder_weight_decay']
+            },
+            # vit decoder
+            {
+                'name':
+                'vit_decoder',
+                'params':
+                self.mp_trainer_rec.model.decoder.vit_decoder.parameters(),
+                'lr':
+                kwargs['vit_decoder_lr'],
+                'weight_decay':
+                kwargs['vit_decoder_wd']
+            },
+            {
+                'name':
+                'vit_decoder_pred',
+                'params':
+                self.mp_trainer_rec.model.decoder.decoder_pred.parameters(),
+                'lr':
+                kwargs['vit_decoder_lr'],
+                # 'weight_decay': 0
+                'weight_decay':
+                kwargs['vit_decoder_wd']
+            },
+
+            # triplane decoder
+            {
+                'name':
+                'triplane_decoder',
+                'params':
+                self.mp_trainer_rec.model.decoder.triplane_decoder.parameters(
+                ),
+                'lr':
+                kwargs['triplane_decoder_lr'],
+                # 'weight_decay': self.weight_decay
+            },
+        ]
+
+        if self.mp_trainer_rec.model.decoder.superresolution is not None:
+            optim_groups.append({
+                'name':
+                'triplane_decoder_superresolution',
+                'params':
+                self.mp_trainer_rec.model.decoder.superresolution.parameters(),
+                'lr':
+                kwargs['super_resolution_lr'],
+            })
+
+        return optim_groups
+
+    def run_loop(self, batch=None):
+        th.cuda.empty_cache()
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            batch = next(self.data)
+            self.run_step(batch)
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_ddpm_sample()
+                #     continue # TODO, diffusion inference
+                # self.eval_loop()
+                # self.eval_novelview_loop()
+                # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+                th.cuda.empty_cache()
+
+            if self.step % self.save_interval == 0 and self.step != 0:
+                self.save()
+                if not self.freeze_ae:
+                    self.save(self.mp_trainer_rec, 'rec')
+                dist_util.synchronize()
+
+                th.cuda.empty_cache()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+                    self.save()
+                    if not self.freeze_ae:
+                        self.save(self.mp_trainer_rec, 'rec')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            if not self.freeze_ae:
+                self.save(self.mp_trainer_rec, 'rec')
+
+    def run_step(self, batch, cond=None):
+        self.forward_backward(batch,
+                              cond)  # type: ignore # * 3D Reconstruction step
+        took_step_ddpm = self.mp_trainer.optimize(self.opt)
+        if took_step_ddpm:
+            self._update_ema()
+
+        if not self.freeze_ae:
+            took_step_rec = self.mp_trainer_rec.optimize(self.opt_rec)
+            if took_step_rec:
+                self._update_ema_rec()
+
+        self._anneal_lr()
+        self.log_step()
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # return super().forward_backward(batch, *args, **kwargs)
+        self.mp_trainer.zero_grad()
+        # all_denoised_out = dict()
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # if not freeze_ae:
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer_rec.use_amp
+                                      and not self.freeze_ae):
+                # with th.cuda.amp.autocast(dtype=th.float16,
+                #                           enabled=False,): # ! debugging, no AMP on all the input
+
+                # pred = self.ddp_rec_model(img=micro['img_to_encoder'],
+                #                           c=micro['c'])  # pred: (B, 3, 64, 64)
+                # if not self.freeze_ae:
+                #     target = micro
+
+                #     if last_batch or not self.use_ddp:
+                #         ae_loss, loss_dict = self.loss_class(pred,
+                #                                              target,
+                #                                              test_mode=False)
+                #     else:
+                #         with self.ddp_model.no_sync():  # type: ignore
+                #             ae_loss, loss_dict = self.loss_class(
+                #                 pred, target, test_mode=False)
+
+                #     log_rec3d_loss_dict(loss_dict)
+                # else:
+                #     ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                # micro_to_denoise = micro['img']
+                # micro_to_denoise = micro['img'].repeat_interleave(
+                    # 8, dim=1)  # B 3*8 H W
+                micro_to_denoise = micro['img'].repeat_interleave(2, dim=1) # B 3*8 H W
+                # micro_to_denoise = micro['img'].repeat_interleave(1, dim=1) # B 3*8 H W
+
+                # micro_to_denoise = pred[
+                #     'latent'] / self.triplane_scaling_divider  # normalize std to 1
+
+                t, weights = self.schedule_sampler.sample(
+                    micro_to_denoise.shape[0], dist_util.dev())
+
+                # print('!!!', micro_to_denoise.dtype)
+                # =================================== denoised part ===================================
+
+                model_kwargs = {}
+
+                # print(micro_to_denoise.min(), micro_to_denoise.max())
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    micro_to_denoise,  # x_start
+                    t,
+                    model_kwargs=model_kwargs,
+                )
+
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                    # denoised_out = denoised_fn()
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        losses = compute_losses()
+                        # denoised_out = denoised_fn()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                denoise_loss = (losses["loss"] * weights).mean()
+
+                x_t = losses['x_t']
+                losses.pop('x_t')
+
+                log_loss_dict(self.diffusion, t,
+                              {k: v * weights
+                               for k, v in losses.items()})
+
+                loss = denoise_loss  # ! leave only denoise_loss for debugging
+
+            # exit AMP before backward
+            self.mp_trainer.backward(loss)
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    # pred_depth = pred['image_depth']
+                    # pred_depth = (pred_depth - pred_depth.min()) / (
+                    #     pred_depth.max() - pred_depth.min())
+                    # pred_img = pred['image_raw']
+                    # gt_img = micro['img']
+
+                    # if 'image_sr' in pred:  # TODO
+                    #     pred_img = th.cat(
+                    #         [self.pool_512(pred_img), pred['image_sr']],
+                    #         dim=-1)
+                    #     gt_img = th.cat(
+                    #         [self.pool_512(micro['img']), micro['img_sr']],
+                    #         dim=-1)
+                    #     pred_depth = self.pool_512(pred_depth)
+                    #     gt_depth = self.pool_512(gt_depth)
+
+                    # gt_vis = th.cat(
+                    #     [
+                    #         # gt_img,
+                    #         micro['img'],
+                    #         # gt_depth.repeat_interleave(3, dim=1)
+                    #     ],
+                    #     dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    # if not self.denoised_ae:
+                    #     # continue
+
+                    #     denoised_ae_pred = self.ddp_rec_model(
+                    #         img=None,
+                    #         c=micro['c'][0:1],
+                    #         latent=denoised_out['pred_xstart'][0:1] * self.
+                    #         triplane_scaling_divider,  # TODO, how to define the scale automatically
+                    #         behaviour='triplane_dec')
+
+                    # assert denoised_ae_pred is not None
+
+                    # print(pred_img.shape)
+                    # print('denoised_ae:', self.denoised_ae)
+
+                    # pred_vis = th.cat([
+                    #     pred_img[0:1], denoised_ae_pred['image_raw'],
+                    #     pred_depth[0:1].repeat_interleave(3, dim=1)
+                    # ],
+                    #                   dim=-1)  # B, 3, H, W
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # x_t = self.diffusion.q_sample(
+                    #     x_start, t, noise=noise
+                    # )  # * add noise according to predefined schedule
+
+                    denoised_fn = functools.partial(
+                        self.diffusion.p_mean_variance,
+                        self.ddp_model,
+                        x_t,  # x_start
+                        t,
+                        model_kwargs=model_kwargs)
+
+                    denoised_out = denoised_fn()
+
+                    vis = th.cat([
+                        micro['img'], x_t[:, :3, ...],
+                        denoised_out['pred_xstart'][:, :3, ...]
+                    ],
+                                 dim=-1)[0].permute(
+                                     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}.jpg'
+                    )
+
+                    th.cuda.empty_cache()
+
+    @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    def eval_novelview_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        all_loss_dict = []
+        novel_view_micro = {}
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i == 0:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            pred = self.model(img=novel_view_micro['img_to_encoder'],
+                              c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+            # targe
+
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            if 'image_sr' in pred:
+                pred_vis = th.cat([
+                    micro['img_sr'],
+                    self.pool_512(pred['image_raw']), pred['image_sr'],
+                    self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+                  'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+                                   self.step + self.resume_step)
+
+    @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    def eval_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+        all_loss_dict = []
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            # pred = self.model(img=micro['img_to_encoder'],
+            #                   c=micro['c'])  # pred: (B, 3, 64, 64)
+
+            # pred of rec model
+            pred = self.ddp_rec_model(img=micro['img_to_encoder'],
+                                      c=micro['c'])  # pred: (B, 3, 64, 64)
+
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+                pred_vis = th.cat([
+                    micro['img_sr'],
+                    self.pool_512(pred['image_raw']), pred['image_sr'],
+                    self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores.json'), 'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/Rec/{k}', v,
+                                   self.step + self.resume_step)
+
+        self.eval_novelview_loop()
+
+    def save(self, mp_trainer=None, model_name='ddpm'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, self.mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        dist.barrier()
+
+    def _load_and_sync_parameters(self, model=None, model_name='ddpm'):
+        resume_checkpoint, self.resume_step = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.model
+        print(resume_checkpoint)
+
+        if resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+
+                # ! rank 0 return will cause all other ranks to hang
+                # if not Path(resume_checkpoint).exists():
+                #     logger.log(
+                #         f"failed to load model from checkpoint: {resume_checkpoint}, not exist"
+                #     )
+                #     return
+
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                print(f'mark {model_name} loading ', flush=True)
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                print(f'mark {model_name} loading finished', flush=True)
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+
+                    # elif 'IN' in k and model_name == 'rec' and getattr(model.decoder, 'decomposed_IN', False):
+                    #     model_state_dict[k.replace('IN', 'superresolution.norm.norm_layer')] = v # decomposed IN
+
+                    else:
+                        print('!!!! ignore key: ', k, ": ", v.size(),
+                              'shape in model: ', model_state_dict[k].size())
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            print(f'synced {model_name} params')
+
+    def _update_ema_rec(self):
+        for rate, params in zip(self.ema_rate, self.ema_params_rec):
+            update_ema(params, self.mp_trainer_rec.master_params, rate=rate)
+
+    def eval_ddpm_sample(self):
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                image_size=128,
+                #  denoise_in_channels=3,
+                # denoise_in_channels=24,
+                 denoise_in_channels=6,
+                #  denoise_in_channels=6,
+                clip_denoised=True,
+                class_cond=False,
+                use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        for i in range(1):
+            img_sample = sample_fn(
+                self.ddp_model,
+                (args.batch_size, args.denoise_in_channels, args.image_size,
+                 args.image_size),
+                clip_denoised=args.clip_denoised,
+                model_kwargs=model_kwargs,
+            )
+
+            pred_vis = img_sample
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()[0][..., :3]
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            Image.fromarray(vis).save(
+                f'{logger.get_dir()}/{self.step + self.resume_step}_{i}.png')
+
+            # th.cuda.empty_cache()
+            # self.render_video_given_triplane(
+            #     triplane_sample,
+            #     name_prefix=f'{self.step + self.resume_step}_{i}')
+
+            th.cuda.empty_cache()
+
+    @th.inference_mode()
+    def render_video_given_triplane(self, planes, name_prefix='0'):
+
+        planes *= self.triplane_scaling_divider  # if setting clip_denoised=True, the sampled planes will lie in [-1,1]. Thus, values beyond [+- std] will be abandoned in this version. Move to IN for later experiments.
+
+        # print(planes.min(), planes.max())
+
+        # used during diffusion sampling inference
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_{name_prefix}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            pred = self.ddp_rec_model(img=None,
+                                      c=micro['c'],
+                                      latent=planes,
+                                      behaviour='triplane_dec')
+
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+                pred_vis = th.cat([
+                    micro['img_sr'],
+                    self.pool_512(pred['image_raw']), pred['image_sr'],
+                    self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+        print('logged video to: ',
+              f'{logger.get_dir()}/triplane_{name_prefix}.mp4')
+
+    @th.inference_mode()
+    def render_video_noise_schedule(self, name_prefix='0'):
+
+        # planes *= self.triplane_std # denormalize for rendering
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_visnoise_{name_prefix}.mp4',
+            mode='I',
+            fps=30,
+            codec='libx264')
+
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i % 10 != 0:
+                continue
+
+            # ========= novel view plane settings ====
+            if i == 0:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            latent = self.ddp_rec_model(
+                img=novel_view_micro['img_to_encoder'],
+                c=micro['c'])['latent']  # pred: (B, 3, 64, 64)
+
+            x_start = latent / self.triplane_scaling_divider  # normalize std to 1
+            # x_start = latent
+
+            all_pred_vis = []
+            # for t in th.range(0,
+            #                   4001,
+            #                   500,
+            #                   dtype=th.long,
+            #                   device=dist_util.dev()):  # cosine 4k steps
+            for t in th.range(0,
+                              1001,
+                              125,
+                              dtype=th.long,
+                              device=dist_util.dev()):  # cosine 4k steps
+
+                # ========= add noise according to t
+                noise = th.randn_like(x_start)  # x_start is the x0 image
+                x_t = self.diffusion.q_sample(
+                    x_start, t, noise=noise
+                )  # * add noise according to predefined schedule
+                planes_x_t = (x_t * self.triplane_scaling_divider).clamp(
+                    -50, 50)  # de-scaling noised x_t
+
+                # planes_x_t = (x_t * 1).clamp(
+                #     -50, 50)  # de-scaling noised x_t
+
+                # ===== visualize
+                pred = self.ddp_rec_model(
+                    img=None,
+                    c=micro['c'],
+                    latent=planes_x_t,
+                    behaviour='triplane_dec')  # pred: (B, 3, 64, 64)
+
+                # pred_depth = pred['image_depth']
+                # pred_depth = (pred_depth - pred_depth.min()) / (
+                #     pred_depth.max() - pred_depth.min())
+                # pred_vis = th.cat([
+                #     # self.pool_128(micro['img']),
+                #     pred['image_raw'],
+                # ],
+                #                   dim=-1)  # B, 3, H, W
+                pred_vis = pred['image_raw']
+
+                all_pred_vis.append(pred_vis)
+                # TODO, make grid
+
+            all_pred_vis = torchvision.utils.make_grid(
+                th.cat(all_pred_vis, 0),
+                nrow=len(all_pred_vis),
+                normalize=True,
+                value_range=(-1, 1),
+                scale_each=True)  # normalized to [-1,1]
+
+            vis = all_pred_vis.permute(1, 2, 0).cpu().numpy()  # H W 3
+
+            vis = (vis * 255).clip(0, 255).astype(np.uint8)
+
+            video_out.append_data(vis)
+
+        video_out.close()
+        print('logged video to: ',
+              f'{logger.get_dir()}/triplane_visnoise_{name_prefix}.mp4')
+
+        th.cuda.empty_cache()
diff --git a/nsr/train_util_cvD.py b/nsr/train_util_cvD.py
new file mode 100644
index 0000000000000000000000000000000000000000..70a1a89da6576457ada7d85dac7fc25f216e6888
--- /dev/null
+++ b/nsr/train_util_cvD.py
@@ -0,0 +1,637 @@
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+import torchvision
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion import dist_util, logger
+from guided_diffusion.train_util import (calc_average_loss,
+                                         log_rec3d_loss_dict,
+                                         find_resume_checkpoint)
+
+from torch.optim import AdamW
+
+from .train_util import TrainLoopBasic, TrainLoop3DRec
+import vision_aided_loss
+from dnnlib.util import calculate_adaptive_weight
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+class TrainLoop3DcvD(TrainLoop3DRec):
+
+    def __init__(
+            self,
+            *,
+            rec_model,
+            loss_class,
+            # diffusion,
+            data,
+            eval_data,
+            batch_size,
+            microbatch,
+            lr,
+            ema_rate,
+            log_interval,
+            eval_interval,
+            save_interval,
+            resume_checkpoint,
+            use_fp16=False,
+            fp16_scale_growth=1e-3,
+            # schedule_sampler=None,
+            weight_decay=0.0,
+            lr_anneal_steps=0,
+            iterations=10001,
+            load_submodule_name='',
+            ignore_resume_opt=False,
+            use_amp=False,
+            cvD_name='cvD',
+            model_name='rec',
+            # SR_TRAINING=True,
+            SR_TRAINING=False,
+            **kwargs):
+        super().__init__(rec_model=rec_model,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         load_submodule_name=load_submodule_name,
+                         ignore_resume_opt=ignore_resume_opt,
+                         model_name=model_name,
+                         use_amp=use_amp, 
+                         cvD_name=cvD_name,
+                         **kwargs)
+
+        # self.rec_model = self.ddp_model
+
+        # device = loss_class.device
+        device = dist_util.dev()
+        # * create vision aided model
+        # TODO, load model
+        self.nvs_cvD = vision_aided_loss.Discriminator(
+            cv_type='clip', loss_type='multilevel_sigmoid_s',
+            device=device).to(device)
+        self.nvs_cvD.cv_ensemble.requires_grad_(False)  # Freeze feature extractor
+        # self.nvs_cvD.train()
+
+        # 
+        # SR_TRAINING = False
+        cvD_model_params=list(self.nvs_cvD.decoder.parameters())
+        self.SR_TRAINING = SR_TRAINING
+        # SR_TRAINING = True
+        if SR_TRAINING:
+            # width, patch_size = self.nvs_cvD.cv_ensemble
+            vision_width, vision_patch_size = [self.nvs_cvD.cv_ensemble.models[0].model.conv1.weight.shape[k] for k in [0, -1]]
+            self.nvs_cvD.cv_ensemble.models[0].model.conv1 = th.nn.Conv2d(in_channels=6, out_channels=vision_width, kernel_size=vision_patch_size, stride=vision_patch_size, bias=False).to(dist_util.dev())
+            self.nvs_cvD.cv_ensemble.models[0].model.conv1.requires_grad_(True)
+            cvD_model_params += list(self.nvs_cvD.cv_ensemble.models[0].model.conv1.parameters())
+
+            # change normalization metrics
+            self.nvs_cvD.cv_ensemble.models[0].image_mean = self.nvs_cvD.cv_ensemble.models[0].image_mean.repeat(2)
+            self.nvs_cvD.cv_ensemble.models[0].image_std = self.nvs_cvD.cv_ensemble.models[0].image_std.repeat(2)
+
+        # logger.log(f'nvs_cvD_model_params: {cvD_model_params}')
+
+        self._load_and_sync_parameters(model=self.nvs_cvD, model_name='cvD')
+
+        self.mp_trainer_cvD = MixedPrecisionTrainer(
+            model=self.nvs_cvD,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name=cvD_name,
+            use_amp=use_amp,
+            model_params=cvD_model_params
+            )
+
+        # cvD_lr = 4e-5*(lr/1e-5)
+        # cvD_lr = 4e-4*(lr/1e-5)
+        cvD_lr = 1e-4*(lr/1e-5) * self.loss_class.opt.nvs_D_lr_mul
+        # cvD_lr = 1e-5*(lr/1e-5)
+        self.opt_cvD = AdamW(
+            self.mp_trainer_cvD.master_params,
+            lr=cvD_lr,
+            betas=(0, 0.999),
+            eps=1e-8)  # dlr in biggan cfg
+        
+        logger.log(f'cpt_cvD lr: {cvD_lr}')
+
+        if self.use_ddp:
+            self.ddp_nvs_cvD = DDP(
+                self.nvs_cvD,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            self.ddp_nvs_cvD = self.nvs_cvD
+
+        th.cuda.empty_cache()
+
+    def run_step(self, batch, step='g_step'):
+        # self.forward_backward(batch)
+
+        if step == 'g_step_rec':
+            self.forward_G_rec(batch)
+            took_step_g_rec = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_rec:
+                self._update_ema()  # g_ema
+
+        elif step == 'g_step_nvs':
+            self.forward_G_nvs(batch)
+            took_step_g_nvs = self.mp_trainer_rec.optimize(self.opt)
+
+            if took_step_g_nvs:
+                self._update_ema()  # g_ema
+
+        elif step == 'd_step':
+            self.forward_D(batch)
+            _ = self.mp_trainer_cvD.optimize(self.opt_cvD)
+
+        self._anneal_lr()
+        self.log_step()
+
+    def run_loop(self):
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_rec')
+
+            batch = next(self.data)
+            self.run_step(batch, 'g_step_nvs')
+
+            batch = next(self.data)
+            self.run_step(batch, 'd_step')
+
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.eval_interval == 0 and self.step != 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_loop()
+                    # self.eval_novelview_loop()
+                    # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+
+            if self.step % self.save_interval == 0:
+                self.save()
+                self.save(self.mp_trainer_cvD, 'cvD')
+                dist_util.synchronize()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                logger.log('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+                    self.save()
+                    self.save(self.mp_trainer_cvD, 'cvD')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            self.save(self.mp_trainer_cvD, 'cvD')
+
+    # def forward_backward(self, batch, *args, **kwargs):
+    # blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0
+
+    def run_D_Diter(self, real, fake, D=None):
+        # Dmain: Minimize logits for generated images and maximize logits for real images.
+        if D is None:
+            D = self.ddp_nvs_cvD
+
+        lossD = D(real, for_real=True).mean() + D(
+            fake, for_real=False).mean()
+        return lossD
+
+    def forward_D(self, batch):  # update D
+        self.mp_trainer_cvD.zero_grad()
+        self.ddp_nvs_cvD.requires_grad_(True)
+        self.rec_model.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        # * sample a new batch for D training
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_cvD.use_amp):
+
+                # pred = self.rec_model(img=micro['img_to_encoder'],
+                #                       c=micro['c'])  # pred: (B, 3, 64, 64)
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][1:],
+                        micro['c'][:1],  # half novel view, half orig view
+                    ]))
+
+                real_logits_cv = self.run_D_Diter(
+                    real=micro['img_to_encoder'],
+                    fake=pred['image_raw'])  # TODO, add SR for FFHQ
+
+            log_rec3d_loss_dict({'vision_aided_loss/D': real_logits_cv})
+
+            self.mp_trainer_cvD.backward(real_logits_cv)
+
+    def forward_G_rec(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # VQ3D novel view d loss
+            # duplicated_for_nvs = th.cat([
+            #     micro['img_to_encoder'][:batch_size - 2],
+            #     micro['img_to_encoder'][:2]
+            # ], 0)
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    img=micro['img_to_encoder'], c=micro['c']
+                )  # render novel view for first half of the batch for D loss
+
+                target_for_rec = micro
+                pred_for_rec = pred
+
+                # pred_for_rec = {
+                #     k: v[:batch_size - 2] if v is not None else None
+                #     for k, v in pred.items()
+                # }
+                # target_for_rec = {
+                #     k: v[:batch_size - 2] if v is not None else None
+                #     for k, v in target.items()
+                # }
+
+                if last_batch or not self.use_ddp:
+                    loss, loss_dict = self.loss_class(pred_for_rec,
+                                                      target_for_rec,
+                                                      test_mode=False)
+                else:
+                    with self.rec_model.no_sync():  # type: ignore
+                        loss, loss_dict = self.loss_class(pred_for_rec,
+                                                          target_for_rec,
+                                                          test_mode=False)
+
+                # add cvD supervision
+                vision_aided_loss = self.ddp_nvs_cvD(
+                    pred_for_rec['image_raw'],
+                    for_G=True).mean()  # [B, 1] shape
+
+                last_layer = self.rec_model.module.decoder.triplane_decoder.decoder.net[  # type: ignore
+                    -1].weight  # type: ignore
+
+                d_weight = calculate_adaptive_weight(
+                    loss, vision_aided_loss, last_layer,
+                    # disc_weight_max=0.1) * 0.1
+                    # disc_weight_max=0.1) * 0.05
+                    disc_weight_max=1)
+                loss += vision_aided_loss * d_weight
+
+                loss_dict.update({
+                    'vision_aided_loss/G_rec': vision_aided_loss,
+                    'd_weight': d_weight
+                })
+
+                log_rec3d_loss_dict(loss_dict)
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        if pred['image_sr'].shape[-1] == 512:
+                            pred_img = th.cat(
+                                [self.pool_512(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_512(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_512(pred_depth)
+                            gt_depth = self.pool_512(gt_depth)
+
+                        elif pred['image_sr'].shape[-1] == 256:
+                            pred_img = th.cat(
+                                [self.pool_256(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_256(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            pred_depth = self.pool_256(pred_depth)
+                            gt_depth = self.pool_256(gt_depth)
+
+                        else:
+                            pred_img = th.cat(
+                                [self.pool_128(pred_img), pred['image_sr']],
+                                dim=-1)
+                            gt_img = th.cat(
+                                [self.pool_128(micro['img']), micro['img_sr']],
+                                dim=-1)
+                            gt_depth = self.pool_128(gt_depth)
+                            pred_depth = self.pool_128(pred_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg'
+                    )
+
+    def forward_G_nvs(self, batch):  # update G
+
+        self.mp_trainer_rec.zero_grad()
+        self.rec_model.requires_grad_(True)
+        self.ddp_nvs_cvD.requires_grad_(False)
+
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous()
+                for k, v in batch.items()
+            }
+
+            # last_batch = (i + self.microbatch) >= batch_size
+
+            # VQ3D novel view d loss
+            # duplicated_for_nvs = th.cat([
+            #     micro['img_to_encoder'][batch_size // 2:],
+            #     micro['img_to_encoder'][:batch_size // 2]
+            # ], 0)
+
+            with th.autocast(device_type='cuda',
+                             dtype=th.float16,
+                             enabled=self.mp_trainer_rec.use_amp):
+
+                pred = self.rec_model(
+                    # img=duplicated_for_nvs, c=micro['c']
+                    img=micro['img_to_encoder'],
+                    c=th.cat([
+                        micro['c'][1:],
+                        micro['c'][:1],
+                    ])
+                )  # render novel view for first half of the batch for D loss
+
+                # add cvD supervision
+                vision_aided_loss = self.ddp_nvs_cvD(
+                    pred['image_raw'], for_G=True).mean()  # [B, 1] shape
+
+                # loss = vision_aided_loss * 0.01
+                # loss = vision_aided_loss * 0.005
+                # loss = vision_aided_loss * 0.1
+                loss = vision_aided_loss * 0.01
+
+                log_rec3d_loss_dict({
+                    'vision_aided_loss/G_nvs':
+                    vision_aided_loss,
+                })
+
+            self.mp_trainer_rec.backward(loss)
+
+            # ! move to other places, add tensorboard
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:
+                        pred_img = th.cat(
+                            [self.pool_512(pred_img), pred['image_sr']],
+                            dim=-1)
+                        gt_img = th.cat(
+                            [self.pool_512(micro['img']), micro['img_sr']],
+                            dim=-1)
+                        pred_depth = self.pool_512(pred_depth)
+                        gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [gt_img, gt_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # TODO, fail to load depth. range [0, 1]
+
+                    pred_vis = th.cat(
+                        [pred_img,
+                         pred_depth.repeat_interleave(3, dim=1)],
+                        dim=-1)  # B, 3, H, W
+
+                    # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                    #     1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)
+
+                    vis = torchvision.utils.make_grid(
+                        vis,
+                        normalize=True,
+                        scale_each=True,
+                        value_range=(-1, 1)).cpu().permute(1, 2, 0)  # H W 3
+                    vis = vis.numpy() * 255
+                    vis = vis.clip(0, 255).astype(np.uint8)
+
+                    # logger.log(vis.shape)
+
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+                    logger.log(
+                        'log vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg'
+                    )
+
+    def save(self, mp_trainer=None, model_name='rec'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer_rec
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, mp_trainer.master_params)
+
+        if model_name == 'ddpm':
+            for rate, params in zip(self.ema_rate, self.ema_params):
+                save_checkpoint(rate, params)
+
+        dist.barrier()
+
+    def _load_and_sync_parameters(self, model=None, model_name='rec'):
+        resume_checkpoint, self.resume_step = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.rec_model  # default model in the parent class
+
+        logger.log(resume_checkpoint)
+
+        if resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                logger.log(f'mark {model_name} loading ', )
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                logger.log(f'mark {model_name} loading finished', )
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+
+                    # elif 'IN' in k and model_name == 'rec' and getattr(model.decoder, 'decomposed_IN', False):
+                    #     model_state_dict[k.replace('IN', 'superresolution.norm.norm_layer')] = v # decomposed IN
+                    elif 'attn.wk' in k or 'attn.wv' in k: # old qkv
+                        logger.log('ignore ', k)
+
+                    elif 'decoder.vit_decoder.blocks' in k:
+                        # st()
+                        # load from 2D ViT pre-trained into 3D ViT blocks. 
+                        assert len(model.decoder.vit_decoder.blocks[0].vit_blks) == 2 # assert depth=2 here.
+                        fusion_ca_depth = len(model.decoder.vit_decoder.blocks[0].vit_blks)
+                        vit_subblk_index = int(k.split('.')[3])
+                        vit_blk_keyname = ('.').join(k.split('.')[4:])
+                        fusion_blk_index = vit_subblk_index // fusion_ca_depth
+                        fusion_blk_subindex = vit_subblk_index % fusion_ca_depth
+                        model_state_dict[f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'] = v
+                        # logger.log('load 2D ViT weight: {}'.format(f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'))
+
+                    elif 'IN' in k:
+                        logger.log('ignore ', k)
+
+                    elif 'quant_conv' in k:
+                        logger.log('ignore ', k)
+
+                    else:
+                        logger.log('!!!! ignore key: ', k, ": ", v.size(),)
+                        if k in model_state_dict:
+                            logger.log('shape in model: ', model_state_dict[k].size())
+                        else:
+                            logger.log(k, 'not in model_state_dict')
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            logger.log(f'synced {model_name} params')
diff --git a/nsr/train_util_diffusion.py b/nsr/train_util_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..85ddb437dd05eb8e9c4831b9d34be9121584dcd0
--- /dev/null
+++ b/nsr/train_util_diffusion.py
@@ -0,0 +1,1349 @@
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+# from PIL import Image
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion.gaussian_diffusion import _extract_into_tensor
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+
+import dnnlib
+from huggingface_hub import hf_hub_download
+
+# AMP
+# from accelerate import Accelerator
+
+# from ..guided_diffusion.train_util import TrainLoop
+
+# use_amp = False
+# use_amp = True
+
+
+class TrainLoopDiffusionWithRec(TrainLoop):
+    """an interface with rec_model required apis
+    """
+
+    def __init__(
+        self,
+        *,
+        model,
+        diffusion,
+        loss_class,
+        data,
+        eval_data,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        eval_interval,
+        save_interval,
+        resume_checkpoint,
+        use_fp16=False,
+        fp16_scale_growth=0.001,
+        weight_decay=0,
+        lr_anneal_steps=0,
+        iterations=10001,
+        triplane_scaling_divider=1,
+        use_amp=False,
+        diffusion_input_size=224,
+        schedule_sampler=None,
+        model_name='ddpm',
+        train_vae=True,
+        **kwargs,
+    ):
+        super().__init__(
+            model=model,
+            diffusion=diffusion,
+            data=data,
+            batch_size=batch_size,
+            microbatch=microbatch,
+            lr=lr,
+            ema_rate=ema_rate,
+            log_interval=log_interval,
+            save_interval=save_interval,
+            resume_checkpoint=resume_checkpoint,
+            use_fp16=use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            schedule_sampler=schedule_sampler,
+            weight_decay=weight_decay,
+            lr_anneal_steps=lr_anneal_steps,
+            use_amp=use_amp,
+            model_name=model_name,
+            train_vae=train_vae,
+            **kwargs,
+        )
+
+        self.latent_name = 'latent_normalized'  # normalized triplane latent
+        self.diffusion_input_size = diffusion_input_size
+        self.render_latent_behaviour = 'triplane_dec'  # directly render using triplane operations
+
+        self.loss_class = loss_class
+        # self.rec_model = rec_model
+        self.eval_interval = eval_interval
+        self.eval_data = eval_data
+        self.iterations = iterations
+        # self.triplane_std = 10
+        self.triplane_scaling_divider = triplane_scaling_divider
+
+        if dist_util.get_rank() == 0:
+            self.writer = SummaryWriter(log_dir=f'{logger.get_dir()}/runs')
+
+    # def _init_optim_groups(self, rec_model):
+    #     """for initializing the reconstruction model.
+    #     """
+    #     kwargs = self.kwargs
+    #     optim_groups = [
+    #         # vit encoder
+    #         {
+    #             'name': 'vit_encoder',
+    #             'params': rec_model.encoder.parameters(),
+    #             'lr': kwargs['encoder_lr'],
+    #             'weight_decay': kwargs['encoder_weight_decay']
+    #         },
+    #         # vit decoder
+    #         {
+    #             'name': 'vit_decoder',
+    #             'params': rec_model.decoder.vit_decoder.parameters(),
+    #             'lr': kwargs['vit_decoder_lr'],
+    #             'weight_decay': kwargs['vit_decoder_wd']
+    #         },
+    #         {
+    #             'name': 'vit_decoder_pred',
+    #             'params': rec_model.decoder.decoder_pred.parameters(),
+    #             'lr': kwargs['vit_decoder_lr'],
+    #             # 'weight_decay': 0
+    #             'weight_decay': kwargs['vit_decoder_wd']
+    #         },
+
+    #         # triplane decoder
+    #         {
+    #             'name': 'triplane_decoder',
+    #             'params': rec_model.decoder.triplane_decoder.parameters(),
+    #             'lr': kwargs['triplane_decoder_lr'],
+    #             # 'weight_decay': self.weight_decay
+    #         },
+    #     ]
+
+    #     if rec_model.decoder.superresolution is not None:
+    #         optim_groups.append({
+    #             'name':
+    #             'triplane_decoder_superresolution',
+    #             'params':
+    #             rec_model.decoder.superresolution.parameters(),
+    #             'lr':
+    #             kwargs['super_resolution_lr'],
+    #         })
+
+    #     return optim_groups
+
+    def _init_optim_groups(self, rec_model, freeze_decoder=False):
+        """for initializing the reconstruction model; fixing decoder part.
+        """
+        kwargs = self.kwargs
+        optim_groups = [
+            # vit encoder
+            {
+                'name': 'vit_encoder',
+                'params': rec_model.encoder.parameters(),
+                'lr': kwargs['encoder_lr'],
+                'weight_decay': kwargs['encoder_weight_decay']
+            },
+        ]
+
+        if not freeze_decoder:
+            optim_groups += [
+                # vit decoder
+                {
+                    'name': 'vit_decoder',
+                    'params': rec_model.decoder.vit_decoder.parameters(),
+                    'lr': kwargs['vit_decoder_lr'],
+                    'weight_decay': kwargs['vit_decoder_wd']
+                },
+                {
+                    'name': 'vit_decoder_pred',
+                    'params': rec_model.decoder.decoder_pred.parameters(),
+                    'lr': kwargs['vit_decoder_lr'],
+                    # 'weight_decay': 0
+                    'weight_decay': kwargs['vit_decoder_wd']
+                },
+
+                # triplane decoder
+                {
+                    'name': 'triplane_decoder',
+                    'params': rec_model.decoder.triplane_decoder.parameters(),
+                    'lr': kwargs['triplane_decoder_lr'],
+                    # 'weight_decay': self.weight_decay
+                },
+            ]
+
+            if rec_model.decoder.superresolution is not None:
+                optim_groups.append({
+                    'name':
+                    'triplane_decoder_superresolution',
+                    'params':
+                    rec_model.decoder.superresolution.parameters(),
+                    'lr':
+                    kwargs['super_resolution_lr'],
+                })
+
+        return optim_groups
+
+    # @th.no_grad()
+    # # def eval_loop(self, c_list:list):
+    # def eval_novelview_loop(self, rec_model):
+    #     # novel view synthesis given evaluation camera trajectory
+    #     video_out = imageio.get_writer(
+    #         f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4',
+    #         mode='I',
+    #         fps=60,
+    #         codec='libx264')
+
+    #     all_loss_dict = []
+    #     novel_view_micro = {}
+
+    #     # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+    #     for i, batch in enumerate(tqdm(self.eval_data)):
+    #         # for i in range(0, 8, self.microbatch):
+    #         # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+    #         micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+    #         # st()
+
+    #         if i == 0:
+    #             novel_view_micro = {
+    #                 'img_to_encoder': micro['img_to_encoder'][0:1]
+    #             }
+
+    #             latent = rec_model(img=novel_view_micro['img_to_encoder'],
+    #                                     behaviour='enc_dec_wo_triplane')
+
+    #         # else:
+    #         #     # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+    #         #     novel_view_micro = {
+    #         #         k:
+    #         #         v[0:1].to(dist_util.dev()).repeat_interleave(
+    #         #             micro['img'].shape[0], 0)
+    #         #         for k, v in novel_view_micro.items()
+    #         #     }
+
+    #         # pred = rec_model(img=novel_view_micro['img_to_encoder'].repeat_interleave(micro['img'].shape[0], 0),
+    #         #                  c=micro['c'])  # pred: (B, 3, 64, 64)
+
+    #         # ! only render
+    #         pred = rec_model(
+    #             latent={
+    #                 'latent_after_vit': latent['latent_after_vit'].repeat_interleave(micro['img'].shape[0], 0)
+    #             },
+    #             c=micro['c'],  # predict novel view here
+    #             behaviour='triplane_dec',
+    #         )
+
+    #         # target = {
+    #         #     'img': micro['img'],
+    #         #     'depth': micro['depth'],
+    #         #     'depth_mask': micro['depth_mask']
+    #         # }
+    #         # targe
+
+    #         _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+    #         all_loss_dict.append(loss_dict)
+
+    #         # ! move to other places, add tensorboard
+
+    #         # pred_vis = th.cat([
+    #         #     pred['image_raw'],
+    #         #     -pred['image_depth'].repeat_interleave(3, dim=1)
+    #         # ],
+    #         #                   dim=-1)
+
+    #         # normalize depth
+    #         # if True:
+    #         pred_depth = pred['image_depth']
+    #         pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+    #                                                         pred_depth.min())
+    #         if 'image_sr' in pred:
+    #             if pred['image_sr'].shape[-1] == 512:
+    #                 pred_vis = th.cat([
+    #                     micro['img_sr'],
+    #                     self.pool_512(pred['image_raw']), pred['image_sr'],
+    #                     self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+    #                 ],
+    #                                   dim=-1)
+    #             else:
+    #                 assert pred['image_sr'].shape[-1] == 128
+    #                 pred_vis = th.cat([
+    #                     micro['img_sr'],
+    #                     self.pool_128(pred['image_raw']), pred['image_sr'],
+    #                     self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+    #                 ],
+    #                                   dim=-1)
+    #         else:
+    #             pred_vis = th.cat([
+    #                 self.pool_128(micro['img']),
+    #                 self.pool_128(pred['image_raw']),
+    #                 self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+    #             ],
+    #                               dim=-1)  # B, 3, H, W
+
+    #         vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+    #         vis = vis * 127.5 + 127.5
+    #         vis = vis.clip(0, 255).astype(np.uint8)
+
+    #         for j in range(vis.shape[0]):
+    #             video_out.append_data(vis[j])
+
+    #     video_out.close()
+
+    #     del video_out, vis, pred_vis, pred
+    #     th.cuda.empty_cache()
+
+    #     val_scores_for_logging = calc_average_loss(all_loss_dict)
+    #     with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+    #               'a') as f:
+    #         json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+    #     # * log to tensorboard
+    #     for k, v in val_scores_for_logging.items():
+    #         self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+    #                                self.step + self.resume_step)
+
+    @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    def eval_novelview_loop(self, rec_model):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        all_loss_dict = []
+        novel_view_micro = {}
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i == 0:
+                novel_view_micro = {
+                    k:
+                    v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k:
+                    v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            pred = rec_model(img=novel_view_micro['img_to_encoder'],
+                             c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+            # targe
+
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            if 'image_sr' in pred:
+
+                if pred['image_sr'].shape[-1] == 512:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), pred['image_sr'],
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                elif pred['image_sr'].shape[-1] == 256:
+
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_256(pred['image_raw']), pred['image_sr'],
+                        self.pool_256(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+                else:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']),
+                        self.pool_128(pred['image_sr']),
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+
+            else:
+                # pred_vis = th.cat([
+                #     self.pool_64(micro['img']), pred['image_raw'],
+                #     pred_depth.repeat_interleave(3, dim=1)
+                # ],
+                #                   dim=-1)  # B, 3, H, W
+
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']),
+                    self.pool_128(pred['image_raw']),
+                    self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+                  'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+                                   self.step + self.resume_step)
+        del video_out
+        # del pred_vis
+        # del pred
+
+        th.cuda.empty_cache()
+
+    @th.no_grad()
+    def eval_loop(self, rec_model):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+        all_loss_dict = []
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            # pred = self.model(img=micro['img_to_encoder'],
+            #                   c=micro['c'])  # pred: (B, 3, 64, 64)
+
+            # pred of rec model
+            pred = rec_model(img=micro['img_to_encoder'],
+                             c=micro['c'])  # pred: (B, 3, 64, 64)
+
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+                if pred['image_sr'].shape[-1] == 512:
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_512(pred['image_raw']), pred['image_sr'],
+                        self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+                else:
+                    assert pred['image_sr'].shape[-1] == 128
+                    pred_vis = th.cat([
+                        micro['img_sr'],
+                        self.pool_128(pred['image_raw']), pred['image_sr'],
+                        self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']),
+                    self.pool_128(pred['image_raw']),
+                    self.pool_128(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores.json'), 'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/Rec/{k}', v,
+                                   self.step + self.resume_step)
+
+        del video_out, vis, pred_vis, pred
+        th.cuda.empty_cache()
+        self.eval_novelview_loop(rec_model)
+
+    def save(self, mp_trainer=None, model_name='ddpm'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        # save_checkpoint(0, self.mp_trainer_ddpm.master_params)
+        try:
+            save_checkpoint(0, mp_trainer.master_params)
+            if model_name == 'ddpm':
+                for rate, params in zip(self.ema_rate, self.ema_params):
+                    save_checkpoint(rate, params)
+        except Exception as e:
+            logger.log(e)
+
+        th.cuda.empty_cache()
+        dist_util.synchronize()
+
+    def _load_and_sync_parameters(self,
+                                  model=None,
+                                  model_name='ddpm',
+                                  resume_checkpoint=None):
+        hf_loading = 'yslan/GaussianAnything' in self.resume_checkpoint
+
+        if not hf_loading and resume_checkpoint is None:
+            resume_checkpoint, self.resume_step = find_resume_checkpoint(
+                self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.model
+
+        if hf_loading or resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+                # ! rank 0 return will cause all other ranks to hang
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                if hf_loading:
+                    logger.log(f'mark {model_name} loading from hugging face')
+                else:
+                    logger.log(f'mark {model_name} loading ')
+
+                if hf_loading:
+                    hf_ckpt = self.resume_checkpoint.split('/')
+                    repo_id = '/'.join(hf_ckpt[:2])
+                    file_name = '/'.join(hf_ckpt[2:])
+                    model_path = hf_hub_download(repo_id=repo_id, 
+                            filename=file_name)
+
+                    resume_state_dict = dist_util.load_state_dict(
+                        model_path, map_location=map_location)
+
+                else:
+                    resume_state_dict = dist_util.load_state_dict(
+                        resume_checkpoint, map_location=map_location)
+
+                # logger.log(f'mark {model_name} loading finished')
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+
+                    else:
+                        print(
+                            '!!!! ignore key: ',
+                            k,
+                            ": ",
+                            v.size(),
+                        )
+                        if k in model_state_dict:
+                            print('shape in model: ',
+                                  model_state_dict[k].size())
+                        else:
+                            print(k, ' not in model')
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+        else:
+            logger.log(f'{resume_checkpoint} not found.')
+        # print(resume_checkpoint)
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            # dist_util.sync_params(model.named_parameters())
+            print(f'synced {model_name} params')
+
+    @th.inference_mode()
+    def apply_model_inference(self,
+                              x_noisy,
+                              t,
+                              c=None,
+                              model_kwargs={}):  # compatiable api
+        # pred_params = self.ddp_model(x_noisy, t, c=c, model_kwargs=model_kwargs)
+        pred_params = self.ddp_model(x_noisy, t,
+                                     **model_kwargs)  # unconditional model
+        return pred_params
+
+    @th.inference_mode()
+    def eval_ddpm_sample(self, rec_model, **kwargs):  # , ddpm_model=None):
+        # rec_model.eval()
+        # self.ddpm_model.eval()
+        self.model.eval()
+
+        # if ddpm_model is None:
+        #     ddpm_model = self.ddp_model
+
+        args = dnnlib.EasyDict(
+            dict(
+                batch_size=1,
+                # image_size=224,
+                image_size=self.diffusion_input_size,
+                # ddpm_image_size=224,
+                # denoise_in_channels=self.ddp_rec_model.module.decoder.triplane_decoder.out_chans,  # type: ignore
+                denoise_in_channels=self.ddpm_model.
+                in_channels,  # type: ignore
+                clip_denoised=False,
+                class_cond=False,
+                use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        # for i in range(2):
+        for i in range(1):
+            triplane_sample = sample_fn(
+                # self.ddp_model,
+                self,
+                (args.batch_size, args.denoise_in_channels,
+                 self.diffusion_input_size, self.diffusion_input_size),
+                clip_denoised=args.clip_denoised,
+                # model_kwargs=model_kwargs,
+                mixing_normal=True,  # !
+                device=dist_util.dev(),
+                # model_kwargs=model_kwargs,
+                **model_kwargs)
+            th.cuda.empty_cache()
+            self.render_video_given_triplane(
+                triplane_sample,
+                rec_model,
+                name_prefix=f'{self.step + self.resume_step}_{i}')
+            th.cuda.empty_cache()
+
+        # rec_model.train()
+        # self.ddpm_model.train()
+        # ddpm_model.train()
+        self.model.train()
+
+
+    @th.inference_mode()
+    def render_video_noise_schedule(self, name_prefix='0'):
+
+        # planes *= self.triplane_std # denormalize for rendering
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_visnoise_{name_prefix}.mp4',
+            mode='I',
+            fps=30,
+            codec='libx264')
+
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i % 10 != 0:
+                continue
+
+            # ========= novel view plane settings ====
+            if i == 0:
+                novel_view_micro = {
+                    k:
+                    v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k:
+                    v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            latent = self.ddp_rec_model(
+                img=novel_view_micro['img_to_encoder'],
+                c=micro['c'])[self.latent_name]  # pred: (B, 3, 64, 64)
+
+            x_start = latent / self.triplane_scaling_divider  # normalize std to 1
+            # x_start = latent
+
+            all_pred_vis = []
+            # for t in th.range(0,
+            #                   4001,
+            #                   500,
+            #                   dtype=th.long,
+            #                   device=dist_util.dev()):  # cosine 4k steps
+            for t in th.range(0,
+                              1001,
+                              125,
+                              dtype=th.long,
+                              device=dist_util.dev()):  # cosine 4k steps
+
+                # ========= add noise according to t
+                noise = th.randn_like(x_start)  # x_start is the x0 image
+                x_t = self.diffusion.q_sample(
+                    x_start, t, noise=noise
+                )  # * add noise according to predefined schedule
+                planes_x_t = (x_t * self.triplane_scaling_divider).clamp(
+                    -50, 50)  # de-scaling noised x_t
+
+                # planes_x_t = (x_t * 1).clamp(
+                #     -50, 50)  # de-scaling noised x_t
+
+                # ===== visualize
+                pred = self.ddp_rec_model(
+                    img=None,
+                    c=micro['c'],
+                    latent=planes_x_t,
+                    behaviour=self.render_latent_behaviour
+                )  # pred: (B, 3, 64, 64)
+
+                # pred_depth = pred['image_depth']
+                # pred_depth = (pred_depth - pred_depth.min()) / (
+                #     pred_depth.max() - pred_depth.min())
+                # pred_vis = th.cat([
+                #     # self.pool_128(micro['img']),
+                #     pred['image_raw'],
+                # ],
+                #                   dim=-1)  # B, 3, H, W
+                pred_vis = pred['image_raw']
+
+                all_pred_vis.append(pred_vis)
+                # TODO, make grid
+
+            all_pred_vis = torchvision.utils.make_grid(
+                th.cat(all_pred_vis, 0),
+                nrow=len(all_pred_vis),
+                normalize=True,
+                value_range=(-1, 1),
+                scale_each=True)  # normalized to [-1,1]
+
+            vis = all_pred_vis.permute(1, 2, 0).cpu().numpy()  # H W 3
+
+            vis = (vis * 255).clip(0, 255).astype(np.uint8)
+
+            video_out.append_data(vis)
+
+        video_out.close()
+        print('logged video to: ',
+              f'{logger.get_dir()}/triplane_visnoise_{name_prefix}.mp4')
+
+        th.cuda.empty_cache()
+
+    @th.inference_mode()
+    def plot_noise_nsr_curve(self, name_prefix='0'):
+        # planes *= self.triplane_std # denormalize for rendering
+
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i % 10 != 0:
+                continue
+
+            # if i == 0:
+            latent = self.ddp_rec_model(
+                img=micro['img_to_encoder'],
+                c=micro['c'],
+                behaviour='enc_dec_wo_triplane')  # pred: (B, 3, 64, 64)
+
+            x_start = latent[
+                self.
+                latent_name] / self.triplane_scaling_divider  # normalize std to 1
+
+            snr_list = []
+            snr_wo_data_list = []
+            xt_mean = []
+            xt_std = []
+
+            for t in th.range(0,
+                              1001,
+                              5,
+                              dtype=th.long,
+                              device=dist_util.dev()):  # cosine 4k steps
+
+                # ========= add noise according to t
+                noise = th.randn_like(x_start)  # x_start is the x0 image
+
+                beta_t = _extract_into_tensor(
+                    self.diffusion.sqrt_alphas_cumprod, t, x_start.shape)
+                one_minus_beta_t = _extract_into_tensor(
+                    self.diffusion.sqrt_one_minus_alphas_cumprod, t,
+                    x_start.shape)
+
+                signal_t = beta_t * x_start
+                noise_t = one_minus_beta_t * noise
+
+                x_t = signal_t + noise_t
+
+                snr = signal_t / (noise_t + 1e-6)
+                snr_wo_data = beta_t / (one_minus_beta_t + 1e-6)
+
+                snr_list.append(abs(snr).mean().cpu().numpy())
+                snr_wo_data_list.append(abs(snr_wo_data).mean().cpu().numpy())
+                xt_mean.append(x_t.mean().cpu().numpy())
+                xt_std.append(x_t.std().cpu().numpy())
+
+            print('xt_mean', xt_mean)
+            print('xt_std', xt_std)
+            print('snr', snr_list)
+
+            th.save(
+                {
+                    'xt_mean': xt_mean,
+                    'xt_std': xt_std,
+                    'snr': snr_list,
+                    'snr_wo_data': snr_wo_data_list,
+                },
+                Path(logger.get_dir()) / f'snr_{i}.pt')
+
+        th.cuda.empty_cache()
+
+
+# a legacy class for direct diffusion training, not joint.
+class TrainLoop3DDiffusion(TrainLoopDiffusionWithRec):
+
+    def __init__(
+            self,
+            *,
+            # model,
+            rec_model,
+            denoise_model,
+            diffusion,
+            loss_class,
+            data,
+            eval_data,
+            batch_size,
+            microbatch,
+            lr,
+            ema_rate,
+            log_interval,
+            eval_interval,
+            save_interval,
+            resume_checkpoint,
+            use_fp16=False,
+            fp16_scale_growth=0.001,
+            schedule_sampler=None,
+            weight_decay=0,
+            lr_anneal_steps=0,
+            iterations=10001,
+            ignore_resume_opt=False,
+            freeze_ae=False,
+            denoised_ae=True,
+            triplane_scaling_divider=10,
+            use_amp=False,
+            diffusion_input_size=224,
+            **kwargs):
+
+        super().__init__(
+            model=denoise_model,
+            diffusion=diffusion,
+            loss_class=loss_class,
+            data=data,
+            eval_data=eval_data,
+            batch_size=batch_size,
+            microbatch=microbatch,
+            lr=lr,
+            ema_rate=ema_rate,
+            log_interval=log_interval,
+            eval_interval=eval_interval,
+            save_interval=save_interval,
+            resume_checkpoint=resume_checkpoint,
+            use_fp16=use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+            weight_decay=weight_decay,
+            lr_anneal_steps=lr_anneal_steps,
+            iterations=iterations,
+            triplane_scaling_divider=triplane_scaling_divider,
+            use_amp=use_amp,
+            diffusion_input_size=diffusion_input_size,
+            schedule_sampler=schedule_sampler,
+        )
+
+        # self.accelerator = Accelerator()
+
+        self._load_and_sync_parameters(model=self.rec_model, model_name='rec')
+
+        # * for loading EMA
+        self.mp_trainer_rec = MixedPrecisionTrainer(
+            model=self.rec_model,
+            use_fp16=self.use_fp16,
+            use_amp=use_amp,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='rec',
+        )
+        self.denoised_ae = denoised_ae
+
+        if not freeze_ae:
+            self.opt_rec = AdamW(
+                self._init_optim_groups(self.mp_trainer_rec.model))
+        else:
+            print('!! freezing AE !!')
+
+        # if not freeze_ae:
+        if self.resume_step:
+            if not ignore_resume_opt:
+                self._load_optimizer_state()
+            else:
+                logger.warn("Ignoring optimizer state from checkpoint.")
+
+            self.ema_params_rec = [
+                self._load_ema_parameters(
+                    rate,
+                    self.rec_model,
+                    self.mp_trainer_rec,
+                    model_name=self.mp_trainer_rec.model_name)
+                for rate in self.ema_rate
+            ]  # for sync reconstruction model
+        else:
+            if not freeze_ae:
+                self.ema_params_rec = [
+                    copy.deepcopy(self.mp_trainer_rec.master_params)
+                    for _ in range(len(self.ema_rate))
+                ]
+
+        if self.use_ddp is True:
+            self.rec_model = th.nn.SyncBatchNorm.convert_sync_batchnorm(
+                self.rec_model)
+            self.ddp_rec_model = DDP(
+                self.rec_model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+                # find_unused_parameters=True,
+            )
+        else:
+            self.ddp_rec_model = self.rec_model
+
+        if freeze_ae:
+            self.ddp_rec_model.eval()
+            self.ddp_rec_model.requires_grad_(False)
+        self.freeze_ae = freeze_ae
+
+        # if use_amp:
+
+    def _update_ema_rec(self):
+        for rate, params in zip(self.ema_rate, self.ema_params_rec):
+            update_ema(params, self.mp_trainer_rec.master_params, rate=rate)
+
+    def run_loop(self, batch=None):
+        th.cuda.empty_cache()
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_ddpm_sample(self.ddp_rec_model)
+                #     continue # TODO, diffusion inference
+                # self.eval_loop()
+                # self.eval_novelview_loop()
+                # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+                th.cuda.empty_cache()
+
+            batch = next(self.data)
+            self.run_step(batch)
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            if self.step % self.save_interval == 0 and self.step != 0:
+                self.save()
+                if not self.freeze_ae:
+                    self.save(self.mp_trainer_rec, 'rec')
+                dist_util.synchronize()
+
+                th.cuda.empty_cache()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+                    self.save()
+                    if not self.freeze_ae:
+                        self.save(self.mp_trainer_rec, 'rec')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            if not self.freeze_ae:
+                self.save(self.mp_trainer_rec, 'rec')
+
+    def run_step(self, batch, cond=None):
+        self.forward_backward(batch,
+                              cond)  # type: ignore # * 3D Reconstruction step
+        took_step_ddpm = self.mp_trainer.optimize(self.opt)
+        if took_step_ddpm:
+            self._update_ema()
+
+        if not self.freeze_ae:
+            took_step_rec = self.mp_trainer_rec.optimize(self.opt_rec)
+            if took_step_rec:
+                self._update_ema_rec()
+
+        self._anneal_lr()
+        self.log_step()
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # return super().forward_backward(batch, *args, **kwargs)
+        self.mp_trainer.zero_grad()
+        # all_denoised_out = dict()
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # if not freeze_ae:
+
+            # =================================== ae part ===================================
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer_rec.use_amp
+                                      and not self.freeze_ae):
+                # with th.cuda.amp.autocast(dtype=th.float16,
+                #                           enabled=False,): # ! debugging, no AMP on all the input
+
+                latent = self.ddp_rec_model(
+                    img=micro['img_to_encoder'],
+                    c=micro['c'],
+                    behaviour='enc_dec_wo_triplane')  # pred: (B, 3, 64, 64)
+
+                if not self.freeze_ae:
+                    target = micro
+                    pred = self.rec_model(latent=latent,
+                                          c=micro['c'],
+                                          behaviour='triplane_dec')
+
+                    if last_batch or not self.use_ddp:
+                        ae_loss, loss_dict = self.loss_class(pred,
+                                                             target,
+                                                             test_mode=False)
+                    else:
+                        with self.ddp_model.no_sync():  # type: ignore
+                            ae_loss, loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+
+                    log_rec3d_loss_dict(loss_dict)
+                else:
+                    ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                # =================================== prepare for ddpm part ===================================
+
+                micro_to_denoise = latent[
+                    self.
+                    latent_name] / self.triplane_scaling_divider  # normalize std to 1
+
+                t, weights = self.schedule_sampler.sample(
+                    micro_to_denoise.shape[0], dist_util.dev())
+
+                model_kwargs = {}
+
+                # print(micro_to_denoise.min(), micro_to_denoise.max())
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    micro_to_denoise,  # x_start
+                    t,
+                    model_kwargs=model_kwargs,
+                )
+
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                    # denoised_out = denoised_fn()
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        losses = compute_losses()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                denoise_loss = (losses["loss"] * weights).mean()
+
+                x_t = losses['x_t']
+                model_output = losses['model_output']
+                losses.pop('x_t')
+                losses.pop('model_output')
+
+                log_loss_dict(self.diffusion, t, {
+                    k: v * weights
+                    for k, v in losses.items()
+                })
+
+                # self.mp_trainer.backward(denoise_loss)
+                # =================================== denosied ae part ===================================
+                # if self.denoised_ae or self.step % 500 == 0:
+                if self.denoised_ae:
+                    with th.cuda.amp.autocast(
+                            dtype=th.float16,
+                            enabled=self.mp_trainer_rec.use_amp
+                            and not self.freeze_ae):
+                        # continue
+                        denoised_out = denoised_fn()
+
+                        denoised_ae_pred = self.ddp_rec_model(
+                            img=None,
+                            c=micro['c'],
+                            latent=denoised_out['pred_xstart'] * self.
+                            triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                            behaviour=self.render_latent_behaviour)
+
+                        # if self.denoised_ae:
+
+                        if last_batch or not self.use_ddp:
+                            denoised_ae_loss, loss_dict = self.loss_class(
+                                denoised_ae_pred, micro, test_mode=False)
+                        else:
+                            with self.ddp_model.no_sync():  # type: ignore
+                                denoised_ae_loss, loss_dict = self.loss_class(
+                                    denoised_ae_pred, micro, test_mode=False)
+
+                        # * rename
+                        loss_dict_denoise_ae = {}
+                        for k, v in loss_dict.items():
+                            loss_dict_denoise_ae[f'{k}_denoised'] = v.mean()
+                        log_rec3d_loss_dict(loss_dict_denoise_ae)
+
+                else:
+                    denoised_ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                loss = ae_loss + denoise_loss + denoised_ae_loss
+                # self.mp_trainer.backward(denosied_ae_loss)
+                # self.mp_trainer.backward(loss)
+
+            # exit AMP before backward
+            self.mp_trainer.backward(loss)
+            # if self.freeze_ae:
+            # else:
+            # self.mp_trainer.backward(denoise_loss)
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            # if dist_util.get_rank() == 0 and self.step % 500 == 0:
+            if dist_util.get_rank() == 1 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+
+                    if self.freeze_ae:
+                        latent_micro = {
+                            k:
+                            v[0:1].to(dist_util.dev()) if v is not None else v
+                            for k, v in latent.items()
+                        }
+
+                        pred = self.rec_model(latent=latent_micro,
+                                              c=micro['c'][0:1],
+                                              behaviour='triplane_dec')
+                    else:
+                        assert pred is not None
+
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    # if 'image_sr' in pred:  # TODO
+                    #     pred_img = th.cat(
+                    #         [self.pool_512(pred_img), pred['image_sr']],
+                    #         dim=-1)
+                    #     gt_img = th.cat(
+                    #         [self.pool_512(micro['img']), micro['img_sr']],
+                    #         dim=-1)
+                    #     pred_depth = self.pool_512(pred_depth)
+                    #     gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'], micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    sr_w_code = latent_micro.get('sr_w_code', None)
+                    if sr_w_code is not None:
+                        sr_w_code = sr_w_code[0:1]
+
+                    noised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        latent={
+                            'latent_normalized':
+                            x_t[0:1] * self.triplane_scaling_divider,
+                            # 'sr_w_code': getattr(self.ddp_rec_model.module.decoder,'w_avg').reshape(1,1,-1)
+                            'sr_w_code': sr_w_code
+                        },  # TODO, how to define the scale automatically
+                        behaviour=self.render_latent_behaviour)
+
+                    denoised_fn = functools.partial(
+                        self.diffusion.p_mean_variance,
+                        self.ddp_model,
+                        x_t,  # x_start
+                        t,
+                        model_kwargs=model_kwargs)
+
+                    denoised_out = denoised_fn()
+
+                    denoised_ae_pred = self.ddp_rec_model(
+                        img=None,
+                        c=micro['c'][0:1],
+                        # latent=denoised_out['pred_xstart'][0:1] * self.
+                        # triplane_scaling_divider,  # TODO, how to define the scale automatically
+                        latent={
+                            'latent_normalized':
+                            denoised_out['pred_xstart'][0:1] * self.
+                            triplane_scaling_divider,  # TODO, how to define the scale automatically
+                            #   'sr_w_code': getattr(self.ddp_rec_model.module.decoder,'w_avg').reshape(1,1,-1)
+                            #   'sr_w_code': latent_micro['sr_w_code'][0:1]
+                            'sr_w_code':
+                            sr_w_code
+                        },
+                        behaviour=self.render_latent_behaviour)
+
+                    assert denoised_ae_pred is not None
+
+                    # print(pred_img.shape)
+                    # print('denoised_ae:', self.denoised_ae)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], noised_ae_pred['image_raw'],
+                        denoised_ae_pred['image_raw'],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis = th.cat([
+                    #     self.pool_128(micro['img']), x_t[:, :3, ...],
+                    #     denoised_out['pred_xstart'][:, :3, ...]
+                    # ],
+                    #              dim=-1)[0].permute(
+                    #                  1, 2, 0).cpu()  # ! pred in range[-1, 1]
+
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}.jpg'
+                    )
+
+                    th.cuda.empty_cache()
diff --git a/nsr/train_util_diffusion_accelerate.py b/nsr/train_util_diffusion_accelerate.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ec575b5129de44ec45e1427c3a4ca1ccc91e10f
--- /dev/null
+++ b/nsr/train_util_diffusion_accelerate.py
@@ -0,0 +1,938 @@
+import copy
+import functools
+import json
+import os
+from pathlib import Path
+from pdb import set_trace as st
+
+import blobfile as bf
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+import torchvision
+from PIL import Image
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.tensorboard.writer import SummaryWriter
+from tqdm import tqdm
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fp16_util import MixedPrecisionTrainer
+from guided_diffusion.nn import update_ema
+from guided_diffusion.resample import LossAwareSampler, UniformSampler
+# from .train_util import TrainLoop3DRec
+from guided_diffusion.train_util import (TrainLoop, calc_average_loss,
+                                         find_ema_checkpoint,
+                                         find_resume_checkpoint,
+                                         get_blob_logdir, log_loss_dict,
+                                         log_rec3d_loss_dict,
+                                         parse_resume_step_from_filename)
+
+import dnnlib
+
+# AMP
+from accelerate import Accelerator
+
+# from ..guided_diffusion.train_util import TrainLoop
+
+# use_amp = False
+# use_amp = True
+
+
+class TrainLoop3DDiffusion(TrainLoop):
+
+    def __init__(
+            self,
+            *,
+            # model,
+            rec_model,
+            denoise_model,
+            diffusion,
+            loss_class,
+            data,
+            eval_data,
+            batch_size,
+            microbatch,
+            lr,
+            ema_rate,
+            log_interval,
+            eval_interval,
+            save_interval,
+            resume_checkpoint,
+            use_fp16=False,
+            fp16_scale_growth=0.001,
+            schedule_sampler=None,
+            weight_decay=0,
+            lr_anneal_steps=0,
+            iterations=10001,
+            ignore_resume_opt=False,
+            freeze_ae=False,
+            denoised_ae=True,
+            triplane_scaling_divider=10,
+            use_amp=False,
+            **kwargs):
+
+        super().__init__(model=denoise_model,
+                         diffusion=diffusion,
+                         data=data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         lr_anneal_steps=lr_anneal_steps,
+                         weight_decay=weight_decay,
+                         use_amp=use_amp)
+        
+        self.accelerator = Accelerator()
+
+        self.pool_512 = th.nn.AdaptiveAvgPool2d((512, 512))
+        self.pool_128 = th.nn.AdaptiveAvgPool2d((128, 128))
+        self.loss_class = loss_class
+        self.rec_model = rec_model
+        self.eval_interval = eval_interval
+        self.eval_data = eval_data
+        self.iterations = iterations
+        # self.triplane_std = 10
+        self.triplane_scaling_divider = triplane_scaling_divider
+
+        self._load_and_sync_parameters(model=self.rec_model, model_name='rec')
+
+        # * for loading EMA
+        self.mp_trainer_rec = MixedPrecisionTrainer(
+            model=self.rec_model,
+            use_fp16=self.use_fp16,
+            use_amp=use_amp,
+            fp16_scale_growth=fp16_scale_growth,
+            model_name='rec',
+        )
+        self.denoised_ae = denoised_ae
+        if not freeze_ae:
+            self.opt_rec = AdamW(self._init_optim_groups(kwargs))
+        else:
+            print('!! freezing AE !!')
+
+        if dist_util.get_rank() == 0:
+            self.writer = SummaryWriter(log_dir=f'{logger.get_dir()}/runs')
+            print(self.opt)
+            if not freeze_ae:
+                print(self.opt_rec)
+
+        # if not freeze_ae:
+        if self.resume_step:
+            if not ignore_resume_opt:
+                self._load_optimizer_state()
+            else:
+                logger.warn("Ignoring optimizer state from checkpoint.")
+            # Model was resumed, either due to a restart or a checkpoint
+            # being specified at the command line.
+            # if not freeze_ae:
+            #     self.ema_params_rec = [
+            #         self._load_ema_parameters(
+            #             rate,
+            #             self.rec_model,
+            #             self.mp_trainer_rec,
+            #             model_name=self.mp_trainer_rec.model_name)
+            #         for rate in self.ema_rate
+            #     ]
+            # else:
+            self.ema_params_rec = [
+                self._load_ema_parameters(
+                    rate,
+                    self.rec_model,
+                    self.mp_trainer_rec,
+                    model_name=self.mp_trainer_rec.model_name)
+                for rate in self.ema_rate
+            ]
+        else:
+            if not freeze_ae:
+                self.ema_params_rec = [
+                    copy.deepcopy(self.mp_trainer_rec.master_params)
+                    for _ in range(len(self.ema_rate))
+                ]
+
+        if self.use_ddp is True:
+            self.rec_model = th.nn.SyncBatchNorm.convert_sync_batchnorm(
+                self.rec_model)
+            self.ddp_rec_model = DDP(
+                self.rec_model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+                # find_unused_parameters=True,
+            )
+        else:
+            self.ddp_rec_model = self.rec_model
+
+        if freeze_ae:
+            self.ddp_rec_model.eval()
+            self.ddp_rec_model.requires_grad_(False)
+        self.freeze_ae = freeze_ae
+
+        # if use_amp:
+
+    def _init_optim_groups(self, kwargs):
+        optim_groups = [
+            # vit encoder
+            {
+                'name': 'vit_encoder',
+                'params': self.mp_trainer_rec.model.encoder.parameters(),
+                'lr': kwargs['encoder_lr'],
+                'weight_decay': kwargs['encoder_weight_decay']
+            },
+            # vit decoder
+            {
+                'name':
+                'vit_decoder',
+                'params':
+                self.mp_trainer_rec.model.decoder.vit_decoder.parameters(),
+                'lr':
+                kwargs['vit_decoder_lr'],
+                'weight_decay':
+                kwargs['vit_decoder_wd']
+            },
+            {
+                'name':
+                'vit_decoder_pred',
+                'params':
+                self.mp_trainer_rec.model.decoder.decoder_pred.parameters(),
+                'lr':
+                kwargs['vit_decoder_lr'],
+                # 'weight_decay': 0
+                'weight_decay':
+                kwargs['vit_decoder_wd']
+            },
+
+            # triplane decoder
+            {
+                'name':
+                'triplane_decoder',
+                'params':
+                self.mp_trainer_rec.model.decoder.triplane_decoder.parameters(
+                ),
+                'lr':
+                kwargs['triplane_decoder_lr'],
+                # 'weight_decay': self.weight_decay
+            },
+        ]
+
+        if self.mp_trainer_rec.model.decoder.superresolution is not None:
+            optim_groups.append({
+                'name':
+                'triplane_decoder_superresolution',
+                'params':
+                self.mp_trainer_rec.model.decoder.superresolution.parameters(),
+                'lr':
+                kwargs['super_resolution_lr'],
+            })
+
+        return optim_groups
+
+    def run_loop(self, batch=None):
+        th.cuda.empty_cache()
+        while (not self.lr_anneal_steps
+               or self.step + self.resume_step < self.lr_anneal_steps):
+
+            # let all processes sync up before starting with a new epoch of training
+            dist_util.synchronize()
+
+            # batch, cond = next(self.data)
+            # if batch is None:
+            batch = next(self.data)
+            self.run_step(batch)
+            if self.step % self.log_interval == 0 and dist_util.get_rank(
+            ) == 0:
+                out = logger.dumpkvs()
+                # * log to tensorboard
+                for k, v in out.items():
+                    self.writer.add_scalar(f'Loss/{k}', v,
+                                           self.step + self.resume_step)
+
+            # if self.step % self.eval_interval == 0 and self.step != 0:
+            if self.step % self.eval_interval == 0:
+                if dist_util.get_rank() == 0:
+                    self.eval_ddpm_sample()
+                #     continue # TODO, diffusion inference
+                # self.eval_loop()
+                # self.eval_novelview_loop()
+                # let all processes sync up before starting with a new epoch of training
+                dist_util.synchronize()
+                th.cuda.empty_cache()
+
+            if self.step % self.save_interval == 0 and self.step != 0:
+                self.save()
+                if not self.freeze_ae:
+                    self.save(self.mp_trainer_rec, 'rec')
+                dist_util.synchronize()
+
+                th.cuda.empty_cache()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST",
+                                  "") and self.step > 0:
+                    return
+
+            self.step += 1
+
+            if self.step > self.iterations:
+                print('reached maximum iterations, exiting')
+
+                # Save the last checkpoint if it wasn't already saved.
+                if (self.step - 1) % self.save_interval != 0:
+                    self.save()
+                    if not self.freeze_ae:
+                        self.save(self.mp_trainer_rec, 'rec')
+
+                exit()
+
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+            if not self.freeze_ae:
+                self.save(self.mp_trainer_rec, 'rec')
+
+    def run_step(self, batch, cond=None):
+        self.forward_backward(batch,
+                              cond)  # type: ignore # * 3D Reconstruction step
+        took_step_ddpm = self.mp_trainer.optimize(self.opt)
+        if took_step_ddpm:
+            self._update_ema()
+
+        if not self.freeze_ae:
+            took_step_rec = self.mp_trainer_rec.optimize(self.opt_rec)
+            if took_step_rec:
+                self._update_ema_rec()
+
+        self._anneal_lr()
+        self.log_step()
+
+    def forward_backward(self, batch, *args, **kwargs):
+        # return super().forward_backward(batch, *args, **kwargs)
+        self.mp_trainer.zero_grad()
+        # all_denoised_out = dict()
+        batch_size = batch['img'].shape[0]
+
+        for i in range(0, batch_size, self.microbatch):
+
+            micro = {
+                k: v[i:i + self.microbatch].to(dist_util.dev())
+                for k, v in batch.items()
+            }
+
+            last_batch = (i + self.microbatch) >= batch_size
+
+            # if not freeze_ae:
+
+            # =================================== ae part ===================================
+            # with th.cuda.amp.autocast(dtype=th.float16,
+            #                           enabled=self.mp_trainer_rec.use_amp
+            #                           and not self.freeze_ae):
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=False,): # ! debugging, no AMP on all the input 
+
+                pred = self.ddp_rec_model(img=micro['img_to_encoder'],
+                                          c=micro['c'])  # pred: (B, 3, 64, 64)
+                if not self.freeze_ae:
+                    target = micro
+
+                    if last_batch or not self.use_ddp:
+                        ae_loss, loss_dict = self.loss_class(pred,
+                                                             target,
+                                                             test_mode=False)
+                    else:
+                        with self.ddp_model.no_sync():  # type: ignore
+                            ae_loss, loss_dict = self.loss_class(
+                                pred, target, test_mode=False)
+
+                    log_rec3d_loss_dict(loss_dict)
+                else:
+                    ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                micro_to_denoise = pred[
+                    'latent'] / self.triplane_scaling_divider  # normalize std to 1
+
+                t, weights = self.schedule_sampler.sample(
+                    micro_to_denoise.shape[0], dist_util.dev())
+
+                # print('!!!', micro_to_denoise.dtype)
+                # =================================== denoised part ===================================
+
+                model_kwargs = {}
+
+                # print(micro_to_denoise.min(), micro_to_denoise.max())
+                compute_losses = functools.partial(
+                    self.diffusion.training_losses,
+                    self.ddp_model,
+                    micro_to_denoise,  # x_start
+                    t,
+                    model_kwargs=model_kwargs,
+                )
+
+                denoised_fn = functools.partial(
+                    self.diffusion.p_mean_variance,
+                    self.ddp_model,
+                    micro_to_denoise,  # x_start
+                    t,
+                    model_kwargs=model_kwargs)
+
+            with th.cuda.amp.autocast(dtype=th.float16,
+                                      enabled=self.mp_trainer.use_amp):
+
+                if last_batch or not self.use_ddp:
+                    losses = compute_losses()
+                    denoised_out = denoised_fn()
+                else:
+                    with self.ddp_model.no_sync():  # type: ignore
+                        losses = compute_losses()
+                        denoised_out = denoised_fn()
+
+                if isinstance(self.schedule_sampler, LossAwareSampler):
+                    self.schedule_sampler.update_with_local_losses(
+                        t, losses["loss"].detach())
+
+                denoise_loss = (losses["loss"] * weights).mean()
+                log_loss_dict(self.diffusion, t,
+                              {k: v * weights
+                               for k, v in losses.items()})
+
+                # self.mp_trainer.backward(denoise_loss)
+                # =================================== denosied ae part ===================================
+                # if self.denoised_ae or self.step % 500 == 0:
+                if self.denoised_ae:
+                    with th.cuda.amp.autocast(
+                            dtype=th.float16,
+                            enabled=self.mp_trainer_rec.use_amp
+                            and not self.freeze_ae):
+                        # continue
+
+                        denoised_ae_pred = self.ddp_rec_model(
+                            img=None,
+                            c=micro['c'],
+                            latent=denoised_out['pred_xstart'] * self.
+                            triplane_scaling_divider,  # TODO, how to define the scale automatically?
+                            behaviour='triplane_dec')
+
+                        # if self.denoised_ae:
+
+                        if last_batch or not self.use_ddp:
+                            denoised_ae_loss, loss_dict = self.loss_class(
+                                denoised_ae_pred, micro, test_mode=False)
+                        else:
+                            with self.ddp_model.no_sync():  # type: ignore
+                                denoised_ae_loss, loss_dict = self.loss_class(
+                                    denoised_ae_pred, micro, test_mode=False)
+
+                        # * rename
+                        loss_dict_denoise_ae = {}
+                        for k, v in loss_dict.items():
+                            loss_dict_denoise_ae[f'{k}_denoised'] = v.mean()
+                        log_rec3d_loss_dict(loss_dict_denoise_ae)
+
+                else:
+                    denoised_ae_loss = th.tensor(0.0).to(dist_util.dev())
+
+                # loss = ae_loss + denoise_loss + denoised_ae_loss
+                loss = denoise_loss # ! leave only denoise_loss for debugging
+                # loss = ae_loss + denoise_loss
+                # self.mp_trainer.backward(denosied_ae_loss)
+                # if use_amp:
+                # self.mp_trainer.backward(loss)
+                # self.mp_trainer.scaler.scale(loss).backward()
+                # else:
+
+            # exit AMP before backward
+            self.mp_trainer.backward(loss)
+
+            # TODO, merge visualization with original AE
+            # =================================== denoised AE log part ===================================
+
+            if dist_util.get_rank() == 0 and self.step % 500 == 0:
+                with th.no_grad():
+                    # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1)
+
+                    gt_depth = micro['depth']
+                    if gt_depth.ndim == 3:
+                        gt_depth = gt_depth.unsqueeze(1)
+                    gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() -
+                                                              gt_depth.min())
+                    # if True:
+                    pred_depth = pred['image_depth']
+                    pred_depth = (pred_depth - pred_depth.min()) / (
+                        pred_depth.max() - pred_depth.min())
+                    pred_img = pred['image_raw']
+                    gt_img = micro['img']
+
+                    if 'image_sr' in pred:  # TODO
+                        pred_img = th.cat(
+                            [self.pool_512(pred_img), pred['image_sr']],
+                            dim=-1)
+                        gt_img = th.cat(
+                            [self.pool_512(micro['img']), micro['img_sr']],
+                            dim=-1)
+                        pred_depth = self.pool_512(pred_depth)
+                        gt_depth = self.pool_512(gt_depth)
+
+                    gt_vis = th.cat(
+                        [
+                            gt_img, micro['img'],
+                            gt_depth.repeat_interleave(3, dim=1)
+                        ],
+                        dim=-1)[0:1]  # TODO, fail to load depth. range [0, 1]
+
+                    if not self.denoised_ae:
+                        # continue
+
+                        denoised_ae_pred = self.ddp_rec_model(
+                            img=None,
+                            c=micro['c'][0:1],
+                            latent=denoised_out['pred_xstart'][0:1] * self.
+                            triplane_scaling_divider,  # TODO, how to define the scale automatically
+                            behaviour='triplane_dec')
+
+                    # assert denoised_ae_pred is not None
+
+                    # print(pred_img.shape)
+                    # print('denoised_ae:', self.denoised_ae)
+
+                    pred_vis = th.cat([
+                        pred_img[0:1], denoised_ae_pred['image_raw'],
+                        pred_depth[0:1].repeat_interleave(3, dim=1)
+                    ],
+                                      dim=-1)  # B, 3, H, W
+
+                    vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(
+                        1, 2, 0).cpu()  # ! pred in range[-1, 1]
+                    # vis_grid = torchvision.utils.make_grid(vis) # HWC
+                    vis = vis.numpy() * 127.5 + 127.5
+                    vis = vis.clip(0, 255).astype(np.uint8)
+                    Image.fromarray(vis).save(
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}.jpg'
+                    )
+                    print(
+                        'log denoised vis to: ',
+                        f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}.jpg'
+                    )
+
+                    th.cuda.empty_cache()
+
+    @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    def eval_novelview_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        all_loss_dict = []
+        novel_view_micro = {}
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i == 0:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            pred = self.model(img=novel_view_micro['img_to_encoder'],
+                              c=micro['c'])  # pred: (B, 3, 64, 64)
+            # target = {
+            #     'img': micro['img'],
+            #     'depth': micro['depth'],
+            #     'depth_mask': micro['depth_mask']
+            # }
+            # targe
+
+            _, loss_dict = self.loss_class(pred, micro, test_mode=True)
+            all_loss_dict.append(loss_dict)
+
+            # ! move to other places, add tensorboard
+
+            # pred_vis = th.cat([
+            #     pred['image_raw'],
+            #     -pred['image_depth'].repeat_interleave(3, dim=1)
+            # ],
+            #                   dim=-1)
+
+            # normalize depth
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+            if 'image_sr' in pred:
+                pred_vis = th.cat([
+                    micro['img_sr'],
+                    self.pool_512(pred['image_raw']), pred['image_sr'],
+                    self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores_novelview.json'),
+                  'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/NovelView/{k}', v,
+                                   self.step + self.resume_step)
+
+    @th.no_grad()
+    # def eval_loop(self, c_list:list):
+    def eval_loop(self):
+        # novel view synthesis given evaluation camera trajectory
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/video_{self.step+self.resume_step}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+        all_loss_dict = []
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            # for i in range(0, 8, self.microbatch):
+            # c = c_list[i].to(dist_util.dev()).reshape(1, -1)
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            # pred = self.model(img=micro['img_to_encoder'],
+            #                   c=micro['c'])  # pred: (B, 3, 64, 64)
+
+            # pred of rec model
+            pred = self.ddp_rec_model(img=micro['img_to_encoder'],
+                                      c=micro['c'])  # pred: (B, 3, 64, 64)
+
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+                pred_vis = th.cat([
+                    micro['img_sr'],
+                    self.pool_512(pred['image_raw']), pred['image_sr'],
+                    self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+
+        val_scores_for_logging = calc_average_loss(all_loss_dict)
+        with open(os.path.join(logger.get_dir(), 'scores.json'), 'a') as f:
+            json.dump({'step': self.step, **val_scores_for_logging}, f)
+
+        # * log to tensorboard
+        for k, v in val_scores_for_logging.items():
+            self.writer.add_scalar(f'Eval/Rec/{k}', v,
+                                   self.step + self.resume_step)
+
+        self.eval_novelview_loop()
+
+    def save(self, mp_trainer=None, model_name='ddpm'):
+        if mp_trainer is None:
+            mp_trainer = self.mp_trainer
+
+        def save_checkpoint(rate, params):
+            state_dict = mp_trainer.master_params_to_state_dict(params)
+            if dist_util.get_rank() == 0:
+                logger.log(f"saving model {model_name} {rate}...")
+                if not rate:
+                    filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt"
+                else:
+                    filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt"
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename),
+                                 "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, self.mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        dist.barrier()
+
+    def _load_and_sync_parameters(self, model=None, model_name='ddpm'):
+        resume_checkpoint, self.resume_step = find_resume_checkpoint(
+            self.resume_checkpoint, model_name) or self.resume_checkpoint
+
+        if model is None:
+            model = self.model
+        print(resume_checkpoint)
+
+        if resume_checkpoint and Path(resume_checkpoint).exists():
+            if dist_util.get_rank() == 0:
+
+                # ! rank 0 return will cause all other ranks to hang
+                # if not Path(resume_checkpoint).exists():
+                #     logger.log(
+                #         f"failed to load model from checkpoint: {resume_checkpoint}, not exist"
+                #     )
+                #     return
+
+                logger.log(
+                    f"loading model from checkpoint: {resume_checkpoint}...")
+                map_location = {
+                    'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank()
+                }  # configure map_location properly
+
+                print(f'mark {model_name} loading ', flush=True)
+                resume_state_dict = dist_util.load_state_dict(
+                    resume_checkpoint, map_location=map_location)
+                print(f'mark {model_name} loading finished', flush=True)
+
+                model_state_dict = model.state_dict()
+
+                for k, v in resume_state_dict.items():
+                    if k in model_state_dict.keys() and v.size(
+                    ) == model_state_dict[k].size():
+                        model_state_dict[k] = v
+                    else:
+                        print('!!!! ignore key: ', k, ": ", v.size(),
+                              'shape in model: ', model_state_dict[k].size())
+
+                model.load_state_dict(model_state_dict, strict=True)
+                del model_state_dict
+
+        if dist_util.get_world_size() > 1:
+            dist_util.sync_params(model.parameters())
+            print(f'synced {model_name} params')
+
+    def _update_ema_rec(self):
+        for rate, params in zip(self.ema_rate, self.ema_params_rec):
+            update_ema(params, self.mp_trainer_rec.master_params, rate=rate)
+
+    def eval_ddpm_sample(self):
+
+        args = dnnlib.EasyDict(
+            dict(batch_size=1,
+                 image_size=224,
+                 denoise_in_channels=24,
+                 clip_denoised=True,
+                 class_cond=False,
+                 use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        for i in range(2):
+            triplane_sample = sample_fn(
+                self.ddp_model,
+                (args.batch_size, args.denoise_in_channels, args.image_size,
+                 args.image_size),
+                clip_denoised=args.clip_denoised,
+                model_kwargs=model_kwargs,
+            )
+
+            self.render_video_given_triplane(
+                triplane_sample,
+                name_prefix=f'{self.step + self.resume_step}_{i}')
+
+    @th.inference_mode()
+    def render_video_given_triplane(self, planes, name_prefix='0'):
+
+        planes *= self.triplane_scaling_divider  # if setting clip_denoised=True, the sampled planes will lie in [-1,1]. Thus, values beyond [+- std] will be abandoned in this version. Move to IN for later experiments.
+
+        # print(planes.min(), planes.max())
+
+        # used during diffusion sampling inference
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_{name_prefix}.mp4',
+            mode='I',
+            fps=60,
+            codec='libx264')
+
+        # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            pred = self.ddp_rec_model(img=None,
+                                      c=micro['c'],
+                                      latent=planes,
+                                      behaviour='triplane_dec')
+
+            # if True:
+            pred_depth = pred['image_depth']
+            pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() -
+                                                            pred_depth.min())
+
+            if 'image_sr' in pred:
+                pred_vis = th.cat([
+                    micro['img_sr'],
+                    self.pool_512(pred['image_raw']), pred['image_sr'],
+                    self.pool_512(pred_depth).repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)
+            else:
+                pred_vis = th.cat([
+                    self.pool_128(micro['img']), pred['image_raw'],
+                    pred_depth.repeat_interleave(3, dim=1)
+                ],
+                                  dim=-1)  # B, 3, H, W
+
+            vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy()
+            vis = vis * 127.5 + 127.5
+            vis = vis.clip(0, 255).astype(np.uint8)
+
+            for j in range(vis.shape[0]):
+                video_out.append_data(vis[j])
+
+        video_out.close()
+        print('logged video to: ',
+              f'{logger.get_dir()}/triplane_{name_prefix}.mp4')
+
+    @th.inference_mode()
+    def render_video_noise_schedule(self, name_prefix='0'):
+
+        # planes *= self.triplane_std # denormalize for rendering
+
+        video_out = imageio.get_writer(
+            f'{logger.get_dir()}/triplane_visnoise_{name_prefix}.mp4',
+            mode='I',
+            fps=30,
+            codec='libx264')
+
+        for i, batch in enumerate(tqdm(self.eval_data)):
+            micro = {k: v.to(dist_util.dev()) for k, v in batch.items()}
+
+            if i % 10 != 0:
+                continue
+
+            # ========= novel view plane settings ====
+            if i == 0:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in batch.items()
+                }
+            else:
+                # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]:
+                novel_view_micro = {
+                    k: v[0:1].to(dist_util.dev()).repeat_interleave(
+                        micro['img'].shape[0], 0)
+                    for k, v in novel_view_micro.items()
+                }
+
+            latent = self.ddp_rec_model(
+                img=novel_view_micro['img_to_encoder'],
+                c=micro['c'])['latent']  # pred: (B, 3, 64, 64)
+
+            x_start = latent / self.triplane_scaling_divider  # normalize std to 1
+            # x_start = latent
+
+            all_pred_vis = []
+            # for t in th.range(0,
+            #                   4001,
+            #                   500,
+            #                   dtype=th.long,
+            #                   device=dist_util.dev()):  # cosine 4k steps
+            for t in th.range(0,
+                              1001,
+                              125,
+                              dtype=th.long,
+                              device=dist_util.dev()):  # cosine 4k steps
+
+                # ========= add noise according to t
+                noise = th.randn_like(x_start)  # x_start is the x0 image
+                x_t = self.diffusion.q_sample(
+                    x_start, t, noise=noise
+                )  # * add noise according to predefined schedule
+                planes_x_t = (x_t * self.triplane_scaling_divider).clamp(
+                    -50, 50)  # de-scaling noised x_t
+
+                # planes_x_t = (x_t * 1).clamp(
+                #     -50, 50)  # de-scaling noised x_t
+
+                # ===== visualize
+                pred = self.ddp_rec_model(
+                    img=None,
+                    c=micro['c'],
+                    latent=planes_x_t,
+                    behaviour='triplane_dec')  # pred: (B, 3, 64, 64)
+
+                # pred_depth = pred['image_depth']
+                # pred_depth = (pred_depth - pred_depth.min()) / (
+                #     pred_depth.max() - pred_depth.min())
+                # pred_vis = th.cat([
+                #     # self.pool_128(micro['img']),
+                #     pred['image_raw'],
+                # ],
+                #                   dim=-1)  # B, 3, H, W
+                pred_vis = pred['image_raw']
+
+                all_pred_vis.append(pred_vis)
+                # TODO, make grid
+
+            all_pred_vis = torchvision.utils.make_grid(
+                th.cat(all_pred_vis, 0),
+                nrow=len(all_pred_vis),
+                normalize=True,
+                value_range=(-1, 1),
+                scale_each=True)  # normalized to [-1,1]
+
+            vis = all_pred_vis.permute(1, 2, 0).cpu().numpy()  # H W 3
+
+            vis = (vis * 255).clip(0, 255).astype(np.uint8)
+
+            video_out.append_data(vis)
+
+        video_out.close()
+        print('logged video to: ',
+              f'{logger.get_dir()}/triplane_visnoise_{name_prefix}.mp4')
+
+        th.cuda.empty_cache()
diff --git a/nsr/train_util_diffusion_dit.py b/nsr/train_util_diffusion_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfeef5cbedb0a837a00d545ebd98d2b32a3fea8
--- /dev/null
+++ b/nsr/train_util_diffusion_dit.py
@@ -0,0 +1,105 @@
+from .train_util_diffusion import TrainLoop3DDiffusion
+import dnnlib
+import torch as th
+
+
+class TrainLoop3DDiffusionDiT(TrainLoop3DDiffusion):
+
+    def __init__(self,
+                 *,
+                 rec_model,
+                 denoise_model,
+                 diffusion,
+                 loss_class,
+                 data,
+                 eval_data,
+                 batch_size,
+                 microbatch,
+                 lr,
+                 ema_rate,
+                 log_interval,
+                 eval_interval,
+                 save_interval,
+                 resume_checkpoint,
+                 use_fp16=False,
+                 fp16_scale_growth=0.001,
+                 schedule_sampler=None,
+                 weight_decay=0,
+                 lr_anneal_steps=0,
+                 iterations=10001,
+                 ignore_resume_opt=False,
+                 freeze_ae=False,
+                 denoised_ae=True,
+                 triplane_scaling_divider=10,
+                 use_amp=False,
+                 **kwargs):
+        super().__init__(rec_model=rec_model,
+                         denoise_model=denoise_model,
+                         diffusion=diffusion,
+                         loss_class=loss_class,
+                         data=data,
+                         eval_data=eval_data,
+                         batch_size=batch_size,
+                         microbatch=microbatch,
+                         lr=lr,
+                         ema_rate=ema_rate,
+                         log_interval=log_interval,
+                         eval_interval=eval_interval,
+                         save_interval=save_interval,
+                         resume_checkpoint=resume_checkpoint,
+                         use_fp16=use_fp16,
+                         fp16_scale_growth=fp16_scale_growth,
+                         schedule_sampler=schedule_sampler,
+                         weight_decay=weight_decay,
+                         lr_anneal_steps=lr_anneal_steps,
+                         iterations=iterations,
+                         ignore_resume_opt=ignore_resume_opt,
+                         freeze_ae=freeze_ae,
+                         denoised_ae=denoised_ae,
+                         triplane_scaling_divider=triplane_scaling_divider,
+                         use_amp=use_amp,
+                         **kwargs)
+
+        self.latent_name = 'latent_from_vit'
+        self.render_latent_behaviour = 'vit_postprocess_triplane_dec'  # translate latent into 2D spatial tokens, then triplane render
+
+    def eval_ddpm_sample(self):
+
+        args = dnnlib.EasyDict(
+            dict(batch_size=1,
+                 image_size=224,
+                 denoise_in_channels=self.ddp_rec_model.module.decoder.triplane_decoder.out_chans, # type: ignore
+                 clip_denoised=False,
+                 class_cond=False,
+                 use_ddim=False))
+
+        model_kwargs = {}
+
+        if args.class_cond:
+            classes = th.randint(low=0,
+                                 high=NUM_CLASSES,
+                                 size=(args.batch_size, ),
+                                 device=dist_util.dev())
+            model_kwargs["y"] = classes
+
+        diffusion = self.diffusion
+        sample_fn = (diffusion.p_sample_loop
+                     if not args.use_ddim else diffusion.ddim_sample_loop)
+
+        vit_L = (224//14)**2 # vit sequence length
+
+        if self.ddp_rec_model.module.decoder.vit_decoder.cls_token: 
+            vit_L += 1
+
+        for i in range(1):
+            triplane_sample = sample_fn(
+                self.ddp_model,
+                (args.batch_size, vit_L, self.ddp_rec_model.module.decoder.vit_decoder.embed_dim), # vit token size, N L C
+                clip_denoised=args.clip_denoised,
+                model_kwargs=model_kwargs,
+            )
+
+            th.cuda.empty_cache()
+            self.render_video_given_triplane(
+                triplane_sample,
+                name_prefix=f'{self.step + self.resume_step}_{i}')
\ No newline at end of file
diff --git a/nsr/triplane.py b/nsr/triplane.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c4d65f82b0524b6790a30102a9521aac7236348
--- /dev/null
+++ b/nsr/triplane.py
@@ -0,0 +1,1088 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+from threading import local
+import torch
+import torch.nn as nn
+from torch_utils import persistence
+from .networks_stylegan2 import Generator as StyleGAN2Backbone
+from .networks_stylegan2 import ToRGBLayer, SynthesisNetwork, MappingNetwork
+from .volumetric_rendering.renderer import ImportanceRenderer
+from .volumetric_rendering.ray_sampler import RaySampler, PatchRaySampler
+import dnnlib
+from pdb import set_trace as st
+import math
+
+import torch.nn.functional as F
+import itertools
+from ldm.modules.diffusionmodules.model import SimpleDecoder, Decoder
+
+
+@persistence.persistent_class
+class TriPlaneGenerator(torch.nn.Module):
+
+    def __init__(
+            self,
+            z_dim,  # Input latent (Z) dimensionality.
+            c_dim,  # Conditioning label (C) dimensionality.
+            w_dim,  # Intermediate latent (W) dimensionality.
+            img_resolution,  # Output resolution.
+            img_channels,  # Number of output color channels.
+            sr_num_fp16_res=0,
+            mapping_kwargs={},  # Arguments for MappingNetwork.
+            rendering_kwargs={},
+            sr_kwargs={},
+            bcg_synthesis_kwargs={},
+            # pifu_kwargs={},
+            # ada_kwargs={},  # not used, place holder
+            **synthesis_kwargs,  # Arguments for SynthesisNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.renderer = ImportanceRenderer()
+        # if 'PatchRaySampler' in rendering_kwargs:
+        #     self.ray_sampler = PatchRaySampler()
+        # else:
+        #     self.ray_sampler = RaySampler()
+        self.backbone = StyleGAN2Backbone(z_dim,
+                                          c_dim,
+                                          w_dim,
+                                          img_resolution=256,
+                                          img_channels=32 * 3,
+                                          mapping_kwargs=mapping_kwargs,
+                                          **synthesis_kwargs)
+        self.superresolution = dnnlib.util.construct_class_by_name(
+            class_name=rendering_kwargs['superresolution_module'],
+            channels=32,
+            img_resolution=img_resolution,
+            sr_num_fp16_res=sr_num_fp16_res,
+            sr_antialias=rendering_kwargs['sr_antialias'],
+            **sr_kwargs)
+
+        # self.bcg_synthesis = None
+        if rendering_kwargs.get('use_background', False):
+            self.bcg_synthesis = SynthesisNetwork(
+                w_dim,
+                img_resolution=self.superresolution.input_resolution,
+                img_channels=32,
+                **bcg_synthesis_kwargs)
+            self.bcg_mapping = MappingNetwork(z_dim=z_dim,
+                                              c_dim=c_dim,
+                                              w_dim=w_dim,
+                                              num_ws=self.num_ws,
+                                              **mapping_kwargs)
+        # New mapping network for self-adaptive camera pose, dim = 3
+
+        self.decoder = OSGDecoder(
+            32, {
+                'decoder_lr_mul': rendering_kwargs.get('decoder_lr_mul', 1),
+                'decoder_output_dim': 32
+            })
+        self.neural_rendering_resolution = 64
+        self.rendering_kwargs = rendering_kwargs
+
+        self._last_planes = None
+        self.pool_256 = torch.nn.AdaptiveAvgPool2d((256, 256))
+
+    def mapping(self,
+                z,
+                c,
+                truncation_psi=1,
+                truncation_cutoff=None,
+                update_emas=False):
+        if self.rendering_kwargs['c_gen_conditioning_zero']:
+            c = torch.zeros_like(c)
+        return self.backbone.mapping(z,
+                                     c *
+                                     self.rendering_kwargs.get('c_scale', 0),
+                                     truncation_psi=truncation_psi,
+                                     truncation_cutoff=truncation_cutoff,
+                                     update_emas=update_emas)
+
+    def synthesis(self,
+                  ws,
+                  c,
+                  neural_rendering_resolution=None,
+                  update_emas=False,
+                  cache_backbone=False,
+                  use_cached_backbone=False,
+                  return_meta=False,
+                  return_raw_only=False,
+                  **synthesis_kwargs):
+
+        return_sampling_details_flag = self.rendering_kwargs.get(
+            'return_sampling_details_flag', False)
+
+        if return_sampling_details_flag:
+            return_meta = True
+
+        cam2world_matrix = c[:, :16].view(-1, 4, 4)
+        # cam2world_matrix = torch.eye(4, device=c.device).unsqueeze(0).repeat_interleave(c.shape[0], dim=0)
+        # c[:, :16] = cam2world_matrix.view(-1, 16)
+        intrinsics = c[:, 16:25].view(-1, 3, 3)
+
+        if neural_rendering_resolution is None:
+            neural_rendering_resolution = self.neural_rendering_resolution
+        else:
+            self.neural_rendering_resolution = neural_rendering_resolution
+
+        H = W = self.neural_rendering_resolution
+        # Create a batch of rays for volume rendering
+        ray_origins, ray_directions = self.ray_sampler(
+            cam2world_matrix, intrinsics, neural_rendering_resolution)
+
+        # Create triplanes by running StyleGAN backbone
+        N, M, _ = ray_origins.shape
+        if use_cached_backbone and self._last_planes is not None:
+            planes = self._last_planes
+        else:
+            planes = self.backbone.synthesis(
+                ws[:, :self.backbone.num_ws, :],  # ws, BS 14 512
+                update_emas=update_emas,
+                **synthesis_kwargs)
+        if cache_backbone:
+            self._last_planes = planes
+
+        # Reshape output into three 32-channel planes
+        planes = planes.view(len(planes), 3, 32, planes.shape[-2],
+                             planes.shape[-1])  # BS 96 256 256
+
+        # Perform volume rendering
+        # st()
+        rendering_details = self.renderer(
+            planes,
+            self.decoder,
+            ray_origins,
+            ray_directions,
+            self.rendering_kwargs,
+            #   return_meta=True)
+            return_meta=return_meta)
+
+        # calibs = create_calib_matrix(c)
+        # all_coords = rendering_details['all_coords']
+        # B, num_rays, S, _ = all_coords.shape
+        # all_coords_B3N = all_coords.reshape(B, -1, 3).permute(0,2,1)
+        # homo_coords = torch.cat([all_coords, torch.zeros_like(all_coords[..., :1])], -1)
+        # homo_coords[..., -1] = 1
+        # homo_coords = homo_coords.reshape(homo_coords.shape[0], -1, 4)
+        # homo_coords = homo_coords.permute(0,2,1)
+        # xyz = calibs @ homo_coords
+        # xyz = xyz.permute(0,2,1).reshape(B, H, W, S, 4)
+        # st()
+
+        # xyz_proj = perspective(all_coords_B3N, calibs)
+        # xyz_proj = xyz_proj.permute(0,2,1).reshape(B, H, W, S, 3) # [0,0] - [1,1]
+        # st()
+
+        feature_samples, depth_samples, weights_samples = (
+            rendering_details[k]
+            for k in ['feature_samples', 'depth_samples', 'weights_samples'])
+
+        if return_sampling_details_flag:
+            shape_synthesized = rendering_details['shape_synthesized']
+        else:
+            shape_synthesized = None
+
+        # Reshape into 'raw' neural-rendered image
+        feature_image = feature_samples.permute(0, 2, 1).reshape(
+            N, feature_samples.shape[-1], H, W).contiguous()  # B 32 H W
+        depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W)
+
+        # Run superresolution to get final image
+        rgb_image = feature_image[:, :3]  # B 3 H W
+        if not return_raw_only:
+            sr_image = self.superresolution(
+                rgb_image,
+                feature_image,
+                ws[:, -1:, :],  # only use the last layer
+                noise_mode=self.rendering_kwargs['superresolution_noise_mode'],
+                **{
+                    k: synthesis_kwargs[k]
+                    for k in synthesis_kwargs.keys() if k != 'noise_mode'
+                })
+        else:
+            sr_image = rgb_image
+
+        ret_dict = {
+            'image': sr_image,
+            'image_raw': rgb_image,
+            'image_depth': depth_image,
+            'weights_samples': weights_samples,
+            'shape_synthesized': shape_synthesized
+        }
+        if return_meta:
+            ret_dict.update({
+                # 'feature_image': feature_image,
+                'feature_volume':
+                rendering_details['feature_volume'],
+                'all_coords':
+                rendering_details['all_coords'],
+                'weights':
+                rendering_details['weights'],
+            })
+
+        return ret_dict
+
+    def sample(self,
+               coordinates,
+               directions,
+               z,
+               c,
+               truncation_psi=1,
+               truncation_cutoff=None,
+               update_emas=False,
+               **synthesis_kwargs):
+        # Compute RGB features, density for arbitrary 3D coordinates. Mostly used for extracting shapes.
+        ws = self.mapping(z,
+                          c,
+                          truncation_psi=truncation_psi,
+                          truncation_cutoff=truncation_cutoff,
+                          update_emas=update_emas)
+        planes = self.backbone.synthesis(ws,
+                                         update_emas=update_emas,
+                                         **synthesis_kwargs)
+        planes = planes.view(len(planes), 3, 32, planes.shape[-2],
+                             planes.shape[-1])
+        return self.renderer.run_model(planes, self.decoder, coordinates,
+                                       directions, self.rendering_kwargs)
+
+    def sample_mixed(self,
+                     coordinates,
+                     directions,
+                     ws,
+                     truncation_psi=1,
+                     truncation_cutoff=None,
+                     update_emas=False,
+                     **synthesis_kwargs):
+        # Same as sample, but expects latent vectors 'ws' instead of Gaussian noise 'z'
+        planes = self.backbone.synthesis(ws,
+                                         update_emas=update_emas,
+                                         **synthesis_kwargs)
+        planes = planes.view(len(planes), 3, 32, planes.shape[-2],
+                             planes.shape[-1])
+        return self.renderer.run_model(planes, self.decoder, coordinates,
+                                       directions, self.rendering_kwargs)
+
+    def forward(self,
+                z,
+                c,
+                truncation_psi=1,
+                truncation_cutoff=None,
+                neural_rendering_resolution=None,
+                update_emas=False,
+                cache_backbone=False,
+                use_cached_backbone=False,
+                **synthesis_kwargs):
+        # Render a batch of generated images.
+        ws = self.mapping(z,
+                          c,
+                          truncation_psi=truncation_psi,
+                          truncation_cutoff=truncation_cutoff,
+                          update_emas=update_emas)
+        return self.synthesis(
+            ws,
+            c,
+            update_emas=update_emas,
+            neural_rendering_resolution=neural_rendering_resolution,
+            cache_backbone=cache_backbone,
+            use_cached_backbone=use_cached_backbone,
+            **synthesis_kwargs)
+
+
+from .networks_stylegan2 import FullyConnectedLayer
+
+# class OSGDecoder(torch.nn.Module):
+
+#     def __init__(self, n_features, options):
+#         super().__init__()
+#         self.hidden_dim = 64
+#         self.output_dim = options['decoder_output_dim']
+#         self.n_features = n_features
+
+#         self.net = torch.nn.Sequential(
+#             FullyConnectedLayer(n_features,
+#                                 self.hidden_dim,
+#                                 lr_multiplier=options['decoder_lr_mul']),
+#             torch.nn.Softplus(),
+#             FullyConnectedLayer(self.hidden_dim,
+#                                 1 + options['decoder_output_dim'],
+#                                 lr_multiplier=options['decoder_lr_mul']))
+
+#     def forward(self, sampled_features, ray_directions):
+#         # Aggregate features
+#         sampled_features = sampled_features.mean(1)
+#         x = sampled_features
+
+#         N, M, C = x.shape
+#         x = x.view(N * M, C)
+
+#         x = self.net(x)
+#         x = x.view(N, M, -1)
+#         rgb = torch.sigmoid(x[..., 1:]) * (
+#             1 + 2 * 0.001) - 0.001  # Uses sigmoid clamping from MipNeRF
+#         sigma = x[..., 0:1]
+#         return {'rgb': rgb, 'sigma': sigma}
+
+
+@persistence.persistent_class
+class OSGDecoder(torch.nn.Module):
+
+    def __init__(self, n_features, options):
+        super().__init__()
+        self.hidden_dim = 64
+        self.decoder_output_dim = options['decoder_output_dim']
+
+        self.net = torch.nn.Sequential(
+            FullyConnectedLayer(n_features,
+                                self.hidden_dim,
+                                lr_multiplier=options['decoder_lr_mul']),
+            torch.nn.Softplus(),
+            FullyConnectedLayer(self.hidden_dim,
+                                1 + options['decoder_output_dim'],
+                                lr_multiplier=options['decoder_lr_mul']))
+        self.activation = options.get('decoder_activation', 'sigmoid')
+
+    def forward(self, sampled_features, ray_directions):
+        # Aggregate features
+        sampled_features = sampled_features.mean(1)
+        x = sampled_features
+
+        N, M, C = x.shape
+        x = x.view(N * M, C)
+
+        x = self.net(x)
+        x = x.view(N, M, -1)
+        rgb = x[..., 1:]
+        sigma = x[..., 0:1]
+        if self.activation == "sigmoid":
+            # Original EG3D
+            rgb = torch.sigmoid(rgb) * (1 + 2 * 0.001) - 0.001
+        elif self.activation == "lrelu":
+            # StyleGAN2-style, use with toRGB
+            rgb = torch.nn.functional.leaky_relu(rgb, 0.2,
+                                                 inplace=True) * math.sqrt(2)
+        return {'rgb': rgb, 'sigma': sigma}
+
+
+class OSGDecoderFlexicube(OSGDecoder):
+    # https://github.com/TencentARC/InstantMesh/blob/0a64425c6d390afa40128132cec42cd5c6408bbf/src/models/renderer/synthesizer_mesh.py#L15
+    def __init__(self, n_features, options, hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU):
+        super().__init__(n_features, options)
+
+        # self.net_sdf = nn.Sequential(
+        #     nn.Linear(3 * n_features, hidden_dim),
+        #     activation(),
+        #     *itertools.chain(*[[
+        #         nn.Linear(hidden_dim, hidden_dim),
+        #         activation(),
+        #     ] for _ in range(num_layers - 2)]),
+        #     nn.Linear(hidden_dim, 1),
+        # )
+        # self.net_rgb = nn.Sequential(
+        #     nn.Linear(3 * n_features, hidden_dim),
+        #     activation(),
+        #     *itertools.chain(*[[
+        #         nn.Linear(hidden_dim, hidden_dim),
+        #         activation(),
+        #     ] for _ in range(num_layers - 2)]),
+        #     nn.Linear(hidden_dim, 3),
+        # )
+
+        # ! sdf and rgb prediction adopts old tradition, softplus here
+        # TODO, load pre-trained model weights
+        self.net_sdf = torch.nn.Sequential(
+            FullyConnectedLayer(n_features,
+                                self.hidden_dim,
+                                lr_multiplier=options['decoder_lr_mul']),
+            torch.nn.Softplus(),
+            FullyConnectedLayer(self.hidden_dim,
+                                1,
+                                lr_multiplier=options['decoder_lr_mul']))
+
+        self.net_rgb = torch.nn.Sequential(
+            FullyConnectedLayer(n_features,
+                                self.hidden_dim,
+                                lr_multiplier=options['decoder_lr_mul']),
+            torch.nn.Softplus(),
+            FullyConnectedLayer(self.hidden_dim,
+                                options['decoder_output_dim'],
+                                lr_multiplier=options['decoder_lr_mul']))
+
+
+        # ! for following MLP, use new behaviour
+        self.net_deformation = nn.Sequential(
+            nn.Linear(3 * n_features, hidden_dim),
+            activation(),
+            *itertools.chain(*[[
+                nn.Linear(hidden_dim, hidden_dim),
+                activation(),
+            ] for _ in range(num_layers - 2)]),
+            nn.Linear(hidden_dim, 3),
+        )
+        self.net_weight = nn.Sequential(
+            nn.Linear(8 * 3 * n_features, hidden_dim),
+            activation(),
+            *itertools.chain(*[[
+                nn.Linear(hidden_dim, hidden_dim),
+                activation(),
+            ] for _ in range(num_layers - 2)]),
+            nn.Linear(hidden_dim, 21),
+        )
+
+        # init all bias to zero
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.zeros_(m.bias)
+
+    #  def forward(self, sampled_features, ray_directions):
+    #     # Aggregate features
+    #     sampled_features = sampled_features.mean(1)
+    #     x = sampled_features
+
+    #     N, M, C = x.shape
+    #     x = x.view(N * M, C)
+
+    #     x = self.net(x)
+    #     x = x.view(N, M, -1)
+    #     rgb = x[..., 1:]
+    #     sigma = x[..., 0:1]
+    #     if self.activation == "sigmoid":
+    #         # Original EG3D
+    #         rgb = torch.sigmoid(rgb) * (1 + 2 * 0.001) - 0.001
+    #     elif self.activation == "lrelu":
+    #         # StyleGAN2-style, use with toRGB
+    #         rgb = torch.nn.functional.leaky_relu(rgb, 0.2,
+    #                                              inplace=True) * math.sqrt(2)
+    #     return {'rgb': rgb, 'sigma': sigma}
+
+
+    def get_geometry_prediction(self, sampled_features, flexicubes_indices):
+        _N, n_planes, _M, _C = sampled_features.shape
+
+        sdf = self.net_sdf(sampled_features.mean(1)) # for compat issue
+        sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
+        deformation = self.net_deformation(sampled_features)
+
+        grid_features = torch.index_select(input=sampled_features, index=flexicubes_indices.reshape(-1), dim=1)
+        grid_features = grid_features.reshape(
+            sampled_features.shape[0], flexicubes_indices.shape[0], flexicubes_indices.shape[1] * sampled_features.shape[-1])
+        weight = self.net_weight(grid_features) * 0.1
+
+        return sdf, deformation, weight
+    
+    def get_texture_prediction(self, sampled_features):
+        _N, n_planes, _M, _C = sampled_features.shape
+        # sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
+        sampled_features = sampled_features.mean(1)
+
+        rgb = self.net_rgb(sampled_features) # sigmoid feat by default
+        rgb = torch.sigmoid(rgb)*(1 + 2*0.001) - 0.001  # Uses sigmoid clamping from MipNeRF
+
+        return rgb
+
+
+class LRMOSGDecoder(nn.Module):
+    """
+    Triplane decoder that gives RGB and sigma values from sampled features.
+    Using ReLU here instead of Softplus in the original implementation.
+    
+    Reference:
+    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112
+    """
+    def __init__(self, n_features: int,
+                 hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU):
+        super().__init__()
+        self.decoder_output_dim = 3
+        self.net = nn.Sequential(
+            nn.Linear(3 * n_features, hidden_dim),
+            activation(),
+            *itertools.chain(*[[
+                nn.Linear(hidden_dim, hidden_dim),
+                activation(),
+            ] for _ in range(num_layers - 2)]),
+            nn.Linear(hidden_dim, 1 + self.decoder_output_dim),
+        )
+        # init all bias to zero
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.zeros_(m.bias)
+
+    def forward(self, sampled_features, ray_directions):
+        # Aggregate features by mean
+        # sampled_features = sampled_features.mean(1)
+        # Aggregate features by concatenation
+        _N, n_planes, _M, _C = sampled_features.shape
+        sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
+        x = sampled_features
+
+        N, M, C = x.shape
+        x = x.contiguous().view(N*M, C)
+
+        x = self.net(x)
+        x = x.view(N, M, -1)
+        rgb = torch.sigmoid(x[..., 1:])*(1 + 2*0.001) - 0.001  # Uses sigmoid clamping from MipNeRF
+        sigma = x[..., 0:1]
+
+        return {'rgb': rgb, 'sigma': sigma}
+
+
+class Triplane(torch.nn.Module):
+
+    def __init__(
+        self,
+        c_dim=25,  # Conditioning label (C) dimensionality.
+        img_resolution=128,  # Output resolution.
+        img_channels=3,  # Number of output color channels.
+        out_chans=96,
+        triplane_size=224,
+        rendering_kwargs={},
+        decoder_in_chans=32,
+        decoder_output_dim=32,
+        sr_num_fp16_res=0,
+        sr_kwargs={},
+        create_triplane=False, # for overfitting single instance study
+        bcg_synthesis_kwargs={},
+        lrm_decoder=False,
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution  # TODO
+        self.img_channels = img_channels
+        self.triplane_size = triplane_size
+
+        self.decoder_in_chans = decoder_in_chans
+        self.out_chans = out_chans
+
+        self.renderer = ImportanceRenderer()
+
+        if 'PatchRaySampler' in rendering_kwargs:
+            self.ray_sampler = PatchRaySampler()
+        else:
+            self.ray_sampler = RaySampler()
+
+        if lrm_decoder:
+            self.decoder = LRMOSGDecoder(
+                decoder_in_chans,)
+        else:
+            self.decoder = OSGDecoder(
+                decoder_in_chans,
+                {
+                    'decoder_lr_mul': rendering_kwargs.get('decoder_lr_mul', 1),
+                    # 'decoder_output_dim': 32
+                    'decoder_output_dim': decoder_output_dim
+                })
+
+        self.neural_rendering_resolution = img_resolution  # TODO
+        # self.neural_rendering_resolution = 128  # TODO
+        self.rendering_kwargs = rendering_kwargs
+        self.create_triplane = create_triplane
+        if create_triplane:
+            self.planes = nn.Parameter(torch.randn(1, out_chans, 256, 256))
+
+        if bool(sr_kwargs):  # check whether empty
+            assert decoder_in_chans == decoder_output_dim, 'tradition'
+            if rendering_kwargs['superresolution_module'] in [
+                    'torch_utils.components.PixelUnshuffleUpsample',
+                    'torch_utils.components.NearestConvSR',
+                    'torch_utils.components.NearestConvSR_Residual'
+            ]:
+                self.superresolution = dnnlib.util.construct_class_by_name(
+                    class_name=rendering_kwargs['superresolution_module'],
+                    # * for PixelUnshuffleUpsample
+                    sr_ratio=2,  # 2x SR, 128 -> 256
+                    output_dim=decoder_output_dim,
+                    num_out_ch=3,
+                )
+            else:
+                self.superresolution = dnnlib.util.construct_class_by_name(
+                    class_name=rendering_kwargs['superresolution_module'],
+                    # * for stylegan upsample
+                    channels=decoder_output_dim,
+                    img_resolution=img_resolution,
+                    sr_num_fp16_res=sr_num_fp16_res,
+                    sr_antialias=rendering_kwargs['sr_antialias'],
+                    **sr_kwargs)
+        else:
+            self.superresolution = None
+
+        self.bcg_synthesis = None
+
+    # * pure reconstruction
+    def forward(
+            self,
+            planes=None,
+            # img,
+            c=None,
+            ws=None,
+            ray_origins=None,
+            ray_directions=None,
+            z_bcg=None,
+            neural_rendering_resolution=None,
+            update_emas=False,
+            cache_backbone=False,
+            use_cached_backbone=False,
+            return_meta=False,
+            return_raw_only=False,
+            sample_ray_only=False,
+            fg_bbox=None,
+            **synthesis_kwargs):
+
+        cam2world_matrix = c[:, :16].reshape(-1, 4, 4)
+        # cam2world_matrix = torch.eye(4, device=c.device).unsqueeze(0).repeat_interleave(c.shape[0], dim=0)
+        # c[:, :16] = cam2world_matrix.view(-1, 16)
+        intrinsics = c[:, 16:25].reshape(-1, 3, 3)
+
+        if neural_rendering_resolution is None:
+            neural_rendering_resolution = self.neural_rendering_resolution
+        else:
+            self.neural_rendering_resolution = neural_rendering_resolution
+
+        if ray_directions is None:  # when output video
+            H = W = self.neural_rendering_resolution
+            # Create a batch of rays for volume rendering
+            # ray_origins, ray_directions, ray_bboxes = self.ray_sampler(
+            #     cam2world_matrix, intrinsics, neural_rendering_resolution)
+
+            if sample_ray_only: # ! for sampling
+                ray_origins, ray_directions, ray_bboxes = self.ray_sampler(
+                    cam2world_matrix, intrinsics, 
+                    self.rendering_kwargs.get( 'patch_rendering_resolution' ),
+                    self.neural_rendering_resolution, fg_bbox)
+
+                # for patch supervision
+                ret_dict = {
+                    'ray_origins': ray_origins,
+                    'ray_directions': ray_directions,
+                    'ray_bboxes': ray_bboxes,
+                }
+
+                return ret_dict
+
+            else: # ! for rendering
+                ray_origins, ray_directions, _ = self.ray_sampler(
+                    cam2world_matrix, intrinsics, self.neural_rendering_resolution,
+                    self.neural_rendering_resolution)
+
+        else:
+            assert ray_origins is not None
+            H = W = int(ray_directions.shape[1]**
+                        0.5)  # dynamically set patch resolution
+
+        # ! match the batch size, if not returned
+        if planes is None:
+            assert self.planes is not None
+            planes = self.planes.repeat_interleave(c.shape[0], dim=0)
+        return_sampling_details_flag = self.rendering_kwargs.get(
+            'return_sampling_details_flag', False)
+
+        if return_sampling_details_flag:
+            return_meta = True
+
+        # Create triplanes by running StyleGAN backbone
+        N, M, _ = ray_origins.shape
+
+        # Reshape output into three 32-channel planes
+        if planes.shape[1] == 3 * 2 * self.decoder_in_chans:
+            # if isinstance(planes, tuple):
+            #     N *= 2
+            triplane_bg = True
+            # planes = torch.cat(planes, 0) # inference in parallel
+            # ray_origins = ray_origins.repeat(2,1,1)
+            # ray_directions = ray_directions.repeat(2,1,1)
+
+        else:
+            triplane_bg = False
+
+        # assert not triplane_bg
+
+        # ! hard coded, will fix later
+        # if planes.shape[1] == 3 * self.decoder_in_chans:
+        # else:
+
+        # planes = planes.view(len(planes), 3, self.decoder_in_chans,
+        planes = planes.reshape(
+            len(planes),
+            3,
+            -1,  # ! support background plane
+            planes.shape[-2],
+            planes.shape[-1])  # BS 96 256 256
+
+        # Perform volume rendering
+        rendering_details = self.renderer(planes,
+                                          self.decoder,
+                                          ray_origins,
+                                          ray_directions,
+                                          self.rendering_kwargs,
+                                          return_meta=return_meta)
+
+        feature_samples, depth_samples, weights_samples = (
+            rendering_details[k]
+            for k in ['feature_samples', 'depth_samples', 'weights_samples'])
+
+        if return_sampling_details_flag:
+            shape_synthesized = rendering_details['shape_synthesized']
+        else:
+            shape_synthesized = None
+
+        # Reshape into 'raw' neural-rendered image
+        feature_image = feature_samples.permute(0, 2, 1).reshape(
+            N, feature_samples.shape[-1], H,
+            W).contiguous()  # B 32 H W, in [-1,1]
+        depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W)
+        weights_samples = weights_samples.permute(0, 2, 1).reshape(N, 1, H, W)
+
+        # Generate Background
+        # if self.bcg_synthesis:
+
+        #     # bg composition
+        #     # if self.decoder.activation == "sigmoid":
+        #     #     feature_image = feature_image * 2 - 1 # Scale to (-1, 1), taken from ray marcher
+
+        #     assert isinstance(
+        #         z_bcg, torch.Tensor
+        #     )  # 512 latents after reparmaterization, reuse the name
+        #     # ws_bcg = ws[:,:self.bcg_synthesis.num_ws] if ws_bcg is None else ws_bcg[:,:self.bcg_synthesis.num_ws]
+
+        #     with torch.autocast(device_type='cuda',
+        #                         dtype=torch.float16,
+        #                         enabled=False):
+
+        #         ws_bcg = self.bcg_mapping(z_bcg, c=None)  # reuse the name
+        #         if ws_bcg.size(1) < self.bcg_synthesis.num_ws:
+        #             ws_bcg = torch.cat([
+        #                 ws_bcg, ws_bcg[:, -1:].repeat(
+        #                     1, self.bcg_synthesis.num_ws - ws_bcg.size(1), 1)
+        #             ], 1)
+
+        #         bcg_image = self.bcg_synthesis(ws_bcg,
+        #                                        update_emas=update_emas,
+        #                                        **synthesis_kwargs)
+        #     bcg_image = torch.nn.functional.interpolate(
+        #         bcg_image,
+        #         size=feature_image.shape[2:],
+        #         mode='bilinear',
+        #         align_corners=False,
+        #         antialias=self.rendering_kwargs['sr_antialias'])
+        #     feature_image = feature_image + (1 - weights_samples) * bcg_image
+
+        #     # Generate Raw image
+        #     assert self.torgb
+        #     rgb_image = self.torgb(feature_image,
+        #                            ws_bcg[:, -1],
+        #                            fused_modconv=False)
+        #     rgb_image = rgb_image.to(dtype=torch.float32,
+        #                              memory_format=torch.contiguous_format)
+        #     # st()
+        # else:
+
+        mask_image = weights_samples * (1 + 2 * 0.001) - 0.001
+        if triplane_bg:
+            # true_bs = N // 2
+            # weights_samples = weights_samples[:true_bs]
+            # mask_image = mask_image[:true_bs]
+            # feature_image = feature_image[:true_bs] * mask_image + feature_image[true_bs:] * (1-mask_image) # the first is foreground
+            # depth_image = depth_image[:true_bs]
+
+            # ! composited colors
+            # rgb_final = (
+            #     1 - fg_ret_dict['weights']
+            # ) * bg_ret_dict['rgb_final'] + fg_ret_dict[
+            #     'feature_samples']  # https://github.com/SizheAn/PanoHead/blob/17ad915941c7e2703d5aa3eb5ff12eac47c90e53/training/triplane.py#L127C45-L127C64
+
+            # ret_dict.update({
+            #     'feature_samples': rgb_final,
+            # })
+            # st()
+            feature_image = (1 - mask_image) * rendering_details[
+                'bg_ret_dict']['rgb_final'] + feature_image
+
+        rgb_image = feature_image[:, :3]
+
+        # # Run superresolution to get final image
+        if self.superresolution is not None and not return_raw_only:
+            # assert ws is not None, 'feed in [cls] token here for SR module'
+
+            if ws is not None and ws.ndim == 2:
+                ws = ws.unsqueeze(
+                    1)[:, -1:, :]  # follow stylegan tradition, B, N, C
+
+            sr_image = self.superresolution(
+                rgb=rgb_image,
+                x=feature_image,
+                base_x=rgb_image,
+                ws=ws,  # only use the last layer
+                noise_mode=self.
+                rendering_kwargs['superresolution_noise_mode'],  # none
+                **{
+                    k: synthesis_kwargs[k]
+                    for k in synthesis_kwargs.keys() if k != 'noise_mode'
+                })
+        else:
+            # sr_image = rgb_image
+            sr_image = None
+
+        if shape_synthesized is not None:
+            shape_synthesized.update({
+                'image_depth': depth_image,
+            })  # for 3D loss easy computation, wrap all 3D in a single dict
+
+        ret_dict = {
+            'feature_image': feature_image,
+            # 'image_raw': feature_image[:, :3],
+            'image_raw': rgb_image,
+            'image_depth': depth_image,
+            'weights_samples': weights_samples,
+            # 'silhouette': mask_image,
+            # 'silhouette_normalized_3channel': (mask_image*2-1).repeat_interleave(3,1), # N 3 H W
+            'shape_synthesized': shape_synthesized,
+            "image_mask": mask_image,
+        }
+
+        if sr_image is not None:
+            ret_dict.update({
+                'image_sr': sr_image,
+            })
+
+        if return_meta:
+            ret_dict.update({
+                'feature_volume':
+                rendering_details['feature_volume'],
+                'all_coords':
+                rendering_details['all_coords'],
+                'weights':
+                rendering_details['weights'],
+            })
+
+        return ret_dict
+
+
+class Triplane_fg_bg_plane(Triplane):
+    # a separate background plane
+
+    def __init__(self,
+                 c_dim=25,
+                 img_resolution=128,
+                 img_channels=3,
+                 out_chans=96,
+                 triplane_size=224,
+                 rendering_kwargs={},
+                 decoder_in_chans=32,
+                 decoder_output_dim=32,
+                 sr_num_fp16_res=0,
+                 sr_kwargs={},
+                 bcg_synthesis_kwargs={}):
+        super().__init__(c_dim, img_resolution, img_channels, out_chans,
+                         triplane_size, rendering_kwargs, decoder_in_chans,
+                         decoder_output_dim, sr_num_fp16_res, sr_kwargs,
+                         bcg_synthesis_kwargs)
+
+        self.bcg_decoder = Decoder(
+            ch=64,  # half channel size
+            out_ch=32,
+            # ch_mult=(1, 2, 4),
+            ch_mult=(1, 2),  # use res=64 for now
+            num_res_blocks=2,
+            dropout=0.0,
+            attn_resolutions=(),
+            z_channels=4,
+            resolution=64,
+            in_channels=3,
+        )
+
+    # * pure reconstruction
+    def forward(
+            self,
+            planes,
+            bg_plane,
+            # img,
+            c,
+            ws=None,
+            z_bcg=None,
+            neural_rendering_resolution=None,
+            update_emas=False,
+            cache_backbone=False,
+            use_cached_backbone=False,
+            return_meta=False,
+            return_raw_only=False,
+            **synthesis_kwargs):
+
+        # ! match the batch size
+        if planes is None:
+            assert self.planes is not None
+            planes = self.planes.repeat_interleave(c.shape[0], dim=0)
+        return_sampling_details_flag = self.rendering_kwargs.get(
+            'return_sampling_details_flag', False)
+
+        if return_sampling_details_flag:
+            return_meta = True
+
+        cam2world_matrix = c[:, :16].reshape(-1, 4, 4)
+        # cam2world_matrix = torch.eye(4, device=c.device).unsqueeze(0).repeat_interleave(c.shape[0], dim=0)
+        # c[:, :16] = cam2world_matrix.view(-1, 16)
+        intrinsics = c[:, 16:25].reshape(-1, 3, 3)
+
+        if neural_rendering_resolution is None:
+            neural_rendering_resolution = self.neural_rendering_resolution
+        else:
+            self.neural_rendering_resolution = neural_rendering_resolution
+
+        H = W = self.neural_rendering_resolution
+        # Create a batch of rays for volume rendering
+        ray_origins, ray_directions, _ = self.ray_sampler(
+            cam2world_matrix, intrinsics, neural_rendering_resolution)
+
+        # Create triplanes by running StyleGAN backbone
+        N, M, _ = ray_origins.shape
+
+        # # Reshape output into three 32-channel planes
+        # if planes.shape[1] == 3 * 2 * self.decoder_in_chans:
+        #     # if isinstance(planes, tuple):
+        #     #     N *= 2
+        #     triplane_bg = True
+        #     # planes = torch.cat(planes, 0) # inference in parallel
+        #     # ray_origins = ray_origins.repeat(2,1,1)
+        #     # ray_directions = ray_directions.repeat(2,1,1)
+
+        # else:
+        #     triplane_bg = False
+
+        # assert not triplane_bg
+
+        planes = planes.view(
+            len(planes),
+            3,
+            -1,  # ! support background plane
+            planes.shape[-2],
+            planes.shape[-1])  # BS 96 256 256
+
+        # Perform volume rendering
+        rendering_details = self.renderer(planes,
+                                          self.decoder,
+                                          ray_origins,
+                                          ray_directions,
+                                          self.rendering_kwargs,
+                                          return_meta=return_meta)
+
+        feature_samples, depth_samples, weights_samples = (
+            rendering_details[k]
+            for k in ['feature_samples', 'depth_samples', 'weights_samples'])
+
+        if return_sampling_details_flag:
+            shape_synthesized = rendering_details['shape_synthesized']
+        else:
+            shape_synthesized = None
+
+        # Reshape into 'raw' neural-rendered image
+        feature_image = feature_samples.permute(0, 2, 1).reshape(
+            N, feature_samples.shape[-1], H,
+            W).contiguous()  # B 32 H W, in [-1,1]
+        depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W)
+        weights_samples = weights_samples.permute(0, 2, 1).reshape(N, 1, H, W)
+
+        bcg_image = self.bcg_decoder(bg_plane)
+        bcg_image = torch.nn.functional.interpolate(
+            bcg_image,
+            size=feature_image.shape[2:],
+            mode='bilinear',
+            align_corners=False,
+            antialias=self.rendering_kwargs['sr_antialias'])
+
+        mask_image = weights_samples * (1 + 2 * 0.001) - 0.001
+
+        # ! fuse fg/bg model output
+        feature_image = feature_image + (1 - weights_samples) * bcg_image
+
+        rgb_image = feature_image[:, :3]
+
+        # # Run superresolution to get final image
+        if self.superresolution is not None and not return_raw_only:
+            # assert ws is not None, 'feed in [cls] token here for SR module'
+
+            if ws is not None and ws.ndim == 2:
+                ws = ws.unsqueeze(
+                    1)[:, -1:, :]  # follow stylegan tradition, B, N, C
+
+            sr_image = self.superresolution(
+                rgb=rgb_image,
+                x=feature_image,
+                base_x=rgb_image,
+                ws=ws,  # only use the last layer
+                noise_mode=self.
+                rendering_kwargs['superresolution_noise_mode'],  # none
+                **{
+                    k: synthesis_kwargs[k]
+                    for k in synthesis_kwargs.keys() if k != 'noise_mode'
+                })
+        else:
+            # sr_image = rgb_image
+            sr_image = None
+
+        if shape_synthesized is not None:
+            shape_synthesized.update({
+                'image_depth': depth_image,
+            })  # for 3D loss easy computation, wrap all 3D in a single dict
+
+        ret_dict = {
+            'feature_image': feature_image,
+            # 'image_raw': feature_image[:, :3],
+            'image_raw': rgb_image,
+            'image_depth': depth_image,
+            'weights_samples': weights_samples,
+            # 'silhouette': mask_image,
+            # 'silhouette_normalized_3channel': (mask_image*2-1).repeat_interleave(3,1), # N 3 H W
+            'shape_synthesized': shape_synthesized,
+            "image_mask": mask_image,
+        }
+
+        if sr_image is not None:
+            ret_dict.update({
+                'image_sr': sr_image,
+            })
+
+        if return_meta:
+            ret_dict.update({
+                'feature_volume':
+                rendering_details['feature_volume'],
+                'all_coords':
+                rendering_details['all_coords'],
+                'weights':
+                rendering_details['weights'],
+            })
+
+        return ret_dict
+
+class TriplaneFlexiCube(Triplane):
+    def __init__(self, c_dim=25, img_resolution=128, img_channels=3, out_chans=96, triplane_size=224, rendering_kwargs={}, decoder_in_chans=32, decoder_output_dim=32, sr_num_fp16_res=0, sr_kwargs={}, 
+        create_triplane=False, # for overfitting single instance study
+        bcg_synthesis_kwargs={},
+        lrm_decoder=False,
+    ):
+        super().__init__(c_dim, img_resolution, img_channels, out_chans, triplane_size, rendering_kwargs, decoder_in_chans, decoder_output_dim, sr_num_fp16_res, sr_kwargs, bcg_synthesis_kwargs, lrm_decoder=lrm_decoder, create_triplane=create_triplane)
+        # https://github.com/TencentARC/InstantMesh/blob/0a64425c6d390afa40128132cec42cd5c6408bbf/src/models/renderer/synthesizer_mesh.py#L93
+
+    def get_geometry_prediction(self, planes, sample_coordinates, flexicubes_indices):
+        plane_axes = self.plane_axes.to(planes.device)
+        sampled_features = sample_from_planes(
+            plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=self.rendering_kwargs['box_warp'])
+
+        sdf, deformation, weight = self.decoder.get_geometry_prediction(sampled_features, flexicubes_indices)
+        return sdf, deformation, weight
+    
+    def get_texture_prediction(self, planes, sample_coordinates):
+        plane_axes = self.plane_axes.to(planes.device)
+        sampled_features = sample_from_planes(
+            plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=self.rendering_kwargs['box_warp'])
+
+        rgb = self.decoder.get_texture_prediction(sampled_features)
+        return rgb
\ No newline at end of file
diff --git a/nsr/volumetric_rendering/__init__.py b/nsr/volumetric_rendering/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..daba66567a95beabb103f7996198a9675ab20b4a
--- /dev/null
+++ b/nsr/volumetric_rendering/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+# empty
\ No newline at end of file
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diff --git a/nsr/volumetric_rendering/math_utils.py b/nsr/volumetric_rendering/math_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..81378a371d959e0c0b5886f00925651c0caa2124
--- /dev/null
+++ b/nsr/volumetric_rendering/math_utils.py
@@ -0,0 +1,137 @@
+# MIT License
+
+# Copyright (c) 2022 Petr Kellnhofer
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import torch
+from pdb import set_trace as st
+
+
+def transform_vectors(matrix: torch.Tensor,
+                      vectors4: torch.Tensor) -> torch.Tensor:
+    """
+    Left-multiplies MxM @ NxM. Returns NxM.
+    """
+    res = torch.matmul(vectors4, matrix.T)
+    return res
+
+
+def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:
+    """
+    Normalize vector lengths.
+    """
+    return vectors / (torch.norm(vectors, dim=-1, keepdim=True))
+
+
+def torch_dot(x: torch.Tensor, y: torch.Tensor):
+    """
+    Dot product of two tensors.
+    """
+    return (x * y).sum(-1)
+
+
+def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor,
+                       box_side_length):
+    """
+    Author: Petr Kellnhofer
+    Intersects rays with the [-1, 1] NDC volume.
+    Returns min and max distance of entry.
+    Returns -1 for no intersection.
+    https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection
+    """
+    o_shape = rays_o.shape
+    rays_o = rays_o.detach().reshape(-1, 3)
+    rays_d = rays_d.detach().reshape(-1, 3)
+
+    bb_min = [
+        -1 * (box_side_length / 2), -1 * (box_side_length / 2),
+        -1 * (box_side_length / 2)
+    ]
+    bb_max = [
+        1 * (box_side_length / 2), 1 * (box_side_length / 2),
+        1 * (box_side_length / 2)
+    ]
+    bounds = torch.tensor([bb_min, bb_max],
+                          dtype=rays_o.dtype,
+                          device=rays_o.device)
+    is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)
+
+    # Precompute inverse for stability.
+    invdir = 1 / rays_d
+    sign = (invdir < 0).long()
+
+    # Intersect with YZ plane.
+    tmin = (bounds.index_select(0, sign[..., 0])[..., 0] -
+            rays_o[..., 0]) * invdir[..., 0]
+    tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] -
+            rays_o[..., 0]) * invdir[..., 0]
+
+    # Intersect with XZ plane.
+    tymin = (bounds.index_select(0, sign[..., 1])[..., 1] -
+             rays_o[..., 1]) * invdir[..., 1]
+    tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] -
+             rays_o[..., 1]) * invdir[..., 1]
+
+    # Resolve parallel rays.
+    is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False
+
+    # Use the shortest intersection.
+    tmin = torch.max(tmin, tymin)
+    tmax = torch.min(tmax, tymax)
+
+    # Intersect with XY plane.
+    tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] -
+             rays_o[..., 2]) * invdir[..., 2]
+    tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] -
+             rays_o[..., 2]) * invdir[..., 2]
+
+    # Resolve parallel rays.
+    is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False
+
+    # Use the shortest intersection.
+    tmin = torch.max(tmin, tzmin)
+    tmax = torch.min(tmax, tzmax)
+
+    # Mark invalid.
+    tmin[torch.logical_not(is_valid)] = -1
+    tmax[torch.logical_not(is_valid)] = -2
+
+    return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)
+
+
+def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
+    """
+    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
+    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
+    """
+    # create a tensor of 'num' steps from 0 to 1
+    steps = torch.arange(num, dtype=torch.float32,
+                         device=start.device) / (num - 1)
+
+    # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
+    # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
+    #   "cannot statically infer the expected size of a list in this contex", hence the code below
+    for i in range(start.ndim):
+        steps = steps.unsqueeze(-1)
+
+    # the output starts at 'start' and increments until 'stop' in each dimension
+    out = start[None] + steps * (stop - start)[None]
+
+    return out
diff --git a/nsr/volumetric_rendering/ray_marcher.py b/nsr/volumetric_rendering/ray_marcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..62e4f0cf06a6e7d2256c810a9b83a58f6a2846ce
--- /dev/null
+++ b/nsr/volumetric_rendering/ray_marcher.py
@@ -0,0 +1,74 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""
+The ray marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths.
+Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!)
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pdb import set_trace as st
+
+
+class MipRayMarcher2(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def run_forward(self, colors, densities, depths, rendering_options):
+        deltas = depths[:, :, 1:] - depths[:, :, :-1]
+        colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2
+        densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2
+        depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2
+
+        if rendering_options['clamp_mode'] == 'softplus':
+            densities_mid = F.softplus(
+                densities_mid -
+                1)  # activation bias of -1 makes things initialize better
+        else:
+            assert False, "MipRayMarcher only supports `clamp_mode`=`softplus`!"
+
+        density_delta = densities_mid * deltas
+
+        alpha = 1 - torch.exp(-density_delta)
+
+        alpha_shifted = torch.cat(
+            [torch.ones_like(alpha[:, :, :1]), 1 - alpha + 1e-10], -2)
+        T = torch.cumprod(alpha_shifted, -2)  # transmittance
+        weights = alpha * T[:, :, :-1]
+        visibility = T[:, :,
+                       -1]  # bg lambda, https://github.com/Kai-46/nerfplusplus/blob/ebf2f3e75fd6c5dfc8c9d0b533800daaf17bd95f/ddp_model.py#L101
+        # st()
+
+        composite_rgb = torch.sum(weights * colors_mid, -2)
+        weight_total = weights.sum(2)
+        # composite_depth = torch.sum(weights * depths_mid, -2) / weight_total
+        composite_depth = torch.sum(
+            weights * depths_mid,
+            -2)  # shapenet white background, no need this.
+
+        # clip the composite to min/max range of depths
+        composite_depth = torch.nan_to_num(composite_depth, float('inf'))
+        composite_depth = torch.clamp(composite_depth, torch.min(depths),
+                                      torch.max(depths))
+
+        if rendering_options.get('white_back', True):
+            composite_rgb = composite_rgb + 1 - weight_total
+
+        composite_rgb = composite_rgb * 2 - 1  # Scale (0,1) to (-1, 1)
+
+        return composite_rgb, composite_depth, visibility, weights
+
+    def forward(self, colors, densities, depths, rendering_options):
+        composite_rgb, composite_depth, visibility, weights = self.run_forward(
+            colors, densities, depths, rendering_options)
+
+        return composite_rgb, composite_depth, visibility, weights
diff --git a/nsr/volumetric_rendering/ray_sampler.py b/nsr/volumetric_rendering/ray_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..56f057cc0b3790c10df4bba8c8a7a9557e166055
--- /dev/null
+++ b/nsr/volumetric_rendering/ray_sampler.py
@@ -0,0 +1,345 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""
+The ray sampler is a module that takes in camera matrices and resolution and batches of rays.
+Expects cam2world matrices that use the OpenCV camera coordinate system conventions.
+"""
+
+import torch
+from pdb import set_trace as st
+import random
+
+HUGE_NUMBER = 1e10
+TINY_NUMBER = 1e-6  # float32 only has 7 decimal digits precision
+
+
+######################################################################################
+# wrapper to simplify the use of nerfnet
+######################################################################################
+# https://github.com/Kai-46/nerfplusplus/blob/ebf2f3e75fd6c5dfc8c9d0b533800daaf17bd95f/ddp_model.py#L16
+def depth2pts_outside(ray_o, ray_d, depth):
+    '''
+    ray_o, ray_d: [..., 3]
+    depth: [...]; inverse of distance to sphere origin
+    '''
+    # note: d1 becomes negative if this mid point is behind camera
+    d1 = -torch.sum(ray_d * ray_o, dim=-1) / torch.sum(ray_d * ray_d, dim=-1)
+    p_mid = ray_o + d1.unsqueeze(-1) * ray_d
+    p_mid_norm = torch.norm(p_mid, dim=-1)
+    ray_d_cos = 1. / torch.norm(ray_d, dim=-1)
+    d2 = torch.sqrt(1. - p_mid_norm * p_mid_norm) * ray_d_cos
+    p_sphere = ray_o + (d1 + d2).unsqueeze(-1) * ray_d
+
+    rot_axis = torch.cross(ray_o, p_sphere, dim=-1)
+    rot_axis = rot_axis / torch.norm(rot_axis, dim=-1, keepdim=True)
+    phi = torch.asin(p_mid_norm)
+    theta = torch.asin(p_mid_norm * depth)  # depth is inside [0, 1]
+    rot_angle = (phi - theta).unsqueeze(-1)  # [..., 1]
+
+    # now rotate p_sphere
+    # Rodrigues formula: https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula
+    p_sphere_new = p_sphere * torch.cos(rot_angle) + \
+                   torch.cross(rot_axis, p_sphere, dim=-1) * torch.sin(rot_angle) + \
+                   rot_axis * torch.sum(rot_axis*p_sphere, dim=-1, keepdim=True) * (1.-torch.cos(rot_angle))
+    p_sphere_new = p_sphere_new / torch.norm(
+        p_sphere_new, dim=-1, keepdim=True)
+    pts = torch.cat((p_sphere_new, depth.unsqueeze(-1)), dim=-1)
+
+    # now calculate conventional depth
+    depth_real = 1. / (depth + TINY_NUMBER) * torch.cos(theta) * ray_d_cos + d1
+    return pts, depth_real
+
+
+class RaySampler(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
+
+    def create_patch_uv(self,
+                        patch_resolution,
+                        resolution,
+                        cam2world_matrix,
+                        fg_bbox=None):
+
+        def sample_patch_uv(fg_bbox=None):
+            assert patch_resolution <= resolution
+
+            def sample_patch_range():
+                patch_reolution_start = random.randint(
+                    0, resolution -
+                    patch_resolution)  # alias for randrange(start, stop+1)
+                # patch_reolution_end = patch_reolution_start + patch_resolution
+                return patch_reolution_start  # , patch_reolution_end
+
+            def sample_patch_range_oversample_boundary(range_start=None,
+                                                       range_end=None):
+                # left down corner undersampled
+                if range_start is None:
+                    # range_start = patch_resolution // 2
+                    range_start = patch_resolution
+                if range_end is None:
+                    # range_end = resolution + patch_resolution // 2
+                    range_end = resolution + patch_resolution
+
+                # oversample the boundary
+                patch_reolution_end = random.randint(
+                    range_start,
+                    range_end,
+                )
+
+                # clip range
+                if patch_reolution_end <= patch_resolution:
+                    patch_reolution_end = patch_resolution
+                elif patch_reolution_end > resolution:
+                    patch_reolution_end = resolution
+
+                # patch_reolution_end = patch_reolution_start + patch_resolution
+                return patch_reolution_end  # , patch_reolution_end
+
+            # h_start = sample_patch_range()
+            # assert fg_bbox is not None
+            if fg_bbox is not None and random.random(
+            ) > 0.025:  # only train foreground. Has 0.1 prob to sample/train background.
+                # if fg_bbox is not None: # only train foreground. Has 0.1 prob to sample/train background.
+                # only return one UV here
+                top_min, left_min = fg_bbox[:, :2].min(dim=0,
+                                                       keepdim=True)[0][0]
+                height_max, width_max = fg_bbox[:, 2:].max(dim=0,
+                                                           keepdim=True)[0][0]
+
+                left_boundary, right_boundary = patch_resolution // 2, resolution - patch_resolution // 2
+                h_mid = random.randint(
+                    min(max(top_min, left_boundary), right_boundary),
+                    max(min(height_max, right_boundary), left_boundary),
+                )
+
+                w_mid = random.randint(
+                    min(max(left_min, left_boundary), right_boundary),
+                    max(min(width_max, right_boundary), left_boundary),
+                )
+
+                h_end = h_mid + patch_resolution // 2
+                w_end = w_mid + patch_resolution // 2
+
+                # if top_min + patch_resolution < height_max:
+                #     h_end = sample_patch_range_oversample_boundary(
+                #         top_min + patch_resolution, height_max)
+                # else:
+                #     h_end = max(
+                #         height_max.to(torch.uint8).item(), patch_resolution)
+                # if left_min + patch_resolution < width_max:
+                #     w_end = sample_patch_range_oversample_boundary(
+                #         left_min + patch_resolution, width_max)
+                # else:
+                #     w_end = max(
+                #         width_max.to(torch.uint8).item(), patch_resolution)
+
+                h_start = h_end - patch_resolution
+                w_start = w_end - patch_resolution
+
+                try:
+                    assert h_start >= 0 and w_start >= 0
+                except:
+                    st()
+
+            else:
+                h_end = sample_patch_range_oversample_boundary()
+                h_start = h_end - patch_resolution
+                w_end = sample_patch_range_oversample_boundary()
+                w_start = w_end - patch_resolution
+
+                assert h_start >= 0 and w_start >= 0
+
+            uv = torch.stack(
+                torch.meshgrid(
+                    torch.arange(
+                        start=h_start,
+                        # end=h_start+patch_resolution,
+                        end=h_end,
+                        dtype=torch.float32,
+                        device=cam2world_matrix.device),
+                    torch.arange(
+                        start=w_start,
+                        #  end=w_start + patch_resolution,
+                        end=w_end,
+                        dtype=torch.float32,
+                        device=cam2world_matrix.device),
+                    indexing='ij')) * (1. / resolution) + (0.5 / resolution)
+
+            uv = uv.flip(0).reshape(2, -1).transpose(1, 0)  # ij -> xy
+
+            return uv, (h_start, w_start, patch_resolution, patch_resolution
+                        )  # top: int, left: int, height: int, width: int
+
+        all_uv = []
+        ray_bboxes = []
+        for _ in range(cam2world_matrix.shape[0]):
+            uv, bbox = sample_patch_uv(fg_bbox)
+            all_uv.append(uv)
+            ray_bboxes.append(bbox)
+
+        all_uv = torch.stack(all_uv, 0)  # B patch_res**2 2
+        # ray_bboxes = torch.stack(ray_bboxes, 0) # B patch_res**2 2
+
+        return all_uv, ray_bboxes
+
+    def create_uv(self, resolution, cam2world_matrix):
+
+        uv = torch.stack(
+            torch.meshgrid(torch.arange(resolution,
+                                        dtype=torch.float32,
+                                        device=cam2world_matrix.device),
+                           torch.arange(resolution,
+                                        dtype=torch.float32,
+                                        device=cam2world_matrix.device),
+                           indexing='ij')) * (1. / resolution) + (0.5 /
+                                                                  resolution)
+
+        uv = uv.flip(0).reshape(2, -1).transpose(1, 0)  # why
+        uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
+
+        return uv
+
+    def forward(self, cam2world_matrix, intrinsics, resolution, fg_mask=None):
+        """
+        Create batches of rays and return origins and directions.
+
+        cam2world_matrix: (N, 4, 4)
+        intrinsics: (N, 3, 3)
+        resolution: int
+
+        ray_origins: (N, M, 3)
+        ray_dirs: (N, M, 2)
+        """
+        N, M = cam2world_matrix.shape[0], resolution**2
+        cam_locs_world = cam2world_matrix[:, :3, 3]
+        fx = intrinsics[:, 0, 0]
+        fy = intrinsics[:, 1, 1]
+        cx = intrinsics[:, 0, 2]
+        cy = intrinsics[:, 1, 2]
+        sk = intrinsics[:, 0, 1]
+
+        # uv = torch.stack(
+        #     torch.meshgrid(torch.arange(resolution,
+        #                                 dtype=torch.float32,
+        #                                 device=cam2world_matrix.device),
+        #                    torch.arange(resolution,
+        #                                 dtype=torch.float32,
+        #                                 device=cam2world_matrix.device),
+        #                    indexing='ij')) * (1. / resolution) + (0.5 /
+        #                                                           resolution)
+        # uv = uv.flip(0).reshape(2, -1).transpose(1, 0)  # why
+        # uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
+        uv = self.create_uv(
+            resolution,
+            cam2world_matrix,
+        )
+
+        x_cam = uv[:, :, 0].view(N, -1)
+        y_cam = uv[:, :, 1].view(N, -1)  # [0,1] range
+        z_cam = torch.ones((N, M), device=cam2world_matrix.device)
+
+        # basically torch.inverse(intrinsics)
+        x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1) *
+                  sk.unsqueeze(-1) / fy.unsqueeze(-1) - sk.unsqueeze(-1) *
+                  y_cam / fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
+        y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
+
+        cam_rel_points = torch.stack(
+            (x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
+
+        # st()
+
+        world_rel_points = torch.bmm(cam2world_matrix,
+                                     cam_rel_points.permute(0, 2, 1)).permute(
+                                         0, 2, 1)[:, :, :3]
+
+        ray_dirs = world_rel_points - cam_locs_world[:, None, :]
+        ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2)
+
+        ray_origins = cam_locs_world.unsqueeze(1).repeat(
+            1, ray_dirs.shape[1], 1)
+
+        return ray_origins, ray_dirs, None
+
+
+class PatchRaySampler(RaySampler):
+
+    def forward(self,
+                cam2world_matrix,
+                intrinsics,
+                patch_resolution,
+                resolution,
+                fg_bbox=None):
+        """
+        Create batches of rays and return origins and directions.
+
+        cam2world_matrix: (N, 4, 4)
+        intrinsics: (N, 3, 3)
+        resolution: int
+
+        ray_origins: (N, M, 3)
+        ray_dirs: (N, M, 2)
+        """
+        N, M = cam2world_matrix.shape[0], patch_resolution**2
+        cam_locs_world = cam2world_matrix[:, :3, 3]
+        fx = intrinsics[:, 0, 0]
+        fy = intrinsics[:, 1, 1]
+        cx = intrinsics[:, 0, 2]
+        cy = intrinsics[:, 1, 2]
+        sk = intrinsics[:, 0, 1]
+
+        # uv = self.create_uv(resolution, cam2world_matrix)
+
+        # all_uv, ray_bboxes = self.create_patch_uv(
+        all_uv_list = []
+        ray_bboxes = []
+        for idx in range(N):
+            uv, bboxes = self.create_patch_uv(
+                patch_resolution, resolution, cam2world_matrix[idx:idx + 1],
+                fg_bbox[idx:idx + 1]
+                if fg_bbox is not None else None)  # for debugging, hard coded
+            all_uv_list.append(
+                uv
+                # cam2world_matrix[idx:idx+1], )[0]  # for debugging, hard coded
+            )
+            ray_bboxes.extend(bboxes)
+        all_uv = torch.cat(all_uv_list, 0)
+        # ray_bboxes = torch.cat(ray_bboxes_list, 0)
+        # all_uv, _ = self.create_patch_uv(
+        #     patch_resolution, resolution,
+        #     cam2world_matrix, fg_bbox)  # for debugging, hard coded
+        # st()
+
+        x_cam = all_uv[:, :, 0].view(N, -1)
+        y_cam = all_uv[:, :, 1].view(N, -1)  # [0,1] range
+        z_cam = torch.ones((N, M), device=cam2world_matrix.device)
+
+        # basically torch.inverse(intrinsics)
+        x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1) *
+                  sk.unsqueeze(-1) / fy.unsqueeze(-1) - sk.unsqueeze(-1) *
+                  y_cam / fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
+        y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
+
+        cam_rel_points = torch.stack(
+            (x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
+
+        world_rel_points = torch.bmm(cam2world_matrix,
+                                     cam_rel_points.permute(0, 2, 1)).permute(
+                                         0, 2, 1)[:, :, :3]
+
+        ray_dirs = world_rel_points - cam_locs_world[:, None, :]
+        ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2)
+
+        ray_origins = cam_locs_world.unsqueeze(1).repeat(
+            1, ray_dirs.shape[1], 1)
+
+        return ray_origins, ray_dirs, ray_bboxes
diff --git a/nsr/volumetric_rendering/renderer.py b/nsr/volumetric_rendering/renderer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cee75f5c034af01dcc916c2681f4f7a06cd43b6f
--- /dev/null
+++ b/nsr/volumetric_rendering/renderer.py
@@ -0,0 +1,637 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""
+The renderer is a module that takes in rays, decides where to sample along each
+ray, and computes pixel colors using the volume rendering equation.
+"""
+
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+
+from .ray_marcher import MipRayMarcher2
+from . import math_utils
+from pdb import set_trace as st
+from .ray_sampler import depth2pts_outside, HUGE_NUMBER, TINY_NUMBER
+
+
+def generate_planes():
+    """
+    Defines planes by the three vectors that form the "axes" of the
+    plane. Should work with arbitrary number of planes and planes of
+    arbitrary orientation.
+    """
+    return torch.tensor(
+        [[[1, 0, 0], [0, 1, 0], [0, 0, 1]], [[1, 0, 0], [0, 0, 1], [0, 1, 0]],
+         [[0, 0, 1], [1, 0, 0], [0, 1, 0]]],
+        dtype=torch.float32)
+
+
+# def project_onto_planes(planes, coordinates):
+#     """
+#     Does a projection of a 3D point onto a batch of 2D planes,
+#     returning 2D plane coordinates.
+
+#     Takes plane axes of shape n_planes, 3, 3
+#     # Takes coordinates of shape N, M, 3
+#     # returns projections of shape N*n_planes, M, 2
+#     """
+#     N, M, C = coordinates.shape
+#     n_planes, _, _ = planes.shape
+#     coordinates = coordinates.unsqueeze(1).expand(-1, n_planes, -1, -1).reshape(N*n_planes, M, 3)
+#     inv_planes = torch.linalg.inv(planes).unsqueeze(0).expand(N, -1, -1, -1).reshape(N*n_planes, 3, 3)
+#     projections = torch.bmm(coordinates, inv_planes)
+#     return projections[..., :2]
+
+
+def project_onto_planes(planes, coordinates):
+    """
+    Does a projection of a 3D point onto a batch of 2D planes,
+    returning 2D plane coordinates.
+
+    Takes plane axes of shape n_planes, 3, 3
+    # Takes coordinates of shape N, M, 3
+    # returns projections of shape N*n_planes, M, 2
+    """
+
+    # # ORIGINAL
+    # N, M, C = coordinates.shape
+    # xy_coords = coordinates[..., [0, 1]]
+    # xz_coords = coordinates[..., [0, 2]]
+    # zx_coords = coordinates[..., [2, 0]]
+    # return torch.stack([xy_coords, xz_coords, zx_coords], dim=1).reshape(N*3, M, 2)
+
+    # FIXED
+    N, M, _ = coordinates.shape
+    xy_coords = coordinates[..., [0, 1]]
+    yz_coords = coordinates[..., [1, 2]]
+    zx_coords = coordinates[..., [2, 0]]
+    return torch.stack([xy_coords, yz_coords, zx_coords],
+                       dim=1).reshape(N * 3, M, 2)
+
+
+def sample_from_planes(plane_axes,
+                       plane_features,
+                       coordinates,
+                       mode='bilinear',
+                       padding_mode='zeros',
+                       box_warp=None):
+    assert padding_mode == 'zeros'
+    N, n_planes, C, H, W = plane_features.shape
+    _, M, _ = coordinates.shape
+    # st()
+    plane_features = plane_features.view(N * n_planes, C, H, W)
+    # plane_features = plane_features.reshape(N * n_planes, C, H, W)
+
+    coordinates = (2 / box_warp) * coordinates  # TODO: add specific box bounds
+
+    projected_coordinates = project_onto_planes(plane_axes,
+                                                coordinates).unsqueeze(1)
+    output_features = torch.nn.functional.grid_sample(
+        plane_features,
+        projected_coordinates.float(),
+        mode=mode,
+        padding_mode=padding_mode,
+        align_corners=False).permute(0, 3, 2, 1).reshape(N, n_planes, M, C)
+    return output_features
+
+
+def sample_from_3dgrid(grid, coordinates):
+    """
+    Expects coordinates in shape (batch_size, num_points_per_batch, 3)
+    Expects grid in shape (1, channels, H, W, D)
+    (Also works if grid has batch size)
+    Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels)
+    """
+    batch_size, n_coords, n_dims = coordinates.shape
+    sampled_features = torch.nn.functional.grid_sample(
+        grid.expand(batch_size, -1, -1, -1, -1),
+        coordinates.reshape(batch_size, 1, 1, -1, n_dims),
+        mode='bilinear',
+        padding_mode='zeros',
+        align_corners=False)
+    N, C, H, W, D = sampled_features.shape
+    sampled_features = sampled_features.permute(0, 4, 3, 2,
+                                                1).reshape(N, H * W * D, C)
+    return sampled_features
+
+
+class ImportanceRenderer(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ray_marcher = MipRayMarcher2()
+        self.plane_axes = generate_planes()
+
+    def forward(self,
+                planes,
+                decoder,
+                ray_origins,
+                ray_directions,
+                rendering_options,
+                return_meta=False):
+        # return_sampling_details_flag=False):
+        self.plane_axes = self.plane_axes.to(ray_origins.device)
+        # if rendering_options.get('return_sampling_details_flag', None) is not None:
+        shape_synthesized = {}
+
+        if rendering_options['ray_start'] == rendering_options[
+                'ray_end'] == 'auto':
+            ray_start, ray_end = math_utils.get_ray_limits_box(
+                ray_origins,
+                ray_directions,
+                box_side_length=rendering_options['box_warp'])
+            is_ray_valid = ray_end > ray_start
+            # st()
+            if torch.any(is_ray_valid).item():
+                ray_start[~is_ray_valid] = ray_start[is_ray_valid].min()
+                ray_end[~is_ray_valid] = ray_start[is_ray_valid].max()
+            depths_coarse = self.sample_stratified(
+                ray_origins, ray_start, ray_end,
+                rendering_options['depth_resolution'],
+                rendering_options['disparity_space_sampling'])
+        else:
+            # Create stratified depth samples
+            depths_coarse = self.sample_stratified(
+                ray_origins, rendering_options['ray_start'],
+                rendering_options['ray_end'],
+                rendering_options['depth_resolution'],
+                rendering_options['disparity_space_sampling'])
+
+        batch_size, num_rays, samples_per_ray, _ = depths_coarse.shape
+
+        # Coarse Pass
+        sample_coordinates = (
+            ray_origins.unsqueeze(-2) +
+            depths_coarse * ray_directions.unsqueeze(-2)).reshape(
+                batch_size, -1, 3)
+        # st() # np.save('sample_coordinates.npy', sample_coordinates.detach().cpu().numpy())
+        sample_directions = ray_directions.unsqueeze(-2).expand(
+            -1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3)
+
+        colors_coarse, densities_coarse = self.run_model(
+            planes, decoder, sample_coordinates, sample_directions,
+            rendering_options, batch_size, num_rays, samples_per_ray)
+
+        colors_coarse = colors_coarse.reshape(batch_size, num_rays,
+                                              samples_per_ray,
+                                              colors_coarse.shape[-1])
+        densities_coarse = densities_coarse.reshape(batch_size, num_rays,
+                                                    samples_per_ray, 1)
+
+        if rendering_options.get('return_sampling_details_flag', False):
+            shape_synthesized.update({
+                # 'coarse_coords': sample_coordinates.detach().clone(),
+                # 'coarse_densities': densities_coarse.detach()
+                'coarse_coords':
+                sample_coordinates.reshape(batch_size, num_rays,
+                                           samples_per_ray, 3),
+                'coarse_densities':
+                densities_coarse
+            })
+
+        # Fine Pass
+        N_importance = rendering_options['depth_resolution_importance']
+        if N_importance > 0:
+            _, _, _, weights = self.ray_marcher(colors_coarse,
+                                                densities_coarse,
+                                                depths_coarse,
+                                                rendering_options)
+
+            depths_fine = self.sample_importance(depths_coarse, weights,
+                                                 N_importance)
+
+            sample_directions = ray_directions.unsqueeze(-2).expand(
+                -1, -1, N_importance, -1).reshape(batch_size, -1, 3)
+            sample_coordinates = (
+                ray_origins.unsqueeze(-2) +
+                depths_fine * ray_directions.unsqueeze(-2)).reshape(
+                    batch_size, -1, 3)
+
+            colors_fine, densities_fine = self.run_model(
+                planes, decoder, sample_coordinates, sample_directions,
+                rendering_options, batch_size, num_rays, N_importance)
+            # colors_fine = out['rgb']
+            # densities_fine = out['sigma']
+            colors_fine = colors_fine.reshape(batch_size, num_rays,
+                                              N_importance,
+                                              colors_fine.shape[-1])
+            densities_fine = densities_fine.reshape(batch_size, num_rays,
+                                                    N_importance, 1)
+            if rendering_options.get('return_sampling_details_flag', False):
+                shape_synthesized.update({
+                    # 'fine_coords': sample_coordinates.detach(),
+                    # 'fine_densities': densities_fine.detach()
+                    'fine_coords': sample_coordinates,
+                    # 'fine_coords': sample_coordinates.reshape(batch_size, num_rays, N_importance, 3),
+                    'fine_densities': densities_fine,
+                })
+
+            all_depths, all_colors, all_densities, indices = self.unify_samples(
+                depths_coarse, colors_coarse, densities_coarse, depths_fine,
+                colors_fine, densities_fine)
+
+            # Aggregate
+            rgb_final, depth_final, visibility, weights = self.ray_marcher(
+                all_colors, all_densities, all_depths, rendering_options)
+
+        else:
+            rgb_final, depth_final, visibility, weights = self.ray_marcher(
+                colors_coarse, densities_coarse, depths_coarse,
+                rendering_options)
+
+        if rendering_options.get('return_surface', False):
+            weight_total = weights.sum(2)
+
+            all_coords = torch.cat([
+                shape_synthesized['coarse_coords'],
+                shape_synthesized['fine_coords']
+            ],
+                                   dim=-2)  # B 4096 48+48 3
+            all_coords = torch.gather(all_coords, -2,
+                                      indices.expand(-1, -1, -1, 3))
+
+            composite_surface = torch.sum(weights * all_coords,
+                                          -2) / weight_total
+
+            # clip the composite to min/max range of depths
+            composite_surface = torch.nan_to_num(composite_surface,
+                                                 float('inf'))
+            composite_surface = torch.clamp(composite_surface,
+                                            torch.min(all_coords),
+                                            torch.max(all_coords))
+            shape_synthesized['surface_coords'] = composite_surface
+
+        shape_synthesized.update({
+            # 'depth': depth_final.detach()
+            'depth': depth_final
+        })
+
+        ret_dict = {
+            'feature_samples': rgb_final,
+            'depth_samples': depth_final,
+            'weights_samples': weights.sum(2),
+            'shape_synthesized': shape_synthesized,
+            'visibility': visibility  # T[..., -1]
+        }
+
+        if return_meta:  # for pifu
+            all_coords = torch.cat([
+                shape_synthesized['coarse_coords'],
+                shape_synthesized['fine_coords'].reshape(
+                    batch_size, num_rays, N_importance, 3)
+            ],
+                                   dim=-2)
+            # 'fine_coords': sample_coordinates,
+            all_coords = torch.gather(all_coords, -2,
+                                      indices.expand(-1, -1, -1, 3))
+
+            ret_dict.update({
+                'all_coords': all_coords,
+                'feature_volume': all_colors,
+                'weights': weights
+            })
+
+        if rendering_options.get('return_sampling_details_flag', False):
+            ret_dict.update({'shape_synthesized': shape_synthesized})
+            # return rgb_final, depth_final, weights.sum(2), shape_synthesized # rgb_final, B, 4096, 32
+
+        # return rgb_final, depth_final, weights.sum(2)
+        return ret_dict
+
+    # old run_model
+    def _run_model(self, planes, decoder, sample_coordinates,
+                   sample_directions, options):
+        sampled_features = sample_from_planes(self.plane_axes,
+                                              planes,
+                                              sample_coordinates,
+                                              padding_mode='zeros',
+                                              box_warp=options['box_warp'])
+
+        out = decoder(sampled_features, sample_directions)
+        if options.get('density_noise', 0) > 0:
+            out['sigma'] += torch.randn_like(
+                out['sigma']) * options['density_noise']
+        return out
+
+    def run_model(self, planes, decoder, sample_coordinates, sample_directions,
+                  rendering_options, batch_size, num_rays, samples_per_ray):
+        """ a compat wrapper for Objaverse (bbox-sampling) and FFHQ/Shapenet-based rendering (ray-start/end sampling).
+        
+            returns color and density
+        """
+
+        if rendering_options.get('filter_out_of_bbox', False):
+            # Coarse Pass
+            colors, densities = self._forward_pass(
+                # depths=depths_coarse,
+                # ray_directions=ray_directions,
+                # ray_origins=ray_origins,
+                sample_coordinates,
+                sample_directions,
+                planes=planes,
+                decoder=decoder,
+                rendering_options=rendering_options,
+                batch_size=batch_size,
+                num_rays=num_rays,
+                samples_per_ray=samples_per_ray,
+            )
+        else:
+            out = self._run_model(planes, decoder, sample_coordinates,
+                                  sample_directions, rendering_options)
+            colors = out['rgb']
+            densities = out['sigma']
+
+        return colors, densities
+
+    def _forward_pass(
+            self,
+            sample_coordinates,
+            sample_directions,
+            #   depths: torch.Tensor,
+            #   ray_directions: torch.Tensor,
+            #   ray_origins: torch.Tensor,
+            planes: torch.Tensor,
+            decoder: nn.Module,
+            rendering_options: dict,
+            batch_size,
+            num_rays,
+            samples_per_ray):
+        """
+        Additional filtering is applied to filter out-of-box samples.
+        Modifications made by Zexin He.
+        """
+
+        # context related variables
+        # batch_size, num_rays, samples_per_ray, _ = depths.shape
+        device = sample_coordinates.device
+
+        # define sample points with depths
+        # sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3)
+        # sample_coordinates = (ray_origins.unsqueeze(-2) + depths * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3)
+
+        # filter out-of-box samples
+        mask_inbox = \
+            (rendering_options['sampler_bbox_min'] <= sample_coordinates) & \
+                (sample_coordinates <= rendering_options['sampler_bbox_max'])
+        mask_inbox = mask_inbox.all(-1) # np.save('box.npy', mask_inbox.detach().cpu().numpy())
+
+        # forward model according to all samples
+        _out = self._run_model(planes, decoder, sample_coordinates,
+                               sample_directions, rendering_options)
+
+        # set out-of-box samples to zeros(rgb) & -inf(sigma)
+        SAFE_GUARD = 3
+        DATA_TYPE = _out['sigma'].dtype
+        colors_pass = torch.zeros(batch_size,
+                                  num_rays * samples_per_ray,
+                                #   3,
+                                  decoder.decoder_output_dim,
+                                  device=device,
+                                  dtype=DATA_TYPE)
+        densities_pass = torch.nan_to_num(
+            torch.full((batch_size, num_rays * samples_per_ray, 1),
+                       -float('inf'),
+                       device=device,
+                       dtype=DATA_TYPE)) / SAFE_GUARD
+        colors_pass[mask_inbox], densities_pass[mask_inbox] = _out['rgb'][
+            mask_inbox], _out['sigma'][mask_inbox]
+
+        # reshape back
+        # colors_pass = colors_pass.reshape(batch_size, num_rays, samples_per_ray, colors_pass.shape[-1])
+        # densities_pass = densities_pass.reshape(batch_size, num_rays, samples_per_ray, densities_pass.shape[-1])
+
+        return colors_pass, densities_pass
+
+    def sort_samples(self, all_depths, all_colors, all_densities):
+        _, indices = torch.sort(all_depths, dim=-2)
+        all_depths = torch.gather(all_depths, -2, indices)
+        all_colors = torch.gather(
+            all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
+        all_densities = torch.gather(all_densities, -2,
+                                     indices.expand(-1, -1, -1, 1))
+        return all_depths, all_colors, all_densities
+
+    def unify_samples(self, depths1, colors1, densities1, depths2, colors2,
+                      densities2):
+        all_depths = torch.cat([depths1, depths2], dim=-2)
+        all_colors = torch.cat([colors1, colors2], dim=-2)
+        all_densities = torch.cat([densities1, densities2], dim=-2)
+
+        _, indices = torch.sort(all_depths, dim=-2)
+        all_depths = torch.gather(all_depths, -2, indices)
+        all_colors = torch.gather(
+            all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
+        all_densities = torch.gather(all_densities, -2,
+                                     indices.expand(-1, -1, -1, 1))
+
+        return all_depths, all_colors, all_densities, indices
+
+    def sample_stratified(self,
+                          ray_origins,
+                          ray_start,
+                          ray_end,
+                          depth_resolution,
+                          disparity_space_sampling=False):
+        """
+        Return depths of approximately uniformly spaced samples along rays.
+        """
+        N, M, _ = ray_origins.shape
+        if disparity_space_sampling:
+            depths_coarse = torch.linspace(0,
+                                           1,
+                                           depth_resolution,
+                                           device=ray_origins.device).reshape(
+                                               1, 1, depth_resolution,
+                                               1).repeat(N, M, 1, 1)
+            depth_delta = 1 / (depth_resolution - 1)
+            depths_coarse += torch.rand_like(depths_coarse) * depth_delta
+            depths_coarse = 1. / (1. / ray_start * (1. - depths_coarse) +
+                                  1. / ray_end * depths_coarse)
+        else:
+            if type(ray_start) == torch.Tensor:
+                depths_coarse = math_utils.linspace(ray_start, ray_end,
+                                                    depth_resolution).permute(
+                                                        1, 2, 0, 3)
+                depth_delta = (ray_end - ray_start) / (depth_resolution - 1)
+                depths_coarse += torch.rand_like(depths_coarse) * depth_delta[
+                    ..., None]
+            else:
+                depths_coarse = torch.linspace(
+                    ray_start,
+                    ray_end,
+                    depth_resolution,
+                    device=ray_origins.device).reshape(1, 1, depth_resolution,
+                                                       1).repeat(N, M, 1, 1)
+                depth_delta = (ray_end - ray_start) / (depth_resolution - 1)
+                depths_coarse += torch.rand_like(depths_coarse) * depth_delta
+                # print("ignore normal noise!!! for debugging")
+
+        return depths_coarse
+
+    def sample_importance(self, z_vals, weights, N_importance):
+        """
+        Return depths of importance sampled points along rays. See NeRF importance sampling for more.
+        """
+        with torch.no_grad():
+            batch_size, num_rays, samples_per_ray, _ = z_vals.shape
+
+            z_vals = z_vals.reshape(batch_size * num_rays, samples_per_ray)
+            weights = weights.reshape(
+                batch_size * num_rays,
+                -1)  # -1 to account for loss of 1 sample in MipRayMarcher
+
+            # smooth weights
+            weights = torch.nn.functional.max_pool1d(
+                weights.unsqueeze(1).float(), 2, 1, padding=1)
+            weights = torch.nn.functional.avg_pool1d(weights, 2, 1).squeeze()
+            weights = weights + 0.01
+
+            z_vals_mid = 0.5 * (z_vals[:, :-1] + z_vals[:, 1:])
+            importance_z_vals = self.sample_pdf(z_vals_mid, weights[:, 1:-1],
+                                                N_importance).detach().reshape(
+                                                    batch_size, num_rays,
+                                                    N_importance, 1)
+        return importance_z_vals
+
+    def sample_pdf(self, bins, weights, N_importance, det=False, eps=1e-5):
+        """
+        Sample @N_importance samples from @bins with distribution defined by @weights.
+        Inputs:
+            bins: (N_rays, N_samples_+1) where N_samples_ is "the number of coarse samples per ray - 2"
+            weights: (N_rays, N_samples_)
+            N_importance: the number of samples to draw from the distribution
+            det: deterministic or not
+            eps: a small number to prevent division by zero
+        Outputs:
+            samples: the sampled samples
+        """
+        N_rays, N_samples_ = weights.shape
+        weights = weights + eps  # prevent division by zero (don't do inplace op!)
+        pdf = weights / torch.sum(weights, -1,
+                                  keepdim=True)  # (N_rays, N_samples_)
+        cdf = torch.cumsum(
+            pdf, -1)  # (N_rays, N_samples), cumulative distribution function
+        cdf = torch.cat([torch.zeros_like(cdf[:, :1]), cdf],
+                        -1)  # (N_rays, N_samples_+1)
+        # padded to 0~1 inclusive
+
+        if det:
+            u = torch.linspace(0, 1, N_importance, device=bins.device)
+            u = u.expand(N_rays, N_importance)
+        else:
+            u = torch.rand(N_rays, N_importance, device=bins.device)
+        u = u.contiguous()
+
+        inds = torch.searchsorted(cdf, u, right=True)
+        below = torch.clamp_min(inds - 1, 0)
+        above = torch.clamp_max(inds, N_samples_)
+
+        inds_sampled = torch.stack([below, above],
+                                   -1).view(N_rays, 2 * N_importance)
+        cdf_g = torch.gather(cdf, 1,
+                             inds_sampled).view(N_rays, N_importance, 2)
+        bins_g = torch.gather(bins, 1,
+                              inds_sampled).view(N_rays, N_importance, 2)
+
+        denom = cdf_g[..., 1] - cdf_g[..., 0]
+        denom[
+            denom <
+            eps] = 1  # denom equals 0 means a bin has weight 0, in which case it will not be sampled
+        # anyway, therefore any value for it is fine (set to 1 here)
+
+        samples = bins_g[..., 0] + (u - cdf_g[..., 0]) / denom * (
+            bins_g[..., 1] - bins_g[..., 0])
+        return samples
+
+
+class ImportanceRendererfg_bg(ImportanceRenderer):
+    """
+    render foreground-background together, using nerfpp strategy.
+    """
+    def __init__(self):
+        super().__init__()
+
+    def forward_background(self, bg_planes, decoder, ray_origins,
+                           ray_directions, rendering_options):
+        # ! no importance sampling here.
+
+        # # background depth
+        depths_coarse = self.sample_stratified(
+            ray_origins, 0, 1, rendering_options['bg_depth_resolution'],
+            rendering_options['disparity_space_sampling']).squeeze(
+                -1)  # remove the last 1 dim, B N S here
+
+        batch_size, num_rays, samples_per_ray = depths_coarse.shape
+
+        sample_directions = ray_directions.unsqueeze(-2).expand(
+            -1, -1, samples_per_ray, -1)
+        sample_origins = ray_origins.unsqueeze(-2).expand(
+            -1, -1, samples_per_ray, -1)
+
+        bg_sample_coordinates, _ = depth2pts_outside(
+            sample_origins, sample_directions,
+            depths_coarse)  # [..., N_samples, 4]
+
+        out = self.run_model(bg_planes, decoder, bg_sample_coordinates,
+                             sample_directions.reshape(batch_size, -1, 3),
+                             rendering_options)
+
+        colors_coarse = out['rgb']
+        densities_coarse = out['sigma']
+        colors_coarse = colors_coarse.reshape(batch_size, num_rays,
+                                              samples_per_ray,
+                                              colors_coarse.shape[-1])
+        densities_coarse = densities_coarse.reshape(batch_size, num_rays,
+                                                    samples_per_ray, 1)
+
+        rgb_final, depth_final, _, weights = self.ray_marcher(
+            colors_coarse, densities_coarse, depths_coarse, rendering_options)
+
+        ret_dict = {
+            'feature_samples': rgb_final,
+            'depth_samples': depth_final,
+            'weights_samples': weights.sum(2),
+            # 'visibility': visibility  # T[..., -1]
+        }
+
+        return ret_dict
+
+    def forward(self,
+                planes,
+                decoder,
+                ray_origins,
+                ray_directions,
+                rendering_options,
+                return_meta=False):
+
+        fg_planes, bg_planes = torch.split(
+            planes, planes.shape[2] // 2,
+            dim=2)  # concatenated on the Channel side
+
+        # ! composite fg/bg
+        fg_ret_dict = super().forward(fg_planes,
+                                      decoder,
+                                      ray_origins,
+                                      ray_directions,
+                                      rendering_options,
+                                      return_meta=False)
+
+        bg_ret_dict = self.forward_background(
+            bg_planes,
+            decoder,
+            ray_origins,
+            ray_directions,
+            rendering_options,
+        )
+
+        ret_dict = {**fg_ret_dict, 'bg_ret_dict': bg_ret_dict}  # for compat
+
+        return ret_dict  # will composite in the external triplane.py
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f79791146759880d4e83c36e9451687afcbb4f39
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,44 @@
+torch
+xformers==0.0.26.post1+cu118
+torchvision
+absl-py
+tensorboard
+lpips
+kornia
+opencv-python
+ffmpeg
+einops
+beartype
+imageio[ffmpeg]
+blobfile
+ninja
+pyspng
+psutil
+mrcfile
+lmdb
+webdataset
+point_cloud_utils
+kiui
+plyfile
+open_clip_torch==2.24.0
+openai-clip
+timm
+matplotlib
+torchtyping
+omegaconf
+pytorch_lightning
+lz4
+xatlas
+open3d
+trimesh
+huggingface_hub
+safetensors
+PyMCubes
+vision aided loss
+transformers
+torchdiffeg==8.2.4torchmetrics==1.4.3torchtyping==0.1.5imath
+OpenEXR
+onnxscript
+tritonclient
+gradio==4.43.0
+third_party/diff-surfel-rasterization
\ No newline at end of file
diff --git a/scripts/__pycache__/run_0123pp.cpython-39.pyc b/scripts/__pycache__/run_0123pp.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c07eed76d0b4584dc909236b00f174323f146382
Binary files /dev/null and b/scripts/__pycache__/run_0123pp.cpython-39.pyc differ
diff --git a/scripts/gradio_app_stage2.py b/scripts/gradio_app_stage2.py
new file mode 100644
index 0000000000000000000000000000000000000000..127deec81336774df21634fc83bed0a9c0d19f82
--- /dev/null
+++ b/scripts/gradio_app_stage2.py
@@ -0,0 +1,370 @@
+import argparse
+import json
+import sys
+sys.path.append('.')
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+
+import os
+import dnnlib
+from omegaconf import OmegaConf
+from PIL import Image 
+from dnnlib.util import EasyDict
+
+import gradio as gr
+
+import rembg
+
+from huggingface_hub import hf_hub_download
+
+
+"""
+Generate a large batch of image samples from a model and save them as a large
+numpy array. This can be used to produce samples for FID evaluation.
+"""
+
+import os
+
+
+from pdb import set_trace as st
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    NUM_CLASSES,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+    add_dict_to_argparser,
+    args_to_dict,
+    continuous_diffusion_defaults,
+    control_net_defaults,
+)
+
+th.backends.cuda.matmul.allow_tf32 = True
+th.backends.cudnn.allow_tf32 = True
+th.backends.cudnn.enabled = True
+
+from pathlib import Path
+
+from tqdm import tqdm, trange
+import dnnlib
+from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, AE_with_Diffusion, rendering_options_defaults, eg3d_options_default, dataset_defaults
+
+from datasets.shapenet import load_eval_data
+from torch.utils.data import Subset
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+from transport.train_utils import parse_transport_args
+
+from utils.infer_utils import remove_background, resize_foreground
+
+SEED = 0
+
+def resize_to_224(img):
+    img = transforms.functional.resize(img, 224,
+        interpolation=transforms.InterpolationMode.LANCZOS)
+    return img
+
+
+def set_white_background(image):
+    image = np.array(image).astype(np.float32) / 255.0
+    mask = image[:, :, 3:4]
+    image = image[:, :, :3] * mask + (1 - mask)
+    image = Image.fromarray((image * 255.0).astype(np.uint8))
+    return image
+
+
+def check_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image uploaded!")
+
+
+
+def main(args):
+
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '12355'
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    os.environ["RANK"] = "0"
+    os.environ["WORLD_SIZE"] = "1"
+
+    # args.rendering_kwargs = rendering_options_defaults(args)
+
+    dist_util.setup_dist(args)
+    logger.configure(dir=args.logdir)
+
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    # * set denoise model args
+    logger.log("creating model and diffusion...")
+    args.img_size = [args.image_size_encoder]
+    # ! no longer required for LDM
+    # args.denoise_in_channels = args.out_chans
+    # args.denoise_out_channels = args.out_chans
+    args.image_size = args.image_size_encoder  # 224, follow the triplane size
+
+    denoise_model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args,
+                       model_and_diffusion_defaults().keys()))
+
+    # if 'cldm' in args.trainer_name:
+    #     assert isinstance(denoise_model, tuple)
+    #     denoise_model, controlNet = denoise_model
+
+    #     controlNet.to(dist_util.dev())
+    #     controlNet.train()
+    # else:
+        # controlNet = None
+
+    opts = eg3d_options_default()
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    # denoise_model.load_state_dict(
+    #     dist_util.load_state_dict(args.ddpm_model_path, map_location="cpu"))
+    denoise_model.to(dist_util.dev())
+    denoise_model.eval()
+
+    # * auto-encoder reconstruction model
+    logger.log("creating 3DAE...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    auto_encoder.to(dist_util.dev())
+    auto_encoder.eval()
+
+    # faster inference
+    # denoise_model = denoise_model.to(th.bfloat16)
+    # auto_encoder = auto_encoder.to(th.bfloat16)
+
+    # TODO, how to set the scale?
+    logger.log("create dataset")
+
+    if args.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data
+    else:  # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data
+    
+    # load data if i23d
+    if args.i23d:
+        data = load_eval_data(
+            file_path=args.eval_data_dir,
+            batch_size=args.eval_batch_size,
+            reso=args.image_size,
+            reso_encoder=args.image_size_encoder,  # 224 -> 128
+            num_workers=args.num_workers,
+            load_depth=True,  # for evaluation
+            preprocess=auto_encoder.preprocess,
+            **args_to_dict(args,
+                            dataset_defaults().keys()))
+    else:
+        data = None # t23d sampling, only caption required
+
+
+    TrainLoop = {
+        'flow_matching':
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine,
+        'flow_matching_gs':  
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine_gs, # slightly modified sampling and rendering for gs
+    }[args.trainer_name]
+
+    # continuous
+    sde_diffusion = None
+
+    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
+    stage_1_output_dir = args.stage_1_output_dir
+
+    training_loop_class = TrainLoop(rec_model=auto_encoder,
+                                    denoise_model=denoise_model,
+                                    control_model=None, # to remove
+                                    diffusion=diffusion,
+                                    sde_diffusion=sde_diffusion,
+                                    loss_class=None,
+                                    data=data,
+                                    eval_data=None,
+                                    **args)
+
+    css = """
+    h1 {
+        text-align: center;
+        display:block;
+    }
+    """
+
+
+    def preprocess(input_image, preprocess_background=True, foreground_ratio=0.85):
+        if preprocess_background:
+            rembg_session = rembg.new_session()
+            image = input_image.convert("RGB")
+            image = remove_background(image, rembg_session)
+            image = resize_foreground(image, foreground_ratio)
+            image = set_white_background(image)
+        else:
+            image = input_image
+            if image.mode == "RGBA":
+                image = set_white_background(image)
+        image = resize_to_224(image)
+        return image
+
+
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            """
+            # LN3Diff (Scalable Latent Neural Fields Diffusion for Speedy 3D Generation)
+
+            **LN3Diff (ECCV 2024)** [[code](https://github.com/NIRVANALAN/LN3Diff), [project page](https://nirvanalan.github.io/projects/ln3diff/)] is a scalable 3D latent diffusion model that supports speedy 3D assets generation. 
+            It first trains a 3D VAE on **Objaverse**, which compress each 3D asset into a compact 3D-aware latent. After that, a image/text-conditioned diffusion model is trained following LDM paradigm.
+            The model used in the demo adopts DiT-L/2 architecture and flow-matching framework, and supports single-image condition.
+            It is trained on 8 A100 GPUs for 1M iterations with batch size 256.
+            Locally, on an NVIDIA A100/A10 GPU, each image-conditioned diffusion generation can be done in 10~20 seconds (time varies due to the adaptive-step ODE solver used in flow-mathcing.)
+            Upload an image of an object or click on one of the provided examples to see how the LN3Diff works.
+            The 3D viewer will render a .obj object exported from the triplane, where the mesh resolution and iso-surface can be set manually.
+            For best results run the demo locally and render locally - to do so, clone the [main repository](https://github.com/NIRVANALAN/LN3Diff).
+            """
+            )
+        with gr.Row(variant="panel"):
+            with gr.Column():
+                with gr.Row():
+                    input_image = gr.Image(
+                        label="Input Image",
+                        image_mode="RGBA",
+                        sources="upload",
+                        type="pil",
+                        elem_id="content_image",
+                    )
+                    processed_image = gr.Image(label="Processed Image", interactive=False)
+
+                # params
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            # with gr.Group():
+
+                            unconditional_guidance_scale = gr.Number(
+                                label="CFG-scale", value=4.0, interactive=True,
+                            )
+                            seed = gr.Number(
+                                label="Seed", value=42, interactive=True,
+                            )
+
+                            num_steps = gr.Number(
+                                label="ODE Sampling Steps", value=250, interactive=True,
+                            )
+
+                        # with gr.Column():
+                        with gr.Row():
+                                mesh_size = gr.Number(
+                                    label="Mesh Resolution", value=192, interactive=True,
+                            )
+
+                                mesh_thres = gr.Number(
+                                    label="Mesh Iso-surface", value=10, interactive=True,
+                                )
+
+                with gr.Row():
+                    with gr.Group():
+                        preprocess_background = gr.Checkbox(
+                            label="Remove Background", value=True
+                        )
+                with gr.Row():
+                    submit = gr.Button("Generate", elem_id="generate", variant="primary")
+
+                with gr.Row(variant="panel"): 
+                    gr.Examples(
+                        examples=[
+                            str(path) for path in sorted(Path('./assets/demo-image-for-i23d/instantmesh').glob('**/*.png'))
+                        ],
+                        inputs=[input_image],
+                        cache_examples=False,
+                        label="Examples",
+                        examples_per_page=20,
+                    )
+
+            with gr.Column():
+                with gr.Row():
+                    with gr.Tab("Reconstruction"):
+                        with gr.Column():
+                            output_video = gr.Video(value=None, width=512, label="Rendered Video", autoplay=True, loop=True)
+                            # output_video = gr.Video(value=None, width=256, label="Rendered Video", autoplay=True)
+                            output_gs = gr.Model3D(
+                                height=256,
+                                label="2DGS Center",
+                                pan_speed=0.5,
+                                clear_color=(1,1,1,1), # loading glb file only.
+                            )
+                            output_model = gr.Model3D(
+                                height=256,
+                                label="TSDF Mesh",
+                                pan_speed=0.5,
+                                clear_color=(1,1,1,1), # loading tsdf ply files.
+                            )
+
+        gr.Markdown(
+            """
+            ## Comments:
+            1. The sampling time varies since ODE-based sampling method (dopri5 by default) has adaptive internal step, and reducing sampling steps may not reduce the overal sampling time. Sampling steps=250 is the emperical value that works well in most cases.
+            2. The 3D viewer shows a colored .glb mesh extracted from volumetric tri-plane, and may differ slightly with the volume rendering result.
+            3. If you find your result unsatisfying, tune the CFG scale and change the random seed. Usually slightly increase the CFG value can lead to better performance.
+            3. Known limitations include:
+            - Texture details missing: since our VAE is trained on 192x192 resolution due the the resource constraints, the texture details generated by the final 3D-LDM may be blurry. We will keep improving the performance in the future.
+            4. Regarding reconstruction performance, our model is slightly inferior to state-of-the-art multi-view LRM-based method (e.g. InstantMesh), but offers much better diversity, flexibility and editing potential due to the intrinsic nature of diffusion model.
+
+            ## How does it work?
+
+            LN3Diff is a feedforward 3D Latent Diffusion Model that supports direct 3D asset generation via diffusion sampling. 
+            Compared to SDS-based ([DreamFusion](https://dreamfusion3d.github.io/)), mulit-view generation-based ([MVDream](https://arxiv.org/abs/2308.16512), [Zero123++](https://github.com/SUDO-AI-3D/zero123plus), [Instant3D](https://instant-3d.github.io/)) and feedforward 3D reconstruction-based ([LRM](https://yiconghong.me/LRM/), [InstantMesh](https://github.com/TencentARC/InstantMesh), [LGM](https://github.com/3DTopia/LGM)), 
+            LN3Diff supports feedforward 3D generation with a unified framework.
+            Like 2D/Video AIGC pipeline, LN3Diff first trains a 3D-VAE and then conduct LDM training (text/image conditioned) on the learned latent space. Some related methods from the industry ([Shape-E](https://github.com/openai/shap-e), [CLAY](https://github.com/CLAY-3D/OpenCLAY), [Meta 3D Gen](https://arxiv.org/abs/2303.05371)) also follow the same paradigm.
+            Though currently the performance of the origin 3D LDM's works are overall inferior to reconstruction-based methods, we believe the proposed method has much potential and scales better with more data and compute resources, and may yield better 3D editing performance due to its compatability with diffusion model.
+            For more results see the [project page](https://szymanowiczs.github.io/splatter-image) and the [ECCV article](https://arxiv.org/pdf/2403.12019).
+            """
+        )
+
+        submit.click(fn=check_input_image, inputs=[input_image]).success(
+            fn=preprocess,
+            inputs=[input_image, preprocess_background],
+            outputs=[processed_image],
+        ).success(
+            # fn=reconstruct_and_export,
+            # inputs=[processed_image],
+            # outputs=[output_model, output_video],
+            fn=training_loop_class.eval_i23d_and_export_gradio,
+            inputs=[processed_image, seed, mesh_size, mesh_thres, unconditional_guidance_scale],
+            # inputs=[processed_image, num_steps, seed, mesh_size, mesh_thres, unconditional_guidance_scale, args.stage_1_output_dir],
+            outputs=[output_video, output_gs, output_model,],
+        )
+
+    demo.queue(max_size=1)
+    demo.launch(share=True)
+
+if __name__ == "__main__":
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    with open('configs/gradio_i23d_stage2_args.json') as f:
+        args = json.load(f)
+        args = EasyDict(args)
+
+    args.local_rank = 0
+    args.gpus = 1
+
+    main(args)
diff --git a/scripts/run_0123pp.py b/scripts/run_0123pp.py
new file mode 100644
index 0000000000000000000000000000000000000000..d24f30e9ee169518491eed73b415f588bbbf7669
--- /dev/null
+++ b/scripts/run_0123pp.py
@@ -0,0 +1,288 @@
+import os
+import imageio
+import argparse
+from pdb import set_trace as st
+import numpy as np
+import torch
+import rembg
+from PIL import Image
+from torchvision.transforms import v2
+from pytorch_lightning import seed_everything
+from omegaconf import OmegaConf
+from einops import rearrange, repeat
+from tqdm import tqdm
+from huggingface_hub import hf_hub_download
+from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
+
+from src.utils.train_util import instantiate_from_config
+from src.utils.camera_util import (
+    FOV_to_intrinsics, 
+    get_zero123plus_input_cameras,
+    get_circular_camera_poses,
+)
+from src.utils.mesh_util import save_obj, save_obj_with_mtl
+from src.utils.infer_util import remove_background, resize_foreground, save_video
+
+
+def get_render_cameras(batch_size=1, M=120, radius=4.0, elevation=20.0, is_flexicubes=False):
+    """
+    Get the rendering camera parameters.
+    """
+    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
+    if is_flexicubes:
+        cameras = torch.linalg.inv(c2ws)
+        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
+    else:
+        extrinsics = c2ws.flatten(-2)
+        intrinsics = FOV_to_intrinsics(30.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
+        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
+        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
+    return cameras
+
+
+def render_frames(model, planes, render_cameras, render_size=512, chunk_size=1, is_flexicubes=False):
+    """
+    Render frames from triplanes.
+    """
+    frames = []
+    for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
+        if is_flexicubes:
+            frame = model.forward_geometry(
+                planes,
+                render_cameras[:, i:i+chunk_size],
+                render_size=render_size,
+            )['img']
+        else:
+            frame = model.forward_synthesizer(
+                planes,
+                render_cameras[:, i:i+chunk_size],
+                render_size=render_size,
+            )['images_rgb']
+        frames.append(frame)
+    
+    frames = torch.cat(frames, dim=1)[0]    # we suppose batch size is always 1
+    return frames
+
+
+###############################################################################
+# Arguments.
+###############################################################################
+
+parser = argparse.ArgumentParser()
+parser.add_argument('config', type=str, help='Path to config file.')
+parser.add_argument('input_path', type=str, help='Path to input image or directory.')
+# parser.add_argument('--output_path', type=str, default='outputs/', help='Output directory.')
+parser.add_argument('--output_path', type=str, default='outputs_debug/', help='Output directory.')
+parser.add_argument('--diffusion_steps', type=int, default=75, help='Denoising Sampling steps.')
+parser.add_argument('--seed', type=int, default=42, help='Random seed for sampling.')
+parser.add_argument('--scale', type=float, default=1.0, help='Scale of generated object.')
+parser.add_argument('--distance', type=float, default=4.5, help='Render distance.')
+parser.add_argument('--view', type=int, default=6, choices=[4, 6], help='Number of input views.')
+parser.add_argument('--no_rembg', action='store_true', help='Do not remove input background.')
+parser.add_argument('--export_texmap', action='store_true', help='Export a mesh with texture map.')
+parser.add_argument('--save_video', action='store_true', help='Save a circular-view video.')
+args = parser.parse_args()
+seed_everything(args.seed)
+
+###############################################################################
+# Stage 0: Configuration.
+###############################################################################
+
+config = OmegaConf.load(args.config)
+config_name = os.path.basename(args.config).replace('.yaml', '')
+model_config = config.model_config
+infer_config = config.infer_config
+
+IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
+
+device = torch.device('cuda')
+
+input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0*args.scale).to(device)
+
+render_cameras = get_render_cameras(
+    batch_size=1, 
+    M=120, 
+    radius=args.distance, 
+    # elevation=20.0,
+    elevation=0,
+    is_flexicubes=IS_FLEXICUBES,
+    ).to(device)
+
+# torch.save(input_cameras.cpu(), 'input_cameras_1.5.pt')
+# torch.save(render_cameras.cpu(), 'render_cameras_1.5.pt')
+
+# st()
+
+# load diffusion model
+print('Loading diffusion model ...')
+pipeline = DiffusionPipeline.from_pretrained(
+    "sudo-ai/zero123plus-v1.2", 
+    custom_pipeline="zero123plus",
+    torch_dtype=torch.float16,
+)
+pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
+    pipeline.scheduler.config, timestep_spacing='trailing'
+)
+
+# load custom white-background UNet
+print('Loading custom white-background unet ...')
+if os.path.exists(infer_config.unet_path):
+    unet_ckpt_path = infer_config.unet_path
+else:
+    unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
+state_dict = torch.load(unet_ckpt_path, map_location='cpu')
+pipeline.unet.load_state_dict(state_dict, strict=True)
+
+pipeline = pipeline.to(device)
+
+# load reconstruction model
+print('Loading reconstruction model ...')
+model = instantiate_from_config(model_config)
+if os.path.exists(infer_config.model_path):
+    model_ckpt_path = infer_config.model_path
+else:
+    model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename=f"{config_name.replace('-', '_')}.ckpt", repo_type="model")
+state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
+state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.')}
+model.load_state_dict(state_dict, strict=True)
+
+model = model.to(device)
+if IS_FLEXICUBES:
+    model.init_flexicubes_geometry(device, fovy=30.0)
+model = model.eval()
+
+# make output directories
+image_path = os.path.join(args.output_path, config_name, 'images')
+mesh_path = os.path.join(args.output_path, config_name, 'meshes')
+video_path = os.path.join(args.output_path, config_name, 'videos')
+os.makedirs(image_path, exist_ok=True)
+os.makedirs(mesh_path, exist_ok=True)
+os.makedirs(video_path, exist_ok=True)
+
+# process input files
+if os.path.isdir(args.input_path):
+    input_files = [
+        os.path.join(args.input_path, file) 
+        for file in os.listdir(args.input_path) 
+        if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.webp')
+    ]
+else:
+    input_files = [args.input_path]
+print(f'Total number of input images: {len(input_files)}')
+
+
+###############################################################################
+# Stage 1: Multiview generation.
+###############################################################################
+
+rembg_session = None if args.no_rembg else rembg.new_session()
+
+outputs = []
+for idx, image_file in enumerate(input_files):
+    name = os.path.basename(image_file).split('.')[0]
+    print(f'[{idx+1}/{len(input_files)}] Imagining {name} ...')
+
+    # remove background optionally
+    input_image = Image.open(image_file)
+    if not args.no_rembg:
+        input_image = remove_background(input_image, rembg_session)
+        input_image = resize_foreground(input_image, 0.85)
+
+    imageio.imwrite(os.path.join(image_path, f'{name}-input.png'), np.array(input_image))
+    # continue
+    
+    # sampling
+    output_image = pipeline(
+        input_image, 
+        num_inference_steps=args.diffusion_steps, 
+    ).images[0]
+
+    output_image.save(os.path.join(image_path, f'{name}.png'))
+    print(f"Image saved to {os.path.join(image_path, f'{name}.png')}")
+
+    images = np.asarray(output_image, dtype=np.float32) / 255.0
+    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
+    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)
+
+    st()
+
+    outputs.append({'name': name, 'images': images})
+
+# delete pipeline to save memory
+del pipeline
+
+
+###############################################################################
+# Stage 2: Reconstruction.
+###############################################################################
+
+
+exit()
+chunk_size = 20 if IS_FLEXICUBES else 1
+
+for idx, sample in enumerate(outputs):
+    name = sample['name']
+    print(f'[{idx+1}/{len(outputs)}] Creating {name} ...')
+
+    images = sample['images'].unsqueeze(0).to(device)
+    images = v2.functional.resize(images, 320, interpolation=3, antialias=True).clamp(0, 1)
+
+    if args.view == 4:
+        indices = torch.tensor([0, 2, 4, 5]).long().to(device)
+        images = images[:, indices]
+        input_cameras = input_cameras[:, indices]
+
+    with torch.no_grad():
+        # get triplane
+        planes = model.forward_planes(images, input_cameras)
+
+        # get mesh
+        mesh_path_idx = os.path.join(mesh_path, f'{name}.obj')
+
+        mesh_out = model.extract_mesh(
+            planes,
+            use_texture_map=args.export_texmap,
+            **infer_config,
+        )
+        if args.export_texmap:
+            vertices, faces, uvs, mesh_tex_idx, tex_map = mesh_out
+            save_obj_with_mtl(
+                vertices.data.cpu().numpy(),
+                uvs.data.cpu().numpy(),
+                faces.data.cpu().numpy(),
+                mesh_tex_idx.data.cpu().numpy(),
+                tex_map.permute(1, 2, 0).data.cpu().numpy(),
+                mesh_path_idx,
+            )
+        else:
+            vertices, faces, vertex_colors = mesh_out
+            save_obj(vertices, faces, vertex_colors, mesh_path_idx)
+        print(f"Mesh saved to {mesh_path_idx}")
+
+        # get video
+        if args.save_video:
+            video_path_idx = os.path.join(video_path, f'{name}.mp4')
+            render_size = infer_config.render_resolution
+            # render_cameras = get_render_cameras(
+            #     batch_size=1, 
+            #     M=120, 
+            #     radius=args.distance, 
+            #     elevation=20.0,
+            #     is_flexicubes=IS_FLEXICUBES,
+            # ).to(device)
+            
+            frames = render_frames(
+                model, 
+                planes, 
+                render_cameras=render_cameras, 
+                render_size=render_size, 
+                chunk_size=chunk_size, 
+                is_flexicubes=IS_FLEXICUBES,
+            )
+
+            save_video(
+                frames,
+                video_path_idx,
+                fps=30,
+            )
+            print(f"Video saved to {video_path_idx}")
diff --git a/scripts/save_pcd.py b/scripts/save_pcd.py
new file mode 100644
index 0000000000000000000000000000000000000000..3430c5a1c8c3be1471f9bea4569ea57a6e1ff9e7
--- /dev/null
+++ b/scripts/save_pcd.py
@@ -0,0 +1,554 @@
+"""
+Train a diffusion model on images.
+"""
+# import imageio
+from pathlib import Path
+import torchvision
+import kornia
+import lz4.frame
+import gzip
+import random
+import json
+import sys
+import os
+import lmdb
+from tqdm import tqdm
+
+sys.path.append('.')
+import torch.distributed as dist
+import pytorch3d.ops
+import pickle
+import traceback
+from PIL import Image
+import torch as th
+if th.cuda.is_available():
+    from xformers.triton import FusedLayerNorm as LayerNorm
+import torch.multiprocessing as mp
+import lzma
+import webdataset as wds
+import numpy as np
+
+import point_cloud_utils as pcu
+from torch.utils.data import DataLoader, Dataset
+import imageio.v3 as iio
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+# from nsr.train_util import TrainLoop3DRec as TrainLoop
+from nsr.train_nv_util import TrainLoop3DRecNV, TrainLoop3DRec, TrainLoop3DRecNVPatch
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
+from datasets.shapenet import load_data, load_data_for_lmdb, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+from datasets.eg3d_dataset import init_dataset_kwargs
+from nsr.volumetric_rendering.ray_sampler import RaySampler
+
+# from .lmdb_create import encode_and_compress_image
+
+
+def encode_and_compress_image(inp_array, is_image=False, compress=True):
+    # Read the image using imageio
+    # image = imageio.v3.imread(image_path)
+
+    # Convert the image to bytes
+    # with io.BytesIO() as byte_buffer:
+    #     imageio.imsave(byte_buffer, image, format="png")
+    #     image_bytes = byte_buffer.getvalue()
+    if is_image:
+        inp_bytes = iio.imwrite("<bytes>", inp_array, extension=".png")
+    else:
+        inp_bytes = inp_array.tobytes()
+
+    # Compress the image data using gzip
+    if compress:
+        # compressed_data = gzip.compress(inp_bytes)
+        compressed_data = lz4.frame.compress(inp_bytes)
+        return compressed_data
+    else:
+        return inp_bytes
+
+
+from pdb import set_trace as st
+import bz2
+
+# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
+
+
+def training_loop(args):
+    # def training_loop(args):
+    # dist_util.setup_dist(args)
+    # th.autograd.set_detect_anomaly(True) # type: ignore
+    th.autograd.set_detect_anomaly(False)  # type: ignore
+    # https://blog.csdn.net/qq_41682740/article/details/126304613
+
+    SEED = args.seed
+
+    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
+    # logger.log(f"{args.local_rank=} init complete, seed={SEED}")
+    # th.cuda.set_device(args.local_rank)
+    th.cuda.empty_cache()
+
+    # * deterministic algorithms flags
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+    random.seed(SEED)
+
+    ray_sampler = RaySampler()
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating encoder and NSR decoder...")
+    # device = dist_util.dev()
+    # device = th.device("cuda", args.local_rank)
+
+    # shared eg3d opts
+    opts = eg3d_options_default()
+
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    if args.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_data_for_lmdb
+    else:  # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data, load_data_for_lmdb
+
+    # auto_encoder = create_3DAE_model(
+    #     **args_to_dict(args,
+    #                    encoder_and_nsr_defaults().keys()))
+    # auto_encoder.to(device)
+    # auto_encoder.train()
+
+    logger.log("creating data loader...")
+    # data = load_data(
+    # st()
+    # if args.overfitting:
+    #     data = load_memory_data(
+    #         file_path=args.data_dir,
+    #         batch_size=args.batch_size,
+    #         reso=args.image_size,
+    #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #         num_workers=args.num_workers,
+    #         # load_depth=args.depth_lambda > 0
+    #         load_depth=True  # for evaluation
+    #     )
+    # else:
+    if args.cfg in ('afhq', 'ffhq'):
+        # ! load data
+        logger.log("creating eg3d data loader...")
+        training_set_kwargs, dataset_name = init_dataset_kwargs(
+            data=args.data_dir,
+            class_name='datasets.eg3d_dataset.ImageFolderDatasetLMDB',
+            reso_gt=args.image_size)  # only load pose here
+        # if args.cond and not training_set_kwargs.use_labels:
+        # raise Exception('check here')
+
+        # training_set_kwargs.use_labels = args.cond
+        training_set_kwargs.use_labels = True
+        training_set_kwargs.xflip = False
+        training_set_kwargs.random_seed = SEED
+        # training_set_kwargs.max_size = args.dataset_size
+        # desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
+
+        # * construct ffhq/afhq dataset
+        training_set = dnnlib.util.construct_class_by_name(
+            **training_set_kwargs)  # subclass of training.dataset.Dataset
+        dataset_size = len(training_set)
+
+        # training_set_sampler = InfiniteSampler(
+        #     dataset=training_set,
+        #     rank=dist_util.get_rank(),
+        #     num_replicas=dist_util.get_world_size(),
+        #     seed=SEED)
+
+        data = DataLoader(
+            training_set,
+            shuffle=False,
+            batch_size=1,
+            num_workers=16,
+            drop_last=False,
+            # prefetch_factor=2,
+            pin_memory=True,
+            persistent_workers=True,
+        )
+
+    else:
+        # data, dataset_name, dataset_size, dataset = load_data_for_lmdb(
+        data, dataset_name, dataset_size = load_data_for_lmdb(
+            file_path=args.data_dir,
+            batch_size=args.batch_size,
+            reso=args.image_size,
+            reso_encoder=args.image_size_encoder,  # 224 -> 128
+            num_workers=args.num_workers,
+            load_depth=True,
+            preprocess=None,
+            dataset_size=args.dataset_size,
+            trainer_name=args.trainer_name,
+            shuffle_across_cls=args.shuffle_across_cls,
+            wds_split=args.wds_split,
+            four_view_for_latent=True
+            # wds_output_path=os.path.join(logger.get_dir(), f'wds-%06d.tar')
+            # load_depth=True # for evaluation
+        )
+    #     if args.pose_warm_up_iter > 0:
+    #         overfitting_dataset = load_memory_data(
+    #             file_path=args.data_dir,
+    #             batch_size=args.batch_size,
+    #             reso=args.image_size,
+    #             reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #             num_workers=args.num_workers,
+    #             # load_depth=args.depth_lambda > 0
+    #             load_depth=True  # for evaluation
+    #         )
+    #         data = [data, overfitting_dataset, args.pose_warm_up_iter]
+    # eval_data = load_eval_data(
+    #     file_path=args.eval_data_dir,
+    #     batch_size=args.eval_batch_size,
+    #     reso=args.image_size,
+    #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #     num_workers=args.num_workers,
+    #     load_depth=True,  # for evaluation
+    #     preprocess=auto_encoder.preprocess)
+    args.img_size = [args.image_size_encoder]
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+
+    # let all processes sync up before starting with a new epoch of training
+    dist_util.synchronize()
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    # opt.max_depth, opt.min_depth = args.rendering_kwargs.ray_end, args.rendering_kwargs.ray_start
+    # loss_class = E3DGELossClass(device, opt).to(device)
+
+    # writer = SummaryWriter() # TODO, add log dir
+
+    logger.log("training...")
+
+    # TrainLoop = {
+    #     'input_rec': TrainLoop3DRec,
+    #     'nv_rec': TrainLoop3DRecNV,
+    #     'nv_rec_patch': TrainLoop3DRecNVPatch,
+    # }[args.trainer_name]
+
+    # TrainLoop(rec_model=auto_encoder,
+    #           loss_class=loss_class,
+    #           data=data,
+    #           eval_data=eval_data,
+    #           **vars(args)).run_loop()  # ! overfitting
+
+    # Function to compress an image using gzip
+    # def compress_image_gzip(image_path):
+    # def encode_and_compress_image(inp_array, is_image=False):
+    #     # Read the image using imageio
+    #     # image = imageio.v3.imread(image_path)
+
+    #     # Convert the image to bytes
+    #     # with io.BytesIO() as byte_buffer:
+    #     #     imageio.imsave(byte_buffer, image, format="png")
+    #     #     image_bytes = byte_buffer.getvalue()
+    #     if is_image:
+    #         inp_bytes = iio.imwrite("<bytes>", inp_array, extension=".png")
+    #     else:
+    #         inp_bytes = inp_array.tobytes()
+
+    #     # Compress the image data using gzip
+    #     compressed_data = gzip.compress(inp_bytes)
+
+    #     return compressed_data
+
+    def save_pcd_from_depth(dataset_loader, dataset_size, lmdb_path,
+                            start_shard, wds_split):
+        """
+        Convert a PyTorch dataset to LMDB format.
+
+        Parameters:
+        - dataset: PyTorch dataset
+        - lmdb_path: Path to store the LMDB database
+        """
+        # env = lmdb.open(lmdb_path, map_size=1024 ** 4, readahead=False)  # Adjust map_size based on your dataset size
+        # sink = wds.ShardWriter(lmdb_path, start_shard=start_shard)
+
+        # with env.begin(write=True) as txn:
+
+        # with env.begin(write=True) as txn:
+        # txn.put("length".encode("utf-8"), str(dataset_size).encode("utf-8"))
+
+        # K = 10000 # fps K
+        K = 4096 # fps K
+        # K = 128*128*2 # fps K, 32768
+        # K = 1024*24 # 20480
+        # K = 4096 # fps K
+
+        # if True:
+
+        # try:
+        for idx, sample in enumerate(tqdm(dataset_loader)):
+
+            # pass
+            # remove the batch index of returned dict sample
+
+            sample_ins = sample.pop('ins')
+            # !!! add all()
+            assert all([ sample_ins[i] == sample_ins[0] for i in range(0, len(sample_ins)) ]), sample_ins  # check the batch is the same instnace
+
+            img_size = sample['raw_img'].shape[2]
+
+            pcd_path = Path(f'{logger.get_dir()}/fps-pcd/{sample_ins[0]}')
+
+            if (pcd_path / f'fps-{K}.ply').exists():
+                continue
+
+            pcd_path.mkdir(parents=True, exist_ok=True)
+
+            # sample = {
+            #     # k:v.squeeze(0).cpu().numpy() if isinstance(v, th.Tensor) else v[0] for k, v in sample.items()
+            #     k:v.cpu().numpy() if isinstance(v, th.Tensor) else v for k, v in sample.items()
+            #     # k:v.cpu().numpy() if isinstance(v, torch.Tensor) else v for k, v in sample.items()
+            # }
+
+            B = sample['c'].shape[0]
+
+            cam2world_matrix = sample['c'][:, :16].reshape(B, 4, 4)
+            intrinsics = sample['c'][:, 16:25].reshape(B, 3, 3)
+
+            ray_origins, ray_directions = ray_sampler(  # shape: 
+                cam2world_matrix, intrinsics, img_size)[:2]
+
+            micro = sample
+
+            # self.gs.output_size,)[:2]
+            # depth = rearrange(micro['depth'], '(B V) H W -> ')
+            # depth_128 = th.nn.functional.interpolate(
+            #     micro['depth'].unsqueeze(1), (128, 128),
+            #     mode='nearest'
+            # )[:, 0]  # since each view has 128x128 Gaussians
+            # depth = depth_128.reshape(B * V, -1).unsqueeze(-1)
+
+            # fg_mask = (micro['depth'] > 0).unsqueeze(1).float()
+
+            # fg_mask = micro['alpha_mask'].unsqueeze(1).float() # anti-alias? B 1 H W
+            fg_mask = (micro['alpha_mask'] == 1).unsqueeze(1).float() # anti-alias? B 1 H W
+
+            kernel = th.tensor([[0, 1, 0], [1, 1, 1], [0, 1,
+                                                       0]]).to(fg_mask.device)
+
+            # ! erode. but still some noise...
+            '''
+            erode_mask = kornia.morphology.erosion(fg_mask, kernel)  # B 1 H W
+            # torchvision.utils.save_image(fg_mask.float()*2-1,'mask.jpg',  value_range=(-1,1), normalize=True)
+            # torchvision.utils.save_image(erode_mask.float()*2-1,'erode_mask.jpg',  value_range=(-1,1), normalize=True)
+
+            fg_mask = (erode_mask==1).float().reshape(B, -1).unsqueeze(-1) > 0 #
+            # '''
+            # fg_mask = fg_mask.reshape(B, -1).unsqueeze(-1) == 1  # ! for some failed data
+            # ! no erode:
+            fg_mask = fg_mask.reshape(B, -1).unsqueeze(-1) > 0  # ! for some failed data
+
+            depth = micro['depth'].reshape(B, -1).unsqueeze(-1)
+            depth = th.where(depth < 1.05, 0, depth)  # filter outlier
+            depth[depth == 0] = 1e10  # so that rays_o will not appear in the final pcd.
+
+            # fg_mask = depth>0
+
+            # fg_mask = th.nn.functional.interpolate(
+            #     micro['depth_mask'].unsqueeze(1).to(th.uint8),
+            #     (128, 128),
+            #     mode='nearest').squeeze(1)  # B*V H W
+            # fg_mask = fg_mask.reshape(B * V, -1).unsqueeze(-1)
+
+
+            # gt_pos = gt_pos[gt_pos.nonzero(as_tuple=True)].reshape(-1, 3) # return non-zero points for fps sampling
+
+            # pcu.save_mesh_v(f'tmp/gt-512.ply', gt_pos.detach().cpu().numpy(),)
+
+            # fps sampling
+            try:
+                
+                gt_pos = ray_origins + depth * ray_directions  # BV HW 3, already in the world space
+                gt_pos = fg_mask * gt_pos  # remove ray_origins when depth=0
+                # gt_pos = gt_pos[[8,16,24,25,26, 27, 31, 35]]
+                # gt_pos = gt_pos[[5,10,15,20,24,25,26]]
+                # gt_pos = gt_pos[[4, 12, 20, 25]]
+                gt_pos = gt_pos[:]
+                # gt_pos = gt_pos[[25,26]]
+                gt_pos = gt_pos.reshape(-1, 3).to(dist_util.dev())
+                gt_pos = gt_pos.clip(-0.45, 0.45)
+                gt_pos = th.where(gt_pos.abs()==0.45, 0, gt_pos) # no boundary here? Yes.
+
+                # ! filter the zero points together here 
+
+                nonzero_mask = (gt_pos != 0).all(dim=-1)  # Shape: (N, 3)
+                nonzero_gt_pos = gt_pos[nonzero_mask] 
+
+                fps_points = pytorch3d.ops.sample_farthest_points(
+                    nonzero_gt_pos.unsqueeze(0), K=K)[0]
+
+                pcu.save_mesh_v(
+                    str(pcd_path / f'fps-{K}.ply'),
+                    fps_points[0].detach().cpu().numpy(),
+                )
+
+                assert (pcd_path / f'fps-{K}.ply').exists()
+            
+            except Exception as e:
+
+                st()
+                pass
+
+                print(pcd_path, 'save failed: ', e)
+            
+            # ! debug projection matrix
+
+            # def pcd_to_homo(pcd):
+            #     return th.cat([pcd, th.ones_like(pcd[..., 0:1])], -1)
+            
+            # st()
+
+            # proj_point = th.inverse(cam2world_matrix[0]).to(fps_points) @ pcd_to_homo(fps_points[0]).permute(1, 0)
+            # # proj_point = th.inverse(cam2world_matrix[0]).to(fps_points) @ pcd_to_homo((ray_origins + depth * ray_directions)[0].to(fps_points)).permute(1, 0)
+            # proj_point[:2, ...] /= proj_point[2, ...]
+            # proj_point[2, ...] = 1 # homo
+
+
+            # proj_point = intrinsics[0].to(fps_points) @ proj_point[:3]
+            # proj_point = proj_point.permute(1,0)[..., :2] # 768 4
+            # st()
+
+            # torchvision.utils.save_image(micro['raw_img'][::5].permute(0,3,1,2).float()/127.5-1,'raw.jpg',  value_range=(-1,1), normalize=True)
+
+            # # encode batch images/depths/strings?  no need to encode ins/fname here; just save the caption
+
+            # # sample = dataset_loader[idx]
+            # compressed_sample = {}
+            # sample['ins'] = sample_ins[0]
+            # sample['caption'] = sample.pop('caption')[0]
+
+            # for k, v in sample.items():
+
+            #     # key = f'{idx}-{k}'.encode('utf-8')
+
+            #     if 'img' in k: # only bytes required? laod the 512 depth bytes only.
+            #         v = encode_and_compress_image(v, is_image=True, compress=True)
+            #         # v = encode_and_compress_image(v, is_image=True, compress=False)
+            #     # elif 'depth' in k:
+            #     elif isinstance(v, str):
+            #         v = v.encode('utf-8') # caption / instance name
+            #     else: # regular bytes encoding
+            #         v = encode_and_compress_image(v.astype(np.float32), is_image=False, compress=True)
+            #         # v = encode_and_compress_image(v.astype(np.float32), is_image=False, compress=False)
+
+            #     compressed_sample[k] = v
+
+            # # st() # TODO, add .gz for compression after pipeline done
+            # sink.write({
+            #     "__key__": f"sample_{wds_split:03d}_{idx:07d}",
+            #     # **{f'{k}.pyd': v for k, v in compressed_sample.items()}, # store as pickle, already compressed
+            #     'sample.pyd': compressed_sample
+            #     # 'sample.gz': compressed_sample
+            # })
+
+            # break
+            # if idx > 25:
+            #     break
+        # except:
+            # continue
+
+        # sink.close()
+
+    # convert_to_lmdb(data, os.path.join(logger.get_dir(), dataset_name)) convert_to_lmdb_compressed(data, os.ath.join(logger.get_dir(), dataset_name))
+    # convert_to_lmdb_compressed(data, os.path.join(logger.get_dir()), dataset_size)
+    save_pcd_from_depth(data, dataset_size,
+                        os.path.join(logger.get_dir(), f'wds-%06d.tar'),
+                        args.start_shard, args.wds_split)
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        seed=0,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_amp=False,
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        # test warm up pose sampling training
+        objv_dataset=False,
+        pose_warm_up_iter=-1,
+        start_shard=0,
+        shuffle_across_cls=False,
+        wds_split=1,  # out of 4
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ[
+    # "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+    # os.environ["NCCL_DEBUG"]="INFO"
+
+    args = create_argparser().parse_args()
+    # args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    opts = args
+
+    args.rendering_kwargs = rendering_options_defaults(opts)
+
+    # print(args)
+    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=2)
+
+    # Launch processes.
+    print('Launching processes...')
+
+    # try:
+    training_loop(args)
+    # except KeyboardInterrupt as e:
+    # except Exception as e:
+    #     # print(e)
+    #     traceback.print_exc()
+    #     dist_util.cleanup() # clean port and socket when ctrl+c
diff --git a/scripts/save_pcd_from_gs.py b/scripts/save_pcd_from_gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..67756318c0c6dae070f838f9c899160c07699112
--- /dev/null
+++ b/scripts/save_pcd_from_gs.py
@@ -0,0 +1,273 @@
+"""
+Train a diffusion model on images.
+"""
+# import imageio
+from pathlib import Path
+import torchvision
+import kornia
+import lz4.frame
+import gzip
+import random
+import json
+import sys
+import os
+import lmdb
+from tqdm import tqdm
+
+sys.path.append('.')
+import torch.distributed as dist
+import pytorch3d.ops
+import pickle
+import traceback
+from PIL import Image
+import torch as th
+if th.cuda.is_available():
+    from xformers.triton import FusedLayerNorm as LayerNorm
+import torch.multiprocessing as mp
+import lzma
+import webdataset as wds
+import numpy as np
+
+import point_cloud_utils as pcu
+from torch.utils.data import DataLoader, Dataset
+import imageio.v3 as iio
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+# from nsr.train_util import TrainLoop3DRec as TrainLoop
+from nsr.train_nv_util import TrainLoop3DRecNV, TrainLoop3DRec, TrainLoop3DRecNVPatch
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
+from datasets.shapenet import load_data, load_data_for_lmdb, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+from datasets.eg3d_dataset import init_dataset_kwargs
+from nsr.volumetric_rendering.ray_sampler import RaySampler
+
+# from .lmdb_create import encode_and_compress_image
+
+
+def encode_and_compress_image(inp_array, is_image=False, compress=True):
+    # Read the image using imageio
+    # image = imageio.v3.imread(image_path)
+
+    # Convert the image to bytes
+    # with io.BytesIO() as byte_buffer:
+    #     imageio.imsave(byte_buffer, image, format="png")
+    #     image_bytes = byte_buffer.getvalue()
+    if is_image:
+        inp_bytes = iio.imwrite("<bytes>", inp_array, extension=".png")
+    else:
+        inp_bytes = inp_array.tobytes()
+
+    # Compress the image data using gzip
+    if compress:
+        # compressed_data = gzip.compress(inp_bytes)
+        compressed_data = lz4.frame.compress(inp_bytes)
+        return compressed_data
+    else:
+        return inp_bytes
+
+
+from pdb import set_trace as st
+import bz2
+
+# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
+
+
+def training_loop(args):
+    # def training_loop(args):
+    # dist_util.setup_dist(args)
+    # th.autograd.set_detect_anomaly(True) # type: ignore
+    th.autograd.set_detect_anomaly(False)  # type: ignore
+    # https://blog.csdn.net/qq_41682740/article/details/126304613
+
+    SEED = args.seed
+
+    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
+    # logger.log(f"{args.local_rank=} init complete, seed={SEED}")
+    # th.cuda.set_device(args.local_rank)
+    th.cuda.empty_cache()
+
+    # * deterministic algorithms flags
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+    random.seed(SEED)
+
+    ray_sampler = RaySampler()
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating encoder and NSR decoder...")
+    # device = dist_util.dev()
+    # device = th.device("cuda", args.local_rank)
+
+    # shared eg3d opts
+    opts = eg3d_options_default()
+
+    logger.log("creating data loader...")
+
+    # let all processes sync up before starting with a new epoch of training
+    dist_util.synchronize()
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    # opt.max_depth, opt.min_depth = args.rendering_kwargs.ray_end, args.rendering_kwargs.ray_start
+    # loss_class = E3DGELossClass(device, opt).to(device)
+
+    # writer = SummaryWriter() # TODO, add log dir
+
+    logger.log("training...")
+
+    def save_pcd_from_gs(lmdb_path, start_shard, wds_split):
+        """
+        Convert a PyTorch dataset to LMDB format.
+
+        Parameters:
+        - dataset: PyTorch dataset
+        - lmdb_path: Path to store the LMDB database
+        """
+
+        # ! read dataset path
+
+        # latent_dir = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/final/objav/vae/gs/infer-latents/768/8x8/animals/latent_dir/Animals'
+        latent_dir = '/nas/shared/V2V/yslan/logs/nips23/Reconstruction/final/objav/vae/gs/infer-latents/768/8x8/animals-gs-latent-dim=10-fullset/latent_dir'
+
+        ins_list = []
+
+        for class_dir in os.listdir(latent_dir)[:]:
+            for dict_dir in os.listdir(os.path.join(latent_dir, class_dir))[:]:
+                for ins_dir in os.listdir(os.path.join(latent_dir, class_dir, dict_dir)):
+                    ins_list.append(os.path.join(class_dir, dict_dir, ins_dir))
+
+        K = 4096 # fps K
+
+        for idx, ins in enumerate(tqdm(ins_list)):
+
+            # sample_ins = sample.pop('ins')
+            pcd_path = Path(f'{logger.get_dir()}/fps-pcd/{ins}')
+
+            if (pcd_path / f'fps-{K}.ply').exists():
+                continue
+
+            # ! load gaussians
+            gaussians = np.load(os.path.join(latent_dir,ins,'gaussians.npy'))
+
+            points = gaussians[0,:, 0:3]
+
+            # load opacity and scale
+            opacity = gaussians[0,:, 3:4]
+            # scale = gaussians[0,:, 4:6]
+            # colors = gaussians[0, :, 10:13]
+
+            opacity_mask = opacity < 0.005 # official threshold
+
+            high_opacity_points = points[~opacity_mask[..., 0]]
+            # high_opacity_colors = colors[~opacity_mask[..., 0]]
+            high_opacity_points = th.from_numpy(high_opacity_points).to(dist_util.dev())
+
+
+            pcd_path.mkdir(parents=True, exist_ok=True)
+
+            try:
+                
+                fps_points = pytorch3d.ops.sample_farthest_points(
+                    high_opacity_points.unsqueeze(0), K=K)[0]
+
+                pcu.save_mesh_v(
+                    str(pcd_path / f'fps-{K}.ply'),
+                    fps_points[0].detach().cpu().numpy(),
+                )
+
+                assert (pcd_path / f'fps-{K}.ply').exists()
+            
+            except Exception as e:
+                continue
+                print(pcd_path, 'save failed: ', e)
+
+
+    save_pcd_from_gs(os.path.join(logger.get_dir(), f'wds-%06d.tar'),
+                        args.start_shard, args.wds_split)
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        seed=0,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_amp=False,
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        # test warm up pose sampling training
+        objv_dataset=False,
+        pose_warm_up_iter=-1,
+        start_shard=0,
+        shuffle_across_cls=False,
+        wds_split=1,  # out of 4
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ[
+    # "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+    # os.environ["NCCL_DEBUG"]="INFO"
+
+    args = create_argparser().parse_args()
+    # args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    opts = args
+
+    args.rendering_kwargs = rendering_options_defaults(opts)
+
+    # print(args)
+    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=2)
+
+    # Launch processes.
+    print('Launching processes...')
+
+    # try:
+    training_loop(args)
+    # except KeyboardInterrupt as e:
+    # except Exception as e:
+    #     # print(e)
+    #     traceback.print_exc()
+    #     dist_util.cleanup() # clean port and socket when ctrl+c
diff --git a/scripts/split_rgb_normal_vid.py b/scripts/split_rgb_normal_vid.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0c70412e7b81ef6ded8a594db60d6fe7d49f01c
--- /dev/null
+++ b/scripts/split_rgb_normal_vid.py
@@ -0,0 +1,85 @@
+# import imageio.v3 as imageio
+import imageio
+from tqdm import tqdm
+from pdb import set_trace as st
+import glob
+import numpy as np
+import os
+from pathlib import Path
+# ! pip install opencv-python
+import cv2
+import matplotlib.pyplot as plt
+
+
+def save_2dgs_rgb_normal_vid(vid_path, output_dir):
+
+    frames = imageio.v3.imread(vid_path)
+    
+    vid_name = Path(vid_path).stem
+
+    # output frames here
+    # output_dir = f'{ga_output_dir}/{index}'
+    # if not os.path.exists(output_dir):
+    #     os.mkdir(output_dir)
+    # all_rgb_frames, all_normal_frames = [], []
+
+    rgb_video_out = imageio.get_writer(
+        f'{output_dir}/rgb/{vid_name}-rgb.mp4',
+        mode='I',
+        fps=24,
+        codec='libx264')
+
+    normal_video_out = imageio.get_writer(
+        f'{output_dir}/normal/{vid_name}-normal.mp4',
+        mode='I',
+        fps=24,
+        codec='libx264')
+
+    depth_video_out = imageio.get_writer(
+        f'{output_dir}/normal/{vid_name}-depth.mp4',
+        mode='I',
+        fps=24,
+        codec='libx264')
+
+
+    for idx, frame in enumerate(frames[:24]):
+        # frame_size = 512
+        frame_size = frame.shape[1] // 3
+
+        # rgb_video_out.append_data(frame[-384:, :384])
+        # normal_video_out.append_data(frame[-384:, 384*2:384*3])
+
+        rgb = frame[-frame_size:, :frame_size]
+        rgb_video_out.append_data(cv2.resize(rgb, (384, 384)))
+
+        depth = frame[-frame_size:, frame_size*2:frame_size*3]
+        depth_video_out.append_data(cv2.resize(depth, (384, 384)))
+
+        normal = frame[-frame_size:, frame_size*1:frame_size*2]
+        normal_video_out.append_data(cv2.resize(normal, (384, 384)))
+
+    rgb_video_out.close()
+    normal_video_out.close()
+    depth_video_out.close()
+
+
+# output_dir = '/mnt/sfs-common/yslan/Repo/3dgen/GA-logs/demo-video-buffer'
+# vid_input_dir = '/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange'
+
+output_dir = '/mnt/sfs-common/yslan/Repo/3dgen/GA-logs/demo-video-buffer-192'
+vid_input_dir = '/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_768-512-perturb0-debug'
+
+os.makedirs(os.path.join(output_dir, 'rgb'), exist_ok=True)
+os.makedirs(os.path.join(output_dir, 'normal'), exist_ok=True)
+
+
+
+all_vids = glob.glob(os.path.join(vid_input_dir, '*.mp4'))
+
+for vid_path in tqdm(all_vids[:]):
+    # if 'daily-used' in vid_path: # only on fancy categories.
+    #     continue
+    try:
+        save_2dgs_rgb_normal_vid(vid_path, output_dir)
+    except:
+        print(vid_path)
\ No newline at end of file
diff --git a/scripts/triplane_rec_inference.py b/scripts/triplane_rec_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fe44ec7d6df9d3299150df8d3cc65ec594a1a64
--- /dev/null
+++ b/scripts/triplane_rec_inference.py
@@ -0,0 +1,153 @@
+"""
+Train a diffusion model on images.
+"""
+import sys
+import os
+sys.path.append('.')
+
+import torch as th
+import torch.multiprocessing as mp
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+from nsr.train_util import TrainLoop3DRec as TrainLoop
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, create_Triplane, loss_defaults
+from datasets.shapenet import load_eval_rays, load_data, load_eval_data
+from nsr.losses.builder import E3DGELossClass
+
+from pdb import set_trace as st
+
+def inference_loop(rank, master_addr, master_port, args):
+    dist_util.setup_dist(rank, master_addr, master_port, args.gpus)
+
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating eval rays...")
+    # TODO, load shapenet data
+    eval_data = load_eval_data(
+        file_path=args.data_dir,
+        batch_size=args.batch_size,
+        reso=args.image_size,
+        reso_encoder=args.image_size_encoder, # 224 -> 128
+        num_workers=args.num_workers,
+        load_depth=args.depth_lambda > 0
+    )
+    # c_list = load_eval_rays(
+    #     file_path=args.data_dir,
+    #     reso=args.image_size,
+    #     reso_encoder=args.image_size_encoder, # 224 -> 128
+    # )
+
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+
+    logger.log("loading encoder and NSR decoder...") 
+    auto_encoder = create_Triplane( # basically overfitting tirplane
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    # auto_encoder = create_3DAE_model(
+    #     **args_to_dict(args,
+    #                    encoder_and_nsr_defaults().keys()))
+    auto_encoder.to(dist_util.dev())
+    auto_encoder.eval()
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()) )
+    loss_class = E3DGELossClass(dist_util.dev(), opt).to(dist_util.dev())
+
+    logger.log("training...")
+    TrainLoop(
+        model=auto_encoder,
+        # encoder,
+        # decoder
+        loss_class=loss_class,
+        # diffusion=diffusion,
+        data=eval_data, # TODO
+        # data=batch,
+        batch_size=args.batch_size,
+        microbatch=args.microbatch,
+        lr=args.lr,
+        ema_rate=args.ema_rate,
+        log_interval=args.log_interval,
+        save_interval=args.save_interval,
+        resume_checkpoint=args.resume_checkpoint,
+        use_fp16=args.use_fp16,
+        fp16_scale_growth=args.fp16_scale_growth,
+        weight_decay=args.weight_decay,
+        lr_anneal_steps=args.lr_anneal_steps,
+    ).eval_loop() # ! overfitting
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        num_workers=4,
+        local_rank=0,
+        gpus=1,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=10,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/eval",
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ[
+    #     "TORCH_DISTRIBUTED_DEBUG"
+    # ] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+
+    args = create_argparser().parse_args()
+    # st()
+
+    master_addr = '127.0.0.1'
+    master_port = dist_util._find_free_port()
+
+    # Launch processes.
+    print('Launching processes...')
+    th.multiprocessing.set_start_method('spawn')
+
+    subprocess_fn = inference_loop
+
+    # launch using torch.multiprocessing.spawn
+    if args.gpus == 1:
+        subprocess_fn(rank=0, master_addr=master_addr, master_port=master_port, args=args)
+    else:
+        th.multiprocessing.spawn(fn=subprocess_fn,
+                                    args=(master_addr, master_port,args),
+                                    nprocs=args.gpus)
+
diff --git a/scripts/triplane_rec_obaverse_train.py b/scripts/triplane_rec_obaverse_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..733c6bc1e81ca9ab0ac8992d86c2970557405fab
--- /dev/null
+++ b/scripts/triplane_rec_obaverse_train.py
@@ -0,0 +1,180 @@
+"""
+Train a diffusion model on images.
+"""
+import sys
+import os
+sys.path.append('.')
+
+import torch as th
+import torch.multiprocessing as mp
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+from nsr.train_util import TrainLoop3DTriplaneRec as TrainLoop
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, create_Triplane, loss_defaults
+from datasets.g_buffer_objaverse import load_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from pdb import set_trace as st
+
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
+
+import random
+import json
+import sys
+import os
+
+sys.path.append('.')
+import torch.distributed as dist
+
+import traceback
+
+
+# def training_loop(rank, master_addr, master_port, args):
+def training_loop(args):
+    # dist_util.setup_dist(rank, master_addr, master_port, args.gpus)
+    dist_util.setup_dist(args)
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    # print(args)
+    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=2)
+
+    logger.log("creating data loader...")
+    # TODO, load shapenet data
+    # data = load_data(
+    data = load_memory_data(
+        file_path=args.data_dir,
+        batch_size=args.batch_size,
+        reso=args.image_size,
+        reso_encoder=args.image_size_encoder, # 224 -> 128
+        num_workers=args.num_workers,
+        load_depth=args.depth_lambda > 0
+        # load_depth=True # for evaluation
+    )
+    eval_data = data
+    # eval_data = load_eval_data(
+    #     file_path=args.data_dir,
+    #     batch_size=args.eval_batch_size,
+    #     reso=args.image_size,
+    #     reso_encoder=args.image_size_encoder, # 224 -> 128
+    #     num_workers=args.num_workers,
+    #     load_depth=True # for evaluation
+    # )
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+    logger.log("creating encoder and NSR decoder...")
+
+    
+    auto_encoder = create_Triplane( # basically overfitting tirplane
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    # auto_encoder = create_3DAE_model(
+    #     **args_to_dict(args,
+    #                    encoder_and_nsr_defaults().keys()))
+    auto_encoder.to(dist_util.dev())
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()) )
+    loss_class = E3DGELossClass(dist_util.dev(), opt).to(dist_util.dev())
+
+    logger.log("training...")
+    TrainLoop(
+        rec_model=auto_encoder,
+        # encoder,
+        # decoder
+        loss_class=loss_class,
+        # diffusion=diffusion,
+        data=data,
+        eval_data=eval_data,
+        # data=batch,
+        batch_size=args.batch_size,
+        microbatch=args.microbatch,
+        lr=args.lr,
+        ema_rate=args.ema_rate,
+        log_interval=args.log_interval,
+        save_interval=args.save_interval,
+        resume_checkpoint=args.resume_checkpoint,
+        use_fp16=args.use_fp16,
+        fp16_scale_growth=args.fp16_scale_growth,
+        weight_decay=args.weight_decay,
+        lr_anneal_steps=args.lr_anneal_steps,
+        eval_interval=args.eval_interval,
+    ).run_loop() # ! overfitting
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        num_workers=4,
+        local_rank=0,
+        gpus=1,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=8,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=10,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        eval_interval=2500,
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ[
+    #     "TORCH_DISTRIBUTED_DEBUG"
+    # ] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    opts = args
+
+    args.rendering_kwargs = rendering_options_defaults(opts)
+
+    # Launch processes.
+    print('Launching processes...')
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup() # clean port and socket when ctrl+c
diff --git a/scripts/triplane_rec_train.py b/scripts/triplane_rec_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..d35d2d5e09fbf2e25e1e1c508901cc8f345f32ba
--- /dev/null
+++ b/scripts/triplane_rec_train.py
@@ -0,0 +1,174 @@
+"""
+Train a diffusion model on images.
+"""
+import sys
+import os
+sys.path.append('.')
+
+import torch as th
+import torch.multiprocessing as mp
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+from nsr.train_util import TrainLoop3DRec as TrainLoop
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, create_Triplane, loss_defaults
+from datasets.shapenet import load_data, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from pdb import set_trace as st
+import json
+
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
+
+import random
+import traceback
+import json
+import sys
+import os
+
+def training_loop(args):
+    # dist_util.setup_dist(rank, master_addr, master_port, args.gpus)
+    dist_util.setup_dist(args)
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    # print(args)
+    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=2)
+
+    logger.log("creating data loader...")
+    # TODO, load shapenet data
+    # data = load_data(
+    data = load_memory_data(
+        file_path=args.data_dir,
+        batch_size=args.batch_size,
+        reso=args.image_size,
+        reso_encoder=args.image_size_encoder, # 224 -> 128
+        num_workers=args.num_workers,
+        load_depth=args.depth_lambda > 0
+        # load_depth=True # for evaluation
+    )
+    eval_data = load_eval_data(
+        file_path=args.data_dir,
+        batch_size=args.eval_batch_size,
+        reso=args.image_size,
+        reso_encoder=args.image_size_encoder, # 224 -> 128
+        num_workers=args.num_workers,
+        load_depth=True # for evaluation
+    )
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+    logger.log("creating encoder and NSR decoder...")
+
+    
+    auto_encoder = create_Triplane( # basically overfitting tirplane
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    # auto_encoder = create_3DAE_model(
+    #     **args_to_dict(args,
+    #                    encoder_and_nsr_defaults().keys()))
+    auto_encoder.to(dist_util.dev())
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()) )
+    loss_class = E3DGELossClass(dist_util.dev(), opt).to(dist_util.dev())
+
+    logger.log("training...")
+    TrainLoop(
+        rec_model=auto_encoder,
+        # encoder,
+        # decoder
+        loss_class=loss_class,
+        # diffusion=diffusion,
+        data=data,
+        eval_data=eval_data,
+        # data=batch,
+        batch_size=args.batch_size,
+        microbatch=args.microbatch,
+        lr=args.lr,
+        ema_rate=args.ema_rate,
+        log_interval=args.log_interval,
+        save_interval=args.save_interval,
+        resume_checkpoint=args.resume_checkpoint,
+        use_fp16=args.use_fp16,
+        fp16_scale_growth=args.fp16_scale_growth,
+        weight_decay=args.weight_decay,
+        lr_anneal_steps=args.lr_anneal_steps,
+        eval_interval=5000
+    ).run_loop() # ! overfitting
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        num_workers=4,
+        local_rank=0,
+        gpus=1,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=8,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=10,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+
+if __name__ == "__main__":
+    # os.environ[
+    #     "TORCH_DISTRIBUTED_DEBUG"
+    # ] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    opts = args
+
+    args.rendering_kwargs = rendering_options_defaults(opts)
+
+    # Launch processes.
+    print('Launching processes...')
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup() # clean port and socket when ctrl+c
diff --git a/scripts/vit_triplane_cldm_train.py b/scripts/vit_triplane_cldm_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..35045dbeb5311fe46fb3cae7b3ca3be711679865
--- /dev/null
+++ b/scripts/vit_triplane_cldm_train.py
@@ -0,0 +1,367 @@
+"""
+Train a diffusion model on images.
+"""
+import json
+import sys
+import os
+
+sys.path.append('.')
+
+# from dnnlib import EasyDict
+import traceback
+
+import torch as th
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import numpy as np
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.resample import create_named_schedule_sampler
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+    continuous_diffusion_defaults,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+)
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+# from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
+from datasets.shapenet import load_data, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from torch_utils import legacy, misc
+from torch.utils.data import Subset
+from pdb import set_trace as st
+
+from dnnlib.util import EasyDict, InfiniteSampler
+# from .vit_triplane_train_FFHQ import init_dataset_kwargs
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+# from torch.utils.tensorboard import SummaryWriter
+
+SEED = 0
+
+
+def training_loop(args):
+    # def training_loop(args):
+    logger.log("dist setup...")
+
+    th.cuda.set_device(
+        args.local_rank)  # set this line to avoid extra memory on rank 0
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    dist_util.setup_dist(args)
+
+    # st() # mark
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating ViT encoder and NSR decoder...")
+    # st() # mark
+    device = dist_util.dev()
+
+    args.img_size = [args.image_size_encoder]
+
+    logger.log("creating model and diffusion...")
+    # * set denoise model args
+
+    if args.denoise_in_channels == -1:
+        args.diffusion_input_size = args.image_size_encoder
+        args.denoise_in_channels = args.out_chans
+        args.denoise_out_channels = args.out_chans
+    else:
+        assert args.denoise_out_channels != -1
+
+    # args.image_size = args.image_size_encoder  # 224, follow the triplane size
+
+    # if args.diffusion_input_size == -1:
+    # else:
+    # args.image_size = args.diffusion_input_size
+
+    denoise_model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args,
+                       model_and_diffusion_defaults().keys()))
+    denoise_model.to(dist_util.dev())
+    denoise_model.train()
+
+    opts = eg3d_options_default()
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    logger.log("creating encoder and NSR decoder...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    auto_encoder.to(device)
+    auto_encoder.eval()
+
+    # * load G_ema modules into autoencoder
+    # * clone G_ema.decoder to auto_encoder triplane
+    # logger.log("AE triplane decoder reuses G_ema decoder...")
+    # auto_encoder.decoder.register_buffer('w_avg', G_ema.backbone.mapping.w_avg)
+
+    # auto_encoder.decoder.triplane_decoder.decoder.load_state_dict(  # type: ignore
+    #     G_ema.decoder.state_dict())  # type: ignore
+
+    # set grad=False in this manner suppresses the DDP forward no grad error.
+    logger.log("freeze triplane decoder...")
+    for param in auto_encoder.decoder.triplane_decoder.parameters(
+    ):  # type: ignore
+        # for param in auto_encoder.decoder.triplane_decoder.decoder.parameters(): # type: ignore
+        param.requires_grad_(False)
+
+    # if args.sr_training:
+
+    #     logger.log("AE triplane decoder reuses G_ema SR module...")
+    #     # auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict(  # type: ignore
+    #     #     G_ema.superresolution.state_dict())  # type: ignore
+
+    #     # set grad=False in this manner suppresses the DDP forward no grad error.
+    # logger.log("freeze SR module...")
+    # for param in auto_encoder.decoder.superresolution.parameters(): # type: ignore
+    #     param.requires_grad_(False)
+
+    #     # del G_ema
+    #     th.cuda.empty_cache()
+
+    if args.cfg in ('afhq', 'ffhq'):
+
+        if args.sr_training:
+
+            logger.log("AE triplane decoder reuses G_ema SR module...")
+            auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict(  # type: ignore
+                G_ema.superresolution.state_dict())  # type: ignore
+
+            # set grad=False in this manner suppresses the DDP forward no grad error.
+            for param in auto_encoder.decoder.triplane_decoder.superresolution.parameters(
+            ):  # type: ignore
+                param.requires_grad_(False)
+
+        # ! load data
+        logger.log("creating eg3d data loader...")
+        training_set_kwargs, dataset_name = init_dataset_kwargs(
+            data=args.data_dir,
+            class_name='datasets.eg3d_dataset.ImageFolderDataset'
+        )  # only load pose here
+        # if args.cond and not training_set_kwargs.use_labels:
+        # raise Exception('check here')
+
+        # training_set_kwargs.use_labels = args.cond
+        training_set_kwargs.use_labels = True
+        training_set_kwargs.xflip = True
+        training_set_kwargs.random_seed = SEED
+        # desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
+
+        # * construct ffhq/afhq dataset
+        training_set = dnnlib.util.construct_class_by_name(
+            **training_set_kwargs)  # subclass of training.dataset.Dataset
+
+        training_set = dnnlib.util.construct_class_by_name(
+            **training_set_kwargs)  # subclass of training.dataset.Dataset
+
+        training_set_sampler = InfiniteSampler(
+            dataset=training_set,
+            rank=dist_util.get_rank(),
+            num_replicas=dist_util.get_world_size(),
+            seed=SEED)
+
+        data = iter(
+            th.utils.data.DataLoader(
+                dataset=training_set,
+                sampler=training_set_sampler,
+                batch_size=args.batch_size,
+                pin_memory=True,
+                num_workers=args.num_workers,
+            ))
+        #  prefetch_factor=2))
+
+        eval_data = th.utils.data.DataLoader(dataset=Subset(
+            training_set, np.arange(10)),
+                                             batch_size=args.eval_batch_size,
+                                             num_workers=1)
+
+    else:
+
+        logger.log("creating data loader...")
+        # TODO, load shapenet data
+        # data = load_data(
+        # st() mark
+        if args.overfitting:
+            logger.log("create overfitting memory dataset")
+            data = load_memory_data(
+                file_path=args.eval_data_dir,
+                batch_size=args.batch_size,
+                reso=args.image_size,
+                reso_encoder=args.image_size_encoder,  # 224 -> 128
+                num_workers=args.num_workers,
+                load_depth=True  # for evaluation
+            )
+        else:
+            logger.log("create all instances dataset")
+            # st() mark
+            data = load_data(
+                file_path=args.data_dir,
+                batch_size=args.batch_size,
+                reso=args.image_size,
+                reso_encoder=args.image_size_encoder,  # 224 -> 128
+                num_workers=args.num_workers,
+                load_depth=True,
+                preprocess=auto_encoder.preprocess,  # clip
+                dataset_size=args.dataset_size,
+                # load_depth=True # for evaluation
+            )
+            # st() mark
+        eval_data = load_eval_data(
+            file_path=args.eval_data_dir,
+            batch_size=args.eval_batch_size,
+            reso=args.image_size,
+            reso_encoder=args.image_size_encoder,  # 224 -> 128
+            num_workers=args.num_workers,
+            load_depth=True  # for evaluation
+        )
+
+    # let all processes sync up before starting with a new epoch of training
+
+    if dist_util.get_rank() == 0:
+        with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+            json.dump(vars(args), f, indent=2)
+
+    args.schedule_sampler = create_named_schedule_sampler(
+        args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    loss_class = E3DGELossClass(device, opt).to(device)
+
+    logger.log("training...")
+
+    TrainLoop = {
+        'adm': nsr.TrainLoop3DDiffusion,
+        'dit': nsr.TrainLoop3DDiffusionDiT,
+        'ssd': nsr.TrainLoop3DDiffusionSingleStage,
+        # 'ssd_cvD': nsr.TrainLoop3DDiffusionSingleStagecvD,
+        'ssd_cvD_sds': nsr.TrainLoop3DDiffusionSingleStagecvDSDS,
+        'ssd_cvd_sds_no_separate_sds_step':
+        nsr.TrainLoop3DDiffusionSingleStagecvDSDS_sdswithrec,
+        'vpsde_lsgm_noD': nsr.lsgm.TrainLoop3DDiffusionLSGM_noD,  # use vpsde
+        # 'vpsde_lsgm': nsr.TrainLoop3DDiffusionLSGM, # use vpsde
+        # 'vpsde': nsr.TrainLoop3DDiffusion_vpsde,
+    }[args.trainer_name]
+
+    if 'vpsde' in args.trainer_name:
+        sde_diffusion = make_sde_diffusion(
+            dnnlib.EasyDict(
+                args_to_dict(args,
+                             continuous_diffusion_defaults().keys())))
+        assert args.mixed_prediction, 'enable mixed_prediction by default'
+        logger.log('create VPSDE diffusion.')
+    else:
+        sde_diffusion = None
+
+    dist_util.synchronize()
+
+    TrainLoop(rec_model=auto_encoder,
+              denoise_model=denoise_model,
+              diffusion=diffusion,
+              sde_diffusion=sde_diffusion,
+              loss_class=loss_class,
+              data=data,
+              eval_data=eval_data,
+              **vars(args)).run_loop()
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        dataset_size=-1,
+        diffusion_input_size=-1,
+        trainer_name='adm',
+        use_amp=False,
+        triplane_scaling_divider=1.0,  # divide by this value
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        schedule_sampler="uniform",
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        resume_checkpoint_EG3D="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        load_submodule_name='',  # for loading pretrained auto_encoder model
+        ignore_resume_opt=False,
+        # freeze_ae=False,
+        denoised_ae=True,
+    )
+
+    defaults.update(model_and_diffusion_defaults())
+    defaults.update(continuous_diffusion_defaults())
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
+    # os.environ["NCCL_DEBUG"] = "INFO"
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    # opts = dnnlib.EasyDict(vars(args))  # compatiable with triplane original settings
+    # opts = args
+    args.rendering_kwargs = rendering_options_defaults(args)
+
+    # Launch processes.
+    logger.log('Launching processes...')
+
+    logger.log('Available devices ', th.cuda.device_count())
+    logger.log('Current cuda device ', th.cuda.current_device())
+    # logger.log('GPU Device name:', th.cuda.get_device_name(th.cuda.current_device()))
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup()  # clean port and socket when ctrl+c
diff --git a/scripts/vit_triplane_diffusion_sample.py b/scripts/vit_triplane_diffusion_sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4c2c739bcb26512d86151ea1181dea8faef98c7
--- /dev/null
+++ b/scripts/vit_triplane_diffusion_sample.py
@@ -0,0 +1,387 @@
+"""
+Generate a large batch of image samples from a model and save them as a large
+numpy array. This can be used to produce samples for FID evaluation.
+"""
+
+import argparse
+import json
+import sys
+import os
+
+sys.path.append('.')
+
+from pdb import set_trace as st
+import imageio
+import numpy as np
+import torch as th
+import torch.distributed as dist
+
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    NUM_CLASSES,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+    add_dict_to_argparser,
+    args_to_dict,
+    continuous_diffusion_defaults,
+    control_net_defaults,
+)
+
+th.backends.cuda.matmul.allow_tf32 = True
+th.backends.cudnn.allow_tf32 = True
+th.backends.cudnn.enabled = True
+
+from pathlib import Path
+
+from tqdm import tqdm, trange
+import dnnlib
+from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, AE_with_Diffusion, rendering_options_defaults, eg3d_options_default, dataset_defaults
+
+from datasets.shapenet import load_eval_data
+from torch.utils.data import Subset
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+SEED = 0
+
+
+def main(args):
+
+    # args.rendering_kwargs = rendering_options_defaults(args)
+
+    dist_util.setup_dist(args)
+    logger.configure(dir=args.logdir)
+
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    # * set denoise model args
+    logger.log("creating model and diffusion...")
+    args.img_size = [args.image_size_encoder]
+    # ! no longer required for LDM
+    # args.denoise_in_channels = args.out_chans
+    # args.denoise_out_channels = args.out_chans
+    args.image_size = args.image_size_encoder  # 224, follow the triplane size
+
+    denoise_model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args,
+                       model_and_diffusion_defaults().keys()))
+
+    if 'cldm' in args.trainer_name:
+        assert isinstance(denoise_model, tuple)
+        denoise_model, controlNet = denoise_model
+
+        controlNet.to(dist_util.dev())
+        controlNet.train()
+    else:
+        controlNet = None
+
+    opts = eg3d_options_default()
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    # denoise_model.load_state_dict(
+    #     dist_util.load_state_dict(args.ddpm_model_path, map_location="cpu"))
+    denoise_model.to(dist_util.dev())
+    if args.use_fp16:
+        denoise_model.convert_to_fp16()
+    denoise_model.eval()
+
+    # * auto-encoder reconstruction model
+    logger.log("creating 3DAE...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    # logger.log("AE triplane decoder reuses G_ema decoder...")
+    # auto_encoder.decoder.register_buffer('w_avg', G_ema.backbone.mapping.w_avg)
+
+    # print(auto_encoder.decoder.w_avg.shape) # [512]
+
+    # auto_encoder.load_state_dict(
+    #     dist_util.load_state_dict(args.rec_model_path, map_location="cpu"))
+
+    auto_encoder.to(dist_util.dev())
+    auto_encoder.eval()
+
+    # TODO, how to set the scale?
+    logger.log("create dataset")
+
+    if args.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data
+    else:  # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data
+
+    if args.cfg in ('afhq', 'ffhq'):
+        # ! load data
+        logger.log("creating eg3d data loader...")
+        training_set_kwargs, dataset_name = init_dataset_kwargs(
+            data=args.data_dir,
+            class_name='datasets.eg3d_dataset.ImageFolderDataset'
+        )  # only load pose here
+        # if args.cond and not training_set_kwargs.use_labels:
+        # raise Exception('check here')
+
+        # training_set_kwargs.use_labels = args.cond
+        training_set_kwargs.use_labels = True
+        training_set_kwargs.xflip = True
+        training_set_kwargs.random_seed = SEED
+        # desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
+
+        # * construct ffhq/afhq dataset
+        training_set = dnnlib.util.construct_class_by_name(
+            **training_set_kwargs)  # subclass of training.dataset.Dataset
+
+        training_set = dnnlib.util.construct_class_by_name(
+            **training_set_kwargs)  # subclass of training.dataset.Dataset
+
+        # training_set_sampler = InfiniteSampler(
+        #     dataset=training_set,
+        #     rank=dist_util.get_rank(),
+        #     num_replicas=dist_util.get_world_size(),
+        #     seed=SEED)
+
+        # data = iter(
+        #     th.utils.data.DataLoader(dataset=training_set,
+        #                             sampler=training_set_sampler,
+        #                             batch_size=args.batch_size,
+        #                             pin_memory=True,
+        #                             num_workers=args.num_workers,))
+        #                             #  prefetch_factor=2))
+
+        eval_data = th.utils.data.DataLoader(dataset=Subset(
+            training_set, np.arange(25)),
+                                             batch_size=args.eval_batch_size,
+                                             num_workers=1)
+
+    else:
+
+        logger.log("creating data loader...")
+
+        if args.use_wds:
+            if args.eval_data_dir == 'NONE':
+                with open(args.eval_shards_lst) as f:
+                    eval_shards_lst = [url.strip() for url in f.readlines()]
+            else:
+                eval_shards_lst = args.eval_data_dir  # auto expanded
+
+            eval_data = load_wds_data(
+                eval_shards_lst, args.image_size, args.image_size_encoder,
+                args.eval_batch_size, args.num_workers,
+                **args_to_dict(args,
+                               dataset_defaults().keys()))
+
+        else:
+            eval_data = load_eval_data(
+                file_path=args.eval_data_dir,
+                batch_size=args.eval_batch_size,
+                reso=args.image_size,
+                reso_encoder=args.image_size_encoder,  # 224 -> 128
+                num_workers=args.num_workers,
+                # load_depth=True,  # for evaluation
+                **args_to_dict(args,
+                               dataset_defaults().keys()))
+
+    TrainLoop = {
+        'adm': nsr.TrainLoop3DDiffusion,
+        'dit': nsr.TrainLoop3DDiffusionDiT,
+        # lsgm
+        'ssd': nsr.TrainLoop3DDiffusionSingleStage,
+        # 'ssd_cvD': nsr.TrainLoop3DDiffusionSingleStagecvD,
+        'ssd_cvD_sds': nsr.TrainLoop3DDiffusionSingleStagecvDSDS,
+        'ssd_cvd_sds_no_separate_sds_step':
+        nsr.TrainLoop3DDiffusionSingleStagecvDSDS_sdswithrec,
+        # 'ssd_cvD': nsr.TrainLoop3DDiffusionSingleStagecvD,
+        'ssd_cvD_sds': nsr.TrainLoop3DDiffusionSingleStagecvDSDS,
+        'vpsde_lsgm_noD': nsr.lsgm.TrainLoop3DDiffusionLSGM_noD,  # use vpsde
+        'vpsde_cldm': nsr.lsgm.TrainLoop3DDiffusionLSGM_Control,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_cvD,
+        'vpsde_lsgm_joint_noD':
+        nsr.lsgm.TrainLoop3DDiffusionLSGMJointnoD,  # use vpsde
+        'vpsde_lsgm_joint_noD_ponly':
+        nsr.lsgm.TrainLoop3DDiffusionLSGMJointnoD_ponly,  # use vpsde
+        'vpsde_crossattn': nsr.lsgm.TrainLoop3DDiffusionLSGM_crossattn,
+        'vpsde_ldm': nsr.lsgm.TrainLoop3D_LDM,
+        'sgm_legacy':
+        nsr.lsgm.sgm_DiffusionEngine.DiffusionEngineLSGM,
+    }[args.trainer_name]
+
+    # continuous
+    if 'vpsde' in args.trainer_name:
+        sde_diffusion = make_sde_diffusion(
+            dnnlib.EasyDict(
+                args_to_dict(args,
+                             continuous_diffusion_defaults().keys())))
+        # assert args.mixed_prediction, 'enable mixed_prediction by default'
+        logger.log('create VPSDE diffusion.')
+    else:
+        sde_diffusion = None
+
+    # if 'cldm' in args.trainer_name:
+    #     assert isinstance(denoise_model, tuple)
+    #     denoise_model, controlNet = denoise_model
+
+    #     controlNet.to(dist_util.dev())
+    #     controlNet.train()
+    # else:
+    #     controlNet = None
+    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
+
+    training_loop_class = TrainLoop(rec_model=auto_encoder,
+                                    denoise_model=denoise_model,
+                                    control_model=controlNet,
+                                    diffusion=diffusion,
+                                    sde_diffusion=sde_diffusion,
+                                    loss_class=None,
+                                    data=None,
+                                    eval_data=eval_data,
+                                    **vars(args))
+
+    logger.log("sampling...")
+    dist_util.synchronize()
+
+    # all_images = []
+    # all_labels = []
+    # while len(all_images) * args.batch_size < args.num_samples:
+
+    if dist_util.get_rank() == 0:
+
+        (Path(logger.get_dir()) / 'FID_Cals').mkdir(exist_ok=True,
+                                                    parents=True)
+
+        with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+            json.dump(vars(args), f, indent=2)
+
+        # ! use pre-saved camera pose
+        camera = th.load('eval_pose.pt', map_location=dist_util.dev())[:]
+
+        # for sample_idx in trange(args.num_samples):
+        model_kwargs = {}
+
+        # if args.class_cond:
+        #     classes = th.randint(low=0,
+        #                          high=NUM_CLASSES,
+        #                          size=(args.batch_size, ),
+        #                          device=dist_util.dev())
+        #     model_kwargs["y"] = classes
+        # training_loop_class.step = sample_idx  # save to different position
+        # if args.create_controlnet or 'crossattn' in args.trainer_name:
+
+        training_loop_class.eval_cldm(
+            prompt=args.prompt,
+            unconditional_guidance_scale=args.
+            unconditional_guidance_scale,
+            use_ddim=args.use_ddim,
+            save_img=args.save_img,
+            use_train_trajectory=args.use_train_trajectory,
+            camera=camera,
+            num_instances=args.num_instances,
+            num_samples=args.num_samples,
+            # training_loop_class.rec_model,
+            # training_loop_class.ddpm_model
+        )
+
+
+    dist.barrier()
+    logger.log("sampling complete")
+
+
+def create_argparser():
+    defaults = dict(
+        image_size_encoder=224,
+        triplane_scaling_divider=1.0,  # divide by this value
+        diffusion_input_size=-1,
+        trainer_name='adm',
+        use_amp=False,
+        # triplane_scaling_divider=1.0, # divide by this value
+
+        # * sampling flags
+        clip_denoised=False,
+        num_samples=10,
+        num_instances=10, # for i23d, loop different condition
+        use_ddim=False,
+        ddpm_model_path="",
+        cldm_model_path="",
+        rec_model_path="",
+
+        # * eval logging flags
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        data_dir="",
+        eval_data_dir="",
+        eval_batch_size=1,
+        num_workers=1,
+
+        # * training flags for loading TrainingLoop class
+        overfitting=False,
+        image_size=128,
+        iterations=150000,
+        schedule_sampler="uniform",
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        resume_cldm_checkpoint="",
+        resume_checkpoint_EG3D="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        load_submodule_name='',  # for loading pretrained auto_encoder model
+        ignore_resume_opt=False,
+        freeze_ae=False,
+        denoised_ae=True,
+        # inference prompt
+        prompt="a red chair",
+        interval=1,
+        save_img=False,
+        use_train_trajectory=
+        False,  # use train trajectory to sample images for fid calculation
+        unconditional_guidance_scale=1.0,
+        use_eos_feature=False,
+    )
+
+    defaults.update(model_and_diffusion_defaults())
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+    defaults.update(continuous_diffusion_defaults())
+    defaults.update(control_net_defaults())
+    defaults.update(dataset_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+
+    # os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
+    # os.environ["NCCL_DEBUG"] = "INFO"
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    args = create_argparser().parse_args()
+
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    args.rendering_kwargs = rendering_options_defaults(args)
+
+    main(args)
diff --git a/scripts/vit_triplane_diffusion_train.py b/scripts/vit_triplane_diffusion_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c945f5bae5218ff17ff395bf4f4d4dce3048a71
--- /dev/null
+++ b/scripts/vit_triplane_diffusion_train.py
@@ -0,0 +1,514 @@
+"""
+Train a diffusion model on images.
+"""
+import json
+import sys
+import os
+
+sys.path.append('.')
+
+# from dnnlib import EasyDict
+import traceback
+
+import torch as th
+from xformers.triton import FusedLayerNorm as LayerNorm
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import numpy as np
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.resample import create_named_schedule_sampler
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+    continuous_diffusion_defaults,
+    control_net_defaults,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+)
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+# from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+
+from datasets.eg3d_dataset import LMDBDataset_MV_Compressed_eg3d
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default, dataset_defaults
+from datasets.shapenet import load_data, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from torch_utils import legacy, misc
+from torch.utils.data import Subset
+from pdb import set_trace as st
+
+from dnnlib.util import EasyDict, InfiniteSampler
+# from .vit_triplane_train_FFHQ import init_dataset_kwargs
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+th.backends.cudnn.enabled = True # https://zhuanlan.zhihu.com/p/635824460
+th.backends.cudnn.benchmark = True
+
+# from torch.utils.tensorboard import SummaryWriter
+
+SEED = 0
+
+
+def training_loop(args):
+    # def training_loop(args):
+    logger.log("dist setup...")
+    # th.multiprocessing.set_start_method('spawn')
+    th.autograd.set_detect_anomaly(False) # type: ignore
+    # th.autograd.set_detect_anomaly(True)  # type: ignore
+    # st()
+
+    th.cuda.set_device(
+        args.local_rank)  # set this line to avoid extra memory on rank 0
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    dist_util.setup_dist(args)
+
+    # st() # mark
+
+    th.backends.cuda.matmul.allow_tf32 = args.allow_tf32
+    th.backends.cudnn.allow_tf32 = args.allow_tf32
+    # st()
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating ViT encoder and NSR decoder...")
+    # st() # mark
+    device = dist_util.dev()
+
+    args.img_size = [args.image_size_encoder]
+
+    logger.log("creating model and diffusion...")
+    # * set denoise model args
+
+    if args.denoise_in_channels == -1:
+        args.diffusion_input_size = args.image_size_encoder
+        args.denoise_in_channels = args.out_chans
+        args.denoise_out_channels = args.out_chans
+    else:
+        assert args.denoise_out_channels != -1
+
+    # args.image_size = args.image_size_encoder  # 224, follow the triplane size
+
+    # if args.diffusion_input_size == -1:
+    # else:
+    # args.image_size = args.diffusion_input_size
+
+    if args.pred_type == 'v':  # for lsgm training
+        assert args.predict_v == True  # for DDIM sampling
+    
+    # if not args.create_dit:
+
+    denoise_model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args,
+                       model_and_diffusion_defaults().keys()))
+
+    opts = eg3d_options_default()
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    logger.log("creating encoder and NSR decoder...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    auto_encoder.to(device)
+    auto_encoder.eval()
+
+    # * load G_ema modules into autoencoder
+    # * clone G_ema.decoder to auto_encoder triplane
+    # logger.log("AE triplane decoder reuses G_ema decoder...")
+    # auto_encoder.decoder.register_buffer('w_avg', G_ema.backbone.mapping.w_avg)
+
+    # auto_encoder.decoder.triplane_decoder.decoder.load_state_dict(  # type: ignore
+    #     G_ema.decoder.state_dict())  # type: ignore
+
+    # set grad=False in this manner suppresses the DDP forward no grad error.
+
+    # if args.sr_training:
+
+    #     logger.log("AE triplane decoder reuses G_ema SR module...")
+    #     # auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict(  # type: ignore
+    #     #     G_ema.superresolution.state_dict())  # type: ignore
+
+    #     # set grad=False in this manner suppresses the DDP forward no grad error.
+    # logger.log("freeze SR module...")
+    # for param in auto_encoder.decoder.superresolution.parameters(): # type: ignore
+    #     param.requires_grad_(False)
+
+    #     # del G_ema
+    #     th.cuda.empty_cache()
+
+    if args.freeze_triplane_decoder:
+        logger.log("freeze triplane decoder...")
+        for param in auto_encoder.decoder.triplane_decoder.parameters(
+        ):  # type: ignore
+            # for param in auto_encoder.decoder.triplane_decoder.decoder.parameters(): # type: ignore
+            param.requires_grad_(False)
+
+    if args.cfg in ('afhq', 'ffhq'):
+
+        if args.sr_training:
+
+            logger.log("AE triplane decoder reuses G_ema SR module...")
+            auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict(  # type: ignore
+                G_ema.superresolution.state_dict())  # type: ignore
+
+            # set grad=False in this manner suppresses the DDP forward no grad error.
+            for param in auto_encoder.decoder.triplane_decoder.superresolution.parameters(
+            ):  # type: ignore
+                param.requires_grad_(False)
+
+        # ! load data
+        if args.use_lmdb:
+            logger.log("creating LMDB eg3d data loader...")
+            training_set = LMDBDataset_MV_Compressed_eg3d(
+                args.data_dir,
+                args.image_size,
+                args.image_size_encoder,
+            )
+        else:
+            logger.log("creating eg3d data loader...")
+
+            training_set_kwargs, dataset_name = init_dataset_kwargs(
+                data=args.data_dir,
+                class_name='datasets.eg3d_dataset.ImageFolderDataset',
+                reso_gt=args.image_size)  # only load pose here
+            # if args.cond and not training_set_kwargs.use_labels:
+            # raise Exception('check here')
+
+            # training_set_kwargs.use_labels = args.cond
+            training_set_kwargs.use_labels = True
+            training_set_kwargs.xflip = False
+            training_set_kwargs.random_seed = SEED
+            training_set_kwargs.max_size = args.dataset_size
+            # desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
+
+            # * construct ffhq/afhq dataset
+            training_set = dnnlib.util.construct_class_by_name(
+                **training_set_kwargs)  # subclass of training.dataset.Dataset
+
+        training_set_sampler = InfiniteSampler(
+            dataset=training_set,
+            rank=dist_util.get_rank(),
+            num_replicas=dist_util.get_world_size(),
+            seed=SEED)
+
+        data = iter(
+            th.utils.data.DataLoader(
+                dataset=training_set,
+                sampler=training_set_sampler,
+                batch_size=args.batch_size,
+                pin_memory=True,
+                num_workers=args.num_workers,
+                persistent_workers=args.num_workers > 0,
+                prefetch_factor=max(8 // args.batch_size, 2),
+            ))
+        #  prefetch_factor=2))
+
+        eval_data = th.utils.data.DataLoader(dataset=Subset(
+            training_set, np.arange(8)),
+                                             batch_size=args.eval_batch_size,
+                                             num_workers=1)
+
+    else:
+
+        logger.log("creating data loader...")
+
+        if args.objv_dataset:
+            from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data
+        else:  # shapenet
+            from datasets.shapenet import load_data, load_eval_data, load_memory_data
+
+        # TODO, load shapenet data
+        # data = load_data(
+        # st() mark
+        # if args.overfitting:
+        #     logger.log("create overfitting memory dataset")
+        #     data = load_memory_data(
+        #         file_path=args.eval_data_dir,
+        #         batch_size=args.batch_size,
+        #         reso=args.image_size,
+        #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+        #         num_workers=args.num_workers,
+        #         load_depth=True  # for evaluation
+        #     )
+        # else:
+        if args.use_wds:
+            if args.data_dir == 'NONE':
+                with open(args.shards_lst) as f:
+                    shards_lst = [url.strip() for url in f.readlines()]
+                data = load_wds_data(
+                    shards_lst, args.image_size, args.image_size_encoder,
+                    args.batch_size, args.num_workers,
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+
+            else:
+                data = load_wds_data(
+                    args.data_dir, args.image_size, args.image_size_encoder,
+                    args.batch_size, args.num_workers,
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+
+                # eval_data = load_wds_data(
+                #     args.data_dir,
+                #     args.image_size,
+                #     args.image_size_encoder,
+                #     args.eval_batch_size,
+                #     args.num_workers,
+                #     decode_encode_img_only=args.decode_encode_img_only,
+                #     load_wds_diff=args.load_wds_diff)
+
+            if args.eval_data_dir == 'NONE':
+                with open(args.eval_shards_lst) as f:
+                    eval_shards_lst = [url.strip() for url in f.readlines()]
+            else:
+                eval_shards_lst = args.eval_data_dir  # auto expanded
+
+            eval_data = load_wds_data(
+                eval_shards_lst,
+                args.image_size,
+                args.image_size_encoder,
+                args.eval_batch_size,
+                args.num_workers,
+                plucker_embedding=args.plucker_embedding,
+                decode_encode_img_only=args.decode_encode_img_only,
+                mv_input=args.mv_input,
+                load_wds_diff=False,
+                load_instance=True)
+
+        else:
+            logger.log("create all instances dataset")
+            # st() mark
+            data = load_data(
+                file_path=args.data_dir,
+                batch_size=args.batch_size,
+                reso=args.image_size,
+                reso_encoder=args.image_size_encoder,  # 224 -> 128
+                num_workers=args.num_workers,
+                **args_to_dict(args,
+                                dataset_defaults().keys())
+                # load_depth=args.load_depth,
+                # preprocess=auto_encoder.preprocess,  # clip
+                # dataset_size=args.dataset_size,
+                # use_lmdb=args.use_lmdb,
+                # trainer_name=args.trainer_name,
+                # load_depth=True # for evaluation
+            )
+            eval_data = data
+            # eval_data = load_eval_data(
+            #     file_path=args.eval_data_dir,
+            #     batch_size=args.eval_batch_size,
+            #     reso=args.image_size,
+            #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+            #     num_workers=args.num_workers,
+            #     load_depth=True,  # for evaluation
+            #     interval=args.interval,
+            #     use_lmdb=args.use_lmdb,
+            # )
+
+    # let all processes sync up before starting with a new epoch of training
+
+    if dist_util.get_rank() == 0:
+        with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+            json.dump(vars(args), f, indent=2)
+
+    args.schedule_sampler = create_named_schedule_sampler(
+        args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    loss_class = E3DGELossClass(device, opt).to(device)
+
+    logger.log("training...")
+
+    TrainLoop = {
+        'adm':
+        nsr.TrainLoop3DDiffusion,
+        'dit':
+        nsr.TrainLoop3DDiffusionDiT,
+        'ssd':
+        nsr.TrainLoop3DDiffusionSingleStage,
+        # 'ssd_cvD': nsr.TrainLoop3DDiffusionSingleStagecvD,
+        'ssd_cvD_sds':
+        nsr.TrainLoop3DDiffusionSingleStagecvDSDS,
+        'ssd_cvd_sds_no_separate_sds_step':
+        nsr.TrainLoop3DDiffusionSingleStagecvDSDS_sdswithrec,
+        'vpsde_lsgm_noD':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_noD,  # use vpsde
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_cvD,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD_scaling':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_cvD_scaling,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_weightingv0':
+        nsr.lsgm.
+        TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_weightingv0,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED':
+        nsr.lsgm.
+        TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED,
+        'vpsde_TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED_nv':
+        nsr.lsgm.
+        TrainLoop3DDiffusionLSGM_cvD_scaling_lsgm_unfreezeD_iterativeED_nv,
+        'vpsde_lsgm_joint_noD':
+        nsr.lsgm.TrainLoop3DDiffusionLSGMJointnoD,  # use vpsde
+        'vpsde_lsgm_joint_noD_ponly':
+        nsr.lsgm.TrainLoop3DDiffusionLSGMJointnoD_ponly,  # use vpsde
+        # control
+        'vpsde_cldm':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_Control,
+        'vpsde_crossattn':
+        nsr.lsgm.TrainLoop3DDiffusionLSGM_crossattn,
+        'vpsde_crossattn_cldm':
+        nsr.lsgm.crossattn_cldm.TrainLoop3DDiffusionLSGM_crossattn_controlNet,
+        'vpsde_ldm':
+        nsr.lsgm.TrainLoop3D_LDM,
+        'sgm_legacy':
+        nsr.lsgm.sgm_DiffusionEngine.DiffusionEngineLSGM,
+    }[args.trainer_name]
+
+    if 'vpsde' in args.trainer_name:
+        sde_diffusion = make_sde_diffusion(
+            dnnlib.EasyDict(
+                args_to_dict(args,
+                             continuous_diffusion_defaults().keys())))
+        # assert args.mixed_prediction, 'enable mixed_prediction by default'
+        logger.log('create VPSDE diffusion.')
+    else:
+        sde_diffusion = None
+
+    if 'cldm' in args.trainer_name:
+        assert isinstance(denoise_model, tuple)
+        denoise_model, controlNet = denoise_model
+
+        controlNet.to(dist_util.dev())
+        controlNet.train()
+    else:
+        controlNet = None
+
+    # st()
+    denoise_model.to(dist_util.dev())
+    denoise_model.train()
+
+    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
+    TrainLoop(rec_model=auto_encoder,
+              denoise_model=denoise_model,
+              control_model=controlNet,
+              diffusion=diffusion,
+              sde_diffusion=sde_diffusion,
+              loss_class=loss_class,
+              data=data,
+              eval_data=eval_data,
+              **vars(args)).run_loop()
+
+    dist_util.synchronize()
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        dataset_size=-1,
+        diffusion_input_size=-1,
+        trainer_name='adm',
+        use_amp=False,
+        train_vae=True,  # jldm?
+        triplane_scaling_divider=1.0,  # divide by this value
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        schedule_sampler="uniform",
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        resume_checkpoint_EG3D="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        load_depth=True,  # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        load_submodule_name='',  # for loading pretrained auto_encoder model
+        ignore_resume_opt=False,
+        # freeze_ae=False,
+        denoised_ae=True,
+        diffusion_ce_anneal=False,
+        use_lmdb=False,
+        interval=1,
+        freeze_triplane_decoder=False,
+        objv_dataset=False,
+        use_eos_feature=False,
+        clip_grad_throld=1.0,
+        allow_tf32=True,
+    )
+
+    defaults.update(model_and_diffusion_defaults())
+    defaults.update(continuous_diffusion_defaults())
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(dataset_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+    defaults.update(control_net_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
+    # os.environ["NCCL_DEBUG"] = "INFO"
+    th.multiprocessing.set_start_method('spawn')
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    # opts = dnnlib.EasyDict(vars(args))  # compatiable with triplane original settings
+    # opts = args
+    args.rendering_kwargs = rendering_options_defaults(args)
+
+    # Launch processes.
+    logger.log('Launching processes...')
+
+    logger.log('Available devices ', th.cuda.device_count())
+    logger.log('Current cuda device ', th.cuda.current_device())
+    # logger.log('GPU Device name:', th.cuda.get_device_name(th.cuda.current_device()))
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup()  # clean port and socket when ctrl+c
diff --git a/scripts/vit_triplane_inference.py b/scripts/vit_triplane_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b752a0734261d85e363b13f8865f095ee86f4d4
--- /dev/null
+++ b/scripts/vit_triplane_inference.py
@@ -0,0 +1,189 @@
+"""
+Train a diffusion model on images.
+"""
+import sys
+import os
+
+sys.path.append('.')
+import torch.distributed as dist
+
+import torch as th
+import torch.multiprocessing as mp
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+from nsr.train_util import TrainLoop3DRec as TrainLoop
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults
+from datasets.shapenet import load_data, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from pdb import set_trace as st
+
+th.backends.cuda.matmul.allow_tf32 = True
+th.backends.cudnn.allow_tf32 = True
+th.backends.cudnn.enabled = True
+
+SEED = 0
+
+
+def training_loop(args):
+    # def training_loop(args):
+    dist_util.setup_dist(args)
+
+    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
+    print(f"{args.local_rank=} init complete")
+    th.cuda.set_device(args.local_rank)
+
+    th.cuda.manual_seed_all(SEED)
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating data loader...")
+    # TODO, load shapenet data
+    # data = load_data(
+    # if args.overfitting:
+    #     data = load_memory_data(
+    #         file_path=args.data_dir,
+    #         batch_size=args.batch_size,
+    #         reso=args.image_size,
+    #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #         num_workers=args.num_workers,
+    #         load_depth=args.depth_lambda > 0
+    #         # load_depth=True # for evaluation
+    #     )
+    # else:
+    #     data = load_data(
+    #         file_path=args.data_dir,
+    #         batch_size=args.batch_size,
+    #         reso=args.image_size,
+    #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #         num_workers=args.num_workers,
+    #         load_depth=args.depth_lambda > 0
+    #         # load_depth=True # for evaluation
+    #     )
+    eval_data = load_eval_data(
+        file_path=args.data_dir,
+        batch_size=args.eval_batch_size,
+        reso=args.image_size,
+        reso_encoder=args.image_size_encoder,  # 224 -> 128
+        num_workers=args.num_workers,
+        load_depth=True  # for evaluation
+    )
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+    logger.log("creating encoder and NSR decoder...")
+    # device = dist_util.dev()
+    device = th.device("cuda", args.local_rank)
+
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+    auto_encoder.to(device)
+    auto_encoder.eval()
+
+    # dist_util.sync_params(auto_encoder.named_parameters())
+
+    # auto_encoder.train()
+
+    # let all processes sync up before starting with a new epoch of training
+    dist_util.synchronize()
+
+    # noise = th.randn(1, 14 * 14, 384).to(device) # B, L, C
+    # noise = th.randn(1, 3,224,224).to(device)
+    # img = auto_encoder(noise, th.zeros(1, 25).to(device))
+    # print(img['image'].shape)
+
+    # if dist_util.get_rank()==0:
+    #     print(auto_encoder)
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    loss_class = E3DGELossClass(device, opt).to(device)
+
+    # logger.log("training...")
+    TrainLoop(
+        rec_model=auto_encoder,
+        loss_class=loss_class,
+        # diffusion=diffusion,
+        data=None,
+        eval_interval=-1,
+        eval_data=eval_data,
+        # data=batch,
+        batch_size=args.batch_size,
+        microbatch=args.microbatch,
+        lr=args.lr,
+        ema_rate=args.ema_rate,
+        log_interval=args.log_interval,
+        save_interval=args.save_interval,
+        resume_checkpoint=args.resume_checkpoint,
+        resume_cldm_checkpoint=args.resume_cldm_checkpoint,
+        use_fp16=args.use_fp16,
+        fp16_scale_growth=args.fp16_scale_growth,
+        weight_decay=args.weight_decay,
+        lr_anneal_steps=args.lr_anneal_steps,
+    ).eval_loop()  # ! overfitting
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=8,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=10,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+    os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+
+    master_addr = '127.0.0.1'
+    master_port = dist_util._find_free_port()
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+    args.master_addr = master_addr
+    args.master_port = master_port
+
+    # Launch processes.
+    print('Launching processes...')
+    training_loop(args)
diff --git a/scripts/vit_triplane_sit_sample.py b/scripts/vit_triplane_sit_sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..33e8b0e72cb684fea410d4f5183bb03940869b7f
--- /dev/null
+++ b/scripts/vit_triplane_sit_sample.py
@@ -0,0 +1,398 @@
+"""
+Train a diffusion model on images.
+"""
+import json
+import sys
+import os
+
+sys.path.append('.')
+
+# from dnnlib import EasyDict
+import traceback
+
+import torch as th
+# from xformers.triton import FusedLayerNorm as LayerNorm
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import numpy as np
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.resample import create_named_schedule_sampler
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+    continuous_diffusion_defaults,
+    control_net_defaults,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+)
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+# from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+
+from datasets.eg3d_dataset import LMDBDataset_MV_Compressed_eg3d
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default, dataset_defaults
+from datasets.shapenet import load_data, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from torch_utils import legacy, misc
+from torch.utils.data import Subset
+from pdb import set_trace as st
+
+from dnnlib.util import EasyDict, InfiniteSampler
+# from .vit_triplane_train_FFHQ import init_dataset_kwargs
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+th.backends.cudnn.enabled = True # https://zhuanlan.zhihu.com/p/635824460
+th.backends.cudnn.benchmark = True
+
+from transport import create_transport, Sampler
+from transport.train_utils import parse_transport_args
+from nsr.camera_utils import generate_input_camera, uni_mesh_path, sample_uniform_cameras_on_sphere
+
+# from torch.utils.tensorboard import SummaryWriter
+
+SEED = 0
+
+
+
+def training_loop(args):
+    # def training_loop(args):
+    logger.log("dist setup...")
+    # th.multiprocessing.set_start_method('spawn')
+    th.autograd.set_detect_anomaly(False) # type: ignore
+    # th.autograd.set_detect_anomaly(True)  # type: ignore
+    # st()
+
+    th.cuda.set_device(
+        args.local_rank)  # set this line to avoid extra memory on rank 0
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    dist_util.setup_dist(args)
+
+    # st() # mark
+
+    th.backends.cuda.matmul.allow_tf32 = args.allow_tf32
+    th.backends.cudnn.allow_tf32 = args.allow_tf32
+    # st()
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating ViT encoder and NSR decoder...")
+    # st() # mark
+    device = dist_util.dev()
+
+    args.img_size = [args.image_size_encoder]
+
+    logger.log("creating model and diffusion...")
+    # * set denoise model args
+
+    if args.denoise_in_channels == -1:
+        args.diffusion_input_size = args.image_size_encoder
+        args.denoise_in_channels = args.out_chans
+        args.denoise_out_channels = args.out_chans
+    else:
+        assert args.denoise_out_channels != -1
+
+    # args.image_size = args.image_size_encoder  # 224, follow the triplane size
+
+    # if args.diffusion_input_size == -1:
+    # else:
+    # args.image_size = args.diffusion_input_size
+
+    if args.pred_type == 'v':  # for lsgm training
+        assert args.predict_v == True  # for DDIM sampling
+    
+    # if not args.create_dit:
+
+    denoise_model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args,
+                       model_and_diffusion_defaults().keys()))
+
+    opts = eg3d_options_default()
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    logger.log("creating encoder and NSR decoder...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    auto_encoder.to(device)
+    auto_encoder.eval()
+
+    logger.log("creating data loader...")
+
+    if args.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data, load_data_cls
+    else:  # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data
+
+    if args.i23d:
+        data = load_eval_data(
+            file_path=args.eval_data_dir,
+            batch_size=args.eval_batch_size,
+            reso=args.image_size,
+            reso_encoder=args.image_size_encoder,  # 224 -> 128
+            num_workers=args.num_workers,
+            load_depth=True,  # for evaluation
+            preprocess=auto_encoder.preprocess,
+            **args_to_dict(args,
+                            dataset_defaults().keys()))
+    else:
+        data = None # t23d sampling, only caption required
+
+    # eval_dataset = load_data_cls(
+    #     file_path=args.data_dir,
+    #     batch_size=args.batch_size,
+    #     reso=args.image_size,
+    #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #     num_workers=args.num_workers,
+    #     load_latent=True,
+    #     return_dataset=True,
+    #     **args_to_dict(args,
+    #                     dataset_defaults().keys())
+    # )
+
+    eval_dataset = None 
+
+
+    # let all processes sync up before starting with a new epoch of training
+
+    if dist_util.get_rank() == 0:
+        with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+            json.dump(vars(args), f, indent=2)
+
+    args.schedule_sampler = create_named_schedule_sampler(
+        args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    loss_class = E3DGELossClass(device, opt).to(device)
+
+    logger.log("training...")
+
+    TrainLoop = {
+        'flow_matching':
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine,
+        'flow_matching_gs':  
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine_gs, # slightly modified sampling and rendering for gs
+    }[args.trainer_name]
+
+
+    # if 'vpsde' in args.trainer_name:
+    #     sde_diffusion = make_sde_diffusion(
+    #         dnnlib.EasyDict(
+    #             args_to_dict(args,
+    #                          continuous_diffusion_defaults().keys())))
+    #     # assert args.mixed_prediction, 'enable mixed_prediction by default'
+    #     logger.log('create VPSDE diffusion.')
+    # else:
+    sde_diffusion = None
+
+    # if 'cldm' in args.trainer_name:
+    #     assert isinstance(denoise_model, tuple)
+    #     denoise_model, controlNet = denoise_model
+
+    #     controlNet.to(dist_util.dev())
+    #     controlNet.train()
+    # else:
+    controlNet = None
+
+    # st()
+    denoise_model.to(dist_util.dev())
+    denoise_model.train()
+
+    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
+    
+    # camera = th.load('eval_pose.pt', map_location=dist_util.dev())[:]
+
+    # if fid 
+
+    # '''
+    azimuths = []
+    elevations = []
+    frame_number = 10
+
+    for i in range(frame_number): # 1030 * 5 * 10, for FID 50K
+
+        azi, elevation = sample_uniform_cameras_on_sphere()
+        # azi, elevation = azi[0] / np.pi * 180, elevation[0] / np.pi * 180
+        azi, elevation = azi[0] / np.pi * 180, (elevation[0]-np.pi*0.5) / np.pi * 180 # [-0.5 pi, 0.5 pi]
+        azimuths.append(azi)
+        elevations.append(elevation)
+
+    azimuths = np.array(azimuths)
+    elevations = np.array(elevations)
+
+    # azimuths = np.array(list(range(0,360,30))).astype(float)
+    # frame_number = azimuths.shape[0]
+    # elevations = np.array([10]*azimuths.shape[0]).astype(float)
+
+    zero123pp_pose, _ = generate_input_camera(1.8, [[elevations[i], azimuths[i]] for i in range(frame_number)], fov=30)
+    K = th.Tensor([1.3889, 0.0000, 0.5000, 0.0000, 1.3889, 0.5000, 0.0000, 0.0000, 0.0039]).to(zero123pp_pose) # keeps the same
+    camera = th.cat([zero123pp_pose.reshape(frame_number,-1), K.unsqueeze(0).repeat(frame_number,1)], dim=-1)
+    # '''
+
+    # camera = uni_mesh_path(12, radius=2.0) # ! for exporting mesh
+
+    training_loop_class=TrainLoop(rec_model=auto_encoder,
+              denoise_model=denoise_model,
+              control_model=controlNet,
+              diffusion=diffusion,
+              sde_diffusion=sde_diffusion,
+              loss_class=loss_class,
+              data=data,
+            #   eval_data=None,
+              eval_data=eval_dataset, # return dataset
+              **vars(args))
+
+    if args.i23d:
+        # ! image-conditioned 3D generation
+        training_loop_class.eval_i23d_and_export(
+            prompt='',
+            save_img=args.save_img,
+            use_train_trajectory=args.use_train_trajectory,
+            camera=camera,
+            num_instances=args.num_instances,
+            num_samples=args.num_samples,
+            stage_1_output_dir=args.stage_1_output_dir,
+            export_mesh=args.export_mesh,
+        )
+
+
+    else:
+        # the script used in 3dtopia
+        with open('datasets/caption-forpaper.txt', 'r') as f:
+            all_prompts_available = [caption.strip() for caption in f.readlines()]
+
+        for prompt in all_prompts_available:
+
+            training_loop_class.eval_and_export(
+                prompt=prompt,
+                save_img=args.save_img,
+                use_train_trajectory=args.use_train_trajectory,
+                camera=camera,
+                num_instances=args.num_instances,
+                num_samples=args.num_samples,
+                stage_1_output_dir=args.stage_1_output_dir,
+                export_mesh=args.export_mesh,
+            )
+
+
+    dist_util.synchronize()
+    logger.log('sampling complete')
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        dataset_size=-1,
+        diffusion_input_size=-1,
+        trainer_name='adm',
+        use_amp=False,
+        train_vae=True,  # jldm?
+        triplane_scaling_divider=1.0,  # divide by this value
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        schedule_sampler="uniform",
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        resume_checkpoint_EG3D="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        load_depth=True,  # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        load_submodule_name='',  # for loading pretrained auto_encoder model
+        ignore_resume_opt=False,
+        # freeze_ae=False,
+        denoised_ae=True,
+        diffusion_ce_anneal=False,
+        use_lmdb=False,
+        interval=1,
+        freeze_triplane_decoder=False,
+        objv_dataset=False,
+        use_eos_feature=False,
+        clip_grad_throld=1.0,
+        allow_tf32=True,
+        save_img=False,
+        use_train_trajectory=
+        False,  # use train trajectory to sample images for fid calculation
+        unconditional_guidance_scale=1.0,
+        num_samples=10,
+        num_instances=10, # for i23d, loop different condition
+    )
+
+    defaults.update(model_and_diffusion_defaults())
+    defaults.update(continuous_diffusion_defaults())
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(dataset_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+    defaults.update(control_net_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    # ! add transport args
+    parse_transport_args(parser)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
+    # os.environ["NCCL_DEBUG"] = "INFO"
+    th.multiprocessing.set_start_method('spawn')
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    # opts = dnnlib.EasyDict(vars(args))  # compatiable with triplane original settings
+    # opts = args
+    args.rendering_kwargs = rendering_options_defaults(args)
+
+    # Launch processes.
+    logger.log('Launching processes...')
+
+    logger.log('Available devices ', th.cuda.device_count())
+    logger.log('Current cuda device ', th.cuda.current_device())
+    # logger.log('GPU Device name:', th.cuda.get_device_name(th.cuda.current_device()))
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup()  # clean port and socket when ctrl+c
diff --git a/scripts/vit_triplane_sit_train.py b/scripts/vit_triplane_sit_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb6c142656b8e44b5a1ac4917a5f9d31a0173bfa
--- /dev/null
+++ b/scripts/vit_triplane_sit_train.py
@@ -0,0 +1,535 @@
+"""
+Train a diffusion model on images.
+"""
+import json
+import sys
+import os
+
+sys.path.append('.')
+
+# from dnnlib import EasyDict
+import traceback
+
+import torch as th
+
+# from xformers.triton import FusedLayerNorm as LayerNorm
+
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import numpy as np
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.resample import create_named_schedule_sampler
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+    continuous_diffusion_defaults,
+    control_net_defaults,
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+)
+from guided_diffusion.continuous_diffusion import make_diffusion as make_sde_diffusion
+import nsr
+import nsr.lsgm
+# from nsr.train_util_diffusion import TrainLoop3DDiffusion as TrainLoop
+
+from datasets.eg3d_dataset import LMDBDataset_MV_Compressed_eg3d
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default, dataset_defaults
+from datasets.shapenet import load_data, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+
+from torch_utils import legacy, misc
+from torch.utils.data import Subset
+from pdb import set_trace as st
+
+from dnnlib.util import EasyDict, InfiniteSampler
+# from .vit_triplane_train_FFHQ import init_dataset_kwargs
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+th.backends.cudnn.enabled = True # https://zhuanlan.zhihu.com/p/635824460
+th.backends.cudnn.benchmark = True
+
+from transport import create_transport, Sampler
+from transport.train_utils import parse_transport_args
+
+# from torch.utils.tensorboard import SummaryWriter
+
+SEED = 0
+
+
+def training_loop(args):
+    # def training_loop(args):
+    logger.log("dist setup...")
+    # th.multiprocessing.set_start_method('spawn')
+    th.autograd.set_detect_anomaly(False) # type: ignore
+    # th.autograd.set_detect_anomaly(True)  # type: ignore
+    # st()
+
+    th.cuda.set_device(
+        args.local_rank)  # set this line to avoid extra memory on rank 0
+    th.cuda.empty_cache()
+
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+
+    dist_util.setup_dist(args)
+
+    # st() # mark
+
+    th.backends.cuda.matmul.allow_tf32 = args.allow_tf32
+    th.backends.cudnn.allow_tf32 = args.allow_tf32
+    # st()
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating ViT encoder and NSR decoder...")
+    # st() # mark
+    device = dist_util.dev()
+
+    args.img_size = [args.image_size_encoder]
+
+    logger.log("creating model and diffusion...")
+    # * set denoise model args
+
+    if args.denoise_in_channels == -1:
+        args.diffusion_input_size = args.image_size_encoder
+        args.denoise_in_channels = args.out_chans
+        args.denoise_out_channels = args.out_chans
+    else:
+        assert args.denoise_out_channels != -1
+
+    # args.image_size = args.image_size_encoder  # 224, follow the triplane size
+
+    # if args.diffusion_input_size == -1:
+    # else:
+    # args.image_size = args.diffusion_input_size
+
+    if args.pred_type == 'v':  # for lsgm training
+        assert args.predict_v == True  # for DDIM sampling
+    
+    # if not args.create_dit:
+
+    denoise_model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args,
+                       model_and_diffusion_defaults().keys()))
+
+    opts = eg3d_options_default()
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    logger.log("creating encoder and NSR decoder...")
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+
+    auto_encoder.to(device)
+    auto_encoder.eval()
+
+    # * load G_ema modules into autoencoder
+    # * clone G_ema.decoder to auto_encoder triplane
+    # logger.log("AE triplane decoder reuses G_ema decoder...")
+    # auto_encoder.decoder.register_buffer('w_avg', G_ema.backbone.mapping.w_avg)
+
+    # auto_encoder.decoder.triplane_decoder.decoder.load_state_dict(  # type: ignore
+    #     G_ema.decoder.state_dict())  # type: ignore
+
+    # set grad=False in this manner suppresses the DDP forward no grad error.
+
+    # if args.sr_training:
+
+    #     logger.log("AE triplane decoder reuses G_ema SR module...")
+    #     # auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict(  # type: ignore
+    #     #     G_ema.superresolution.state_dict())  # type: ignore
+
+    #     # set grad=False in this manner suppresses the DDP forward no grad error.
+    # logger.log("freeze SR module...")
+    # for param in auto_encoder.decoder.superresolution.parameters(): # type: ignore
+    #     param.requires_grad_(False)
+
+    #     # del G_ema
+    #     th.cuda.empty_cache()
+
+    if args.freeze_triplane_decoder:
+        logger.log("freeze triplane decoder...")
+        for param in auto_encoder.decoder.triplane_decoder.parameters(
+        ):  # type: ignore
+            # for param in auto_encoder.decoder.triplane_decoder.decoder.parameters(): # type: ignore
+            param.requires_grad_(False)
+
+    if args.cfg in ('afhq', 'ffhq'):
+
+        if args.sr_training:
+
+            logger.log("AE triplane decoder reuses G_ema SR module...")
+            auto_encoder.decoder.triplane_decoder.superresolution.load_state_dict(  # type: ignore
+                G_ema.superresolution.state_dict())  # type: ignore
+
+            # set grad=False in this manner suppresses the DDP forward no grad error.
+            for param in auto_encoder.decoder.triplane_decoder.superresolution.parameters(
+            ):  # type: ignore
+                param.requires_grad_(False)
+
+        # ! load data
+        if args.use_lmdb:
+            logger.log("creating LMDB eg3d data loader...")
+            training_set = LMDBDataset_MV_Compressed_eg3d(
+                args.data_dir,
+                args.image_size,
+                args.image_size_encoder,
+            )
+        else:
+            logger.log("creating eg3d data loader...")
+
+            training_set_kwargs, dataset_name = init_dataset_kwargs(
+                data=args.data_dir,
+                class_name='datasets.eg3d_dataset.ImageFolderDataset',
+                reso_gt=args.image_size)  # only load pose here
+            # if args.cond and not training_set_kwargs.use_labels:
+            # raise Exception('check here')
+
+            # training_set_kwargs.use_labels = args.cond
+            training_set_kwargs.use_labels = True
+            training_set_kwargs.xflip = False
+            training_set_kwargs.random_seed = SEED
+            training_set_kwargs.max_size = args.dataset_size
+            # desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
+
+            # * construct ffhq/afhq dataset
+            training_set = dnnlib.util.construct_class_by_name(
+                **training_set_kwargs)  # subclass of training.dataset.Dataset
+
+        training_set_sampler = InfiniteSampler(
+            dataset=training_set,
+            rank=dist_util.get_rank(),
+            num_replicas=dist_util.get_world_size(),
+            seed=SEED)
+
+        data = iter(
+            th.utils.data.DataLoader(
+                dataset=training_set,
+                sampler=training_set_sampler,
+                batch_size=args.batch_size,
+                pin_memory=True,
+                num_workers=args.num_workers,
+                persistent_workers=args.num_workers > 0,
+                prefetch_factor=max(8 // args.batch_size, 2),
+            ))
+        #  prefetch_factor=2))
+
+        eval_data = th.utils.data.DataLoader(dataset=Subset(
+            training_set, np.arange(8)),
+                                             batch_size=args.eval_batch_size,
+                                             num_workers=1)
+
+    else:
+
+        logger.log("creating data loader...")
+
+        if args.objv_dataset:
+            from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data, load_data_cls
+        else:  # shapenet
+            from datasets.shapenet import load_data, load_eval_data, load_memory_data
+
+        # TODO, load shapenet data
+        # data = load_data(
+        # st() mark
+        # if args.overfitting:
+        #     logger.log("create overfitting memory dataset")
+        #     data = load_memory_data(
+        #         file_path=args.eval_data_dir,
+        #         batch_size=args.batch_size,
+        #         reso=args.image_size,
+        #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+        #         num_workers=args.num_workers,
+        #         load_depth=True  # for evaluation
+        #     )
+        # else:
+        if args.use_wds:
+            if args.data_dir == 'NONE':
+                with open(args.shards_lst) as f:
+                    shards_lst = [url.strip() for url in f.readlines()]
+                data = load_wds_data(
+                    shards_lst, args.image_size, args.image_size_encoder,
+                    args.batch_size, args.num_workers,
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+
+            else:
+                data = load_wds_data(
+                    args.data_dir, args.image_size, args.image_size_encoder,
+                    args.batch_size, args.num_workers,
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+
+                # eval_data = load_wds_data(
+                #     args.data_dir,
+                #     args.image_size,
+                #     args.image_size_encoder,
+                #     args.eval_batch_size,
+                #     args.num_workers,
+                #     decode_encode_img_only=args.decode_encode_img_only,
+                #     load_wds_diff=args.load_wds_diff)
+
+            if args.eval_data_dir == 'NONE':
+                with open(args.eval_shards_lst) as f:
+                    eval_shards_lst = [url.strip() for url in f.readlines()]
+            else:
+                eval_shards_lst = args.eval_data_dir  # auto expanded
+
+            eval_data = load_wds_data(
+                eval_shards_lst,
+                args.image_size,
+                args.image_size_encoder,
+                args.eval_batch_size,
+                args.num_workers,
+                plucker_embedding=args.plucker_embedding,
+                decode_encode_img_only=args.decode_encode_img_only,
+                mv_input=args.mv_input,
+                load_wds_diff=False,
+                load_instance=True)
+
+        else:
+            logger.log("create all instances dataset")
+            # st() mark
+            # data = load_data(
+            #     file_path=args.data_dir,
+            #     batch_size=args.batch_size,
+            #     reso=args.image_size,
+            #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+            #     num_workers=args.num_workers,
+            #     load_depth=args.load_depth,
+            #     preprocess=auto_encoder.preprocess,  # clip
+            #     dataset_size=args.dataset_size,
+            #     use_lmdb=args.use_lmdb,
+            #     trainer_name=args.trainer_name,
+            #     # load_depth=True # for evaluation
+            # )
+            # eval_data = load_eval_data(
+            #     file_path=args.eval_data_dir,
+            #     batch_size=args.eval_batch_size,
+            #     reso=args.image_size,
+            #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+            #     num_workers=args.num_workers,
+            #     load_depth=True,  # for evaluation
+            #     interval=args.interval,
+            #     use_lmdb=args.use_lmdb,
+            # )
+
+            data = load_data(
+                file_path=args.data_dir,
+                batch_size=args.batch_size,
+                reso=args.image_size,
+                reso_encoder=args.image_size_encoder,  # 224 -> 128
+                num_workers=args.num_workers,
+                load_latent=True,
+                **args_to_dict(args,
+                                dataset_defaults().keys())
+                # load_depth=args.load_depth,
+                # preprocess=auto_encoder.preprocess,  # clip
+                # dataset_size=args.dataset_size,
+                # use_lmdb=args.use_lmdb,
+                # trainer_name=args.trainer_name,
+                # load_depth=True # for evaluation
+            )
+            # eval_dataset = load_data_cls(
+            #     file_path=args.data_dir,
+            #     batch_size=args.batch_size,
+            #     reso=args.image_size,
+            #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+            #     num_workers=args.num_workers,
+            #     load_latent=True,
+            #     return_dataset=True,
+            #     **args_to_dict(args,
+            #                     dataset_defaults().keys())
+            #     # load_depth=args.load_depth,
+            #     # preprocess=auto_encoder.preprocess,  # clip
+            #     # dataset_size=args.dataset_size,
+            #     # use_lmdb=args.use_lmdb,
+            #     # trainer_name=args.trainer_name,
+            #     # load_depth=True # for evaluation
+            # )
+            eval_dataset = None
+
+            # st()
+
+            # eval_data = data
+            # eval_data = load_eval_data(
+            #     file_path=args.eval_data_dir,
+            #     batch_size=args.eval_batch_size,
+            #     reso=args.image_size,
+            #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+            #     num_workers=args.num_workers,
+            #     load_depth=True,  # for evaluation
+            #     interval=args.interval,
+            #     use_lmdb=args.use_lmdb,
+            # )
+
+
+    # let all processes sync up before starting with a new epoch of training
+
+    if dist_util.get_rank() == 0:
+        with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+            json.dump(vars(args), f, indent=2)
+
+    args.schedule_sampler = create_named_schedule_sampler(
+        args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    loss_class = E3DGELossClass(device, opt).to(device)
+
+    logger.log("training...")
+
+    TrainLoop = {
+        'flow_matching':
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine,
+        'flow_matching_gs':  
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine_gs, # slightly modified sampling and rendering for gs
+        'flow_matching_gs_clay':  
+        nsr.lsgm.flow_matching_trainer.FlowMatchingEngine_gs_clay, # slightly modified sampling and rendering for gs
+    }[args.trainer_name]
+
+    if 'vpsde' in args.trainer_name:
+        sde_diffusion = make_sde_diffusion(
+            dnnlib.EasyDict(
+                args_to_dict(args,
+                             continuous_diffusion_defaults().keys())))
+        # assert args.mixed_prediction, 'enable mixed_prediction by default'
+        logger.log('create VPSDE diffusion.')
+    else:
+        sde_diffusion = None
+
+    if 'cldm' in args.trainer_name:
+        assert isinstance(denoise_model, tuple)
+        denoise_model, controlNet = denoise_model
+
+        controlNet.to(dist_util.dev())
+        controlNet.train()
+    else:
+        controlNet = None
+
+    # st()
+    denoise_model.to(dist_util.dev())
+    denoise_model.train()
+
+    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
+    TrainLoop(rec_model=auto_encoder,
+              denoise_model=denoise_model,
+              control_model=controlNet,
+              diffusion=diffusion,
+              sde_diffusion=sde_diffusion,
+              loss_class=loss_class,
+              data=data,
+              eval_data=None, # return dataset
+            #   eval_data=eval_dataset, # return dataset
+            #   eval_data=eval_data, # return dataset
+              **vars(args)).run_loop()
+
+    dist_util.synchronize()
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        dataset_size=-1,
+        diffusion_input_size=-1,
+        trainer_name='adm',
+        use_amp=False,
+        train_vae=True,  # jldm?
+        triplane_scaling_divider=1.0,  # divide by this value
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        schedule_sampler="uniform",
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        resume_checkpoint_EG3D="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        load_depth=True,  # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        load_submodule_name='',  # for loading pretrained auto_encoder model
+        ignore_resume_opt=False,
+        # freeze_ae=False,
+        denoised_ae=True,
+        diffusion_ce_anneal=False,
+        use_lmdb=False,
+        interval=1,
+        freeze_triplane_decoder=False,
+        objv_dataset=False,
+        use_eos_feature=False,
+        clip_grad_throld=1.0,
+        allow_tf32=True,
+    )
+
+    defaults.update(model_and_diffusion_defaults())
+    defaults.update(continuous_diffusion_defaults())
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(dataset_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+    defaults.update(control_net_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    # ! add transport args
+    parse_transport_args(parser)
+
+    return parser
+
+
+if __name__ == "__main__":
+    # os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
+    # os.environ["NCCL_DEBUG"] = "INFO"
+    th.multiprocessing.set_start_method('spawn')
+    th.multiprocessing.set_sharing_strategy('file_system') # avoid "too many files opened"
+    # https://stackoverflow.com/questions/76300957/multiprocessing-pool-too-many-file-descriptors-using-pytorch
+    # https://stackoverflow.com/questions/48250053/pytorchs-dataloader-too-many-open-files-error-when-no-files-should-be-open
+
+    os.environ[
+        "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    # opts = dnnlib.EasyDict(vars(args))  # compatiable with triplane original settings
+    # opts = args
+    args.rendering_kwargs = rendering_options_defaults(args)
+
+    # Launch processes.
+    logger.log('Launching processes...')
+
+    logger.log('Available devices ', th.cuda.device_count())
+    logger.log('Current cuda device ', th.cuda.current_device())
+    # logger.log('GPU Device name:', th.cuda.get_device_name(th.cuda.current_device()))
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup()  # clean port and socket when ctrl+c
diff --git a/scripts/vit_triplane_train.py b/scripts/vit_triplane_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4929ecc4cbf332a7798a7358c3c6a60a702a230
--- /dev/null
+++ b/scripts/vit_triplane_train.py
@@ -0,0 +1,351 @@
+"""
+Train a diffusion model on images.
+"""
+from pdb import set_trace as st
+import random
+import json
+import sys
+import os
+
+sys.path.append('.')
+
+import torch.distributed as dist
+
+import traceback
+
+import torch as th
+
+# if th.cuda.is_available(): # FIXME
+#     from xformers.triton import FusedLayerNorm as LayerNorm
+
+import torch.multiprocessing as mp
+import numpy as np
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+from nsr.train_nv_util import TrainLoop3DRecNV, TrainLoop3DRec, TrainLoop3DRecNVPatch, TrainLoop3DRecNVPatchSingleForward, TrainLoop3DRecNVPatchSingleForwardMV, TrainLoop3DRecNVPatchSingleForwardMV_NoCrop, TrainLoop3DRecNVPatchSingleForwardMVAdvLoss, TrainLoop3DRecNVPatchSingleForwardMV_NoCrop_adv
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default, dataset_defaults
+# from datasets import g_buffer_objaverse
+from nsr.losses.builder import E3DGELossClass, E3DGE_with_AdvLoss
+
+
+# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
+th.backends.cuda.matmul.allow_tf32 = True
+th.backends.cudnn.allow_tf32 = True
+th.backends.cudnn.enabled = True
+
+
+def training_loop(args):
+    # def training_loop(args):
+    dist_util.setup_dist(args)
+    # th.autograd.set_detect_anomaly(True) # type: ignore
+    th.autograd.set_detect_anomaly(False)  # type: ignore
+    # https://blog.csdn.net/qq_41682740/article/details/126304613
+
+    SEED = args.seed
+
+    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
+    logger.log(f"global_rank={args.global_rank}, local_rank={args.local_rank} init complete, seed={SEED}")
+    th.cuda.set_device(args.local_rank)
+    th.cuda.empty_cache()
+
+    # * deterministic algorithms flags
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+    random.seed(SEED)
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating encoder and NSR decoder...")
+    # device = dist_util.dev()
+    device = th.device("cuda", args.local_rank)
+
+    # shared eg3d opts
+    opts = eg3d_options_default()
+
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+    auto_encoder = create_3DAE_model(
+        **args_to_dict(args,
+                       encoder_and_nsr_defaults().keys()))
+    auto_encoder.to(device)
+    auto_encoder.train()
+
+    logger.log("creating data loader...")
+    # data = load_data(
+    # st()
+    if args.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_wds_data
+    else:  # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data
+
+    if args.overfitting:
+        data = load_memory_data(
+            file_path=args.data_dir,
+            batch_size=args.batch_size,
+            reso=args.image_size,
+            reso_encoder=args.image_size_encoder,  # 224 -> 128
+            num_workers=args.num_workers,
+            # load_depth=args.depth_lambda > 0
+            # load_depth=True,  # for evaluation
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+        eval_data = None
+    else:
+        if args.use_wds:
+            # st()
+            if args.data_dir == 'NONE':
+                with open(args.shards_lst) as f:
+                    shards_lst = [url.strip() for url in f.readlines()]
+                data = load_wds_data(
+                    shards_lst,  # type: ignore
+                    args.image_size,
+                    args.image_size_encoder,
+                    args.batch_size,
+                    args.num_workers,
+                    #  plucker_embedding=args.plucker_embedding,
+                    #  mv_input=args.mv_input,
+                    #  split_chunk_input=args.split_chunk_input,
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+
+            elif not args.inference:
+                data = load_wds_data(args.data_dir,
+                                     args.image_size,
+                                     args.image_size_encoder,
+                                     args.batch_size,
+                                     args.num_workers,
+                                     plucker_embedding=args.plucker_embedding,
+                                     mv_input=args.mv_input,
+                                     split_chunk_input=args.split_chunk_input)
+            else:
+                data = None
+            # ! load eval
+
+            if args.eval_data_dir == 'NONE':
+                with open(args.eval_shards_lst) as f:
+                    eval_shards_lst = [url.strip() for url in f.readlines()]
+            else:
+                eval_shards_lst = args.eval_data_dir  # auto expanded
+
+            eval_data = load_wds_data(
+                eval_shards_lst,  # type: ignore
+                args.image_size,
+                args.image_size_encoder,
+                args.eval_batch_size,
+                args.num_workers,
+                **args_to_dict(args,
+                               dataset_defaults().keys()))
+            # load_instance=True) # TODO
+
+        else:
+
+            if args.inference:
+                data = None
+            else:
+                data = load_data(
+                    file_path=args.data_dir,
+                    batch_size=args.batch_size,
+                    reso=args.image_size,
+                    reso_encoder=args.image_size_encoder,  # 224 -> 128
+                    num_workers=args.num_workers,
+                    **args_to_dict(args,
+                                   dataset_defaults().keys())
+                                   )
+
+                    # load_depth=True # for evaluation
+
+            if args.pose_warm_up_iter > 0:
+                overfitting_dataset = load_memory_data(
+                    file_path=args.data_dir,
+                    batch_size=args.batch_size,
+                    reso=args.image_size,
+                    reso_encoder=args.image_size_encoder,  # 224 -> 128
+                    num_workers=args.num_workers,
+                    # load_depth=args.depth_lambda > 0
+                    # load_depth=True  # for evaluation
+                    **args_to_dict(args,
+                                   dataset_defaults().keys()))
+                data = [data, overfitting_dataset, args.pose_warm_up_iter]
+
+            eval_data = load_eval_data(
+                file_path=args.eval_data_dir,
+                batch_size=args.eval_batch_size,
+                reso=args.image_size,
+                reso_encoder=args.image_size_encoder,  # 224 -> 128
+                num_workers=args.num_workers,
+                load_depth=True,  # for evaluation
+                preprocess=auto_encoder.preprocess,
+                wds_split=args.wds_split,
+                # interval=args.interval,
+                # use_lmdb=args.use_lmdb,
+                # plucker_embedding=args.plucker_embedding,
+                # load_real=args.load_real,
+                # four_view_for_latent=args.four_view_for_latent,
+                # load_extra_36_view=args.load_extra_36_view,
+                # shuffle_across_cls=args.shuffle_across_cls,
+                **args_to_dict(args,
+                               dataset_defaults().keys()))
+
+    logger.log("creating data loader done...")
+
+    args.img_size = [args.image_size_encoder]
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+
+    # let all processes sync up before starting with a new epoch of training
+    dist_util.synchronize()
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    # opt.max_depth, opt.min_depth = args.rendering_kwargs.ray_end, args.rendering_kwargs.ray_start
+    if 'disc' in args.trainer_name:
+        loss_class = E3DGE_with_AdvLoss(
+            device,
+            opt,
+            # disc_weight=args.patchgan_disc, # rec_cvD_lambda
+            disc_factor=args.patchgan_disc_factor,  # reduce D update speed
+            disc_weight=args.patchgan_disc_g_weight).to(device)
+    else:
+        loss_class = E3DGELossClass(device, opt).to(device)
+
+    # writer = SummaryWriter() # TODO, add log dir
+
+    logger.log("training...")
+
+    TrainLoop = {
+        'input_rec': TrainLoop3DRec,
+        'nv_rec': TrainLoop3DRecNV,
+        # 'nv_rec_patch': TrainLoop3DRecNVPatch,
+        'nv_rec_patch': TrainLoop3DRecNVPatchSingleForward,
+        'nv_rec_patch_mvE': TrainLoop3DRecNVPatchSingleForwardMV,
+        'nv_rec_patch_mvE_disc': TrainLoop3DRecNVPatchSingleForwardMVAdvLoss,
+        'nv_rec_patch_mvE_gs': TrainLoop3DRecNVPatchSingleForwardMV_NoCrop,
+        'nv_rec_patch_mvE_gs_disc': TrainLoop3DRecNVPatchSingleForwardMV_NoCrop_adv,
+    }[args.trainer_name]
+
+    logger.log("creating TrainLoop done...")
+
+    # th._dynamo.config.verbose=True # th212 required
+    # th._dynamo.config.suppress_errors = True
+    auto_encoder.decoder.rendering_kwargs = args.rendering_kwargs
+    train_loop = TrainLoop(
+        rec_model=auto_encoder,
+        loss_class=loss_class,
+        data=data,
+        eval_data=eval_data,
+        #   compile=args.compile,
+        **vars(args))
+    # train_loop.rendering_kwargs = args.rendering_kwargs
+
+    if args.inference:
+        camera = th.load('eval_pose.pt', map_location=dist_util.dev())
+        train_loop.eval_novelview_loop(camera=camera,
+                                       save_latent=args.save_latent)
+    else:
+        train_loop.run_loop()
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        seed=0,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_amp=False,
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        # test warm up pose sampling training
+        pose_warm_up_iter=-1,
+        inference=False,
+        export_latent=False,
+        save_latent=False,
+        wds_split=1,  # out of 4
+    )
+
+    defaults.update(dataset_defaults())  # type: ignore
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    th.multiprocessing.set_start_method('spawn')
+
+    # os.environ[
+    # "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+    # os.environ["NCCL_DEBUG"]="INFO"
+
+    args = create_argparser().parse_args()
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    # if os.environ['WORLD_SIZE'] > 1:
+
+    # for multi-node training
+    if dist_util.get_world_size() > 1:
+        args.global_rank = int(os.environ["RANK"])
+    else:
+        args.global_rank = 0
+
+    args.gpus = th.cuda.device_count()
+
+    opts = args
+
+    args.rendering_kwargs = rendering_options_defaults(opts)
+
+    # print(args)
+    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=2)
+
+    # Launch processes.
+    print('Launching processes...')
+
+    try:
+        training_loop(args)
+    # except KeyboardInterrupt as e:
+    except Exception as e:
+        # print(e)
+        traceback.print_exc()
+        dist_util.cleanup()  # clean port and socket when ctrl+c
diff --git a/scripts/wds_create_batch_gz.py b/scripts/wds_create_batch_gz.py
new file mode 100644
index 0000000000000000000000000000000000000000..871d40084736dde416f8adedb5b15def2b21c31f
--- /dev/null
+++ b/scripts/wds_create_batch_gz.py
@@ -0,0 +1,475 @@
+"""
+Train a diffusion model on images.
+"""
+import cv2
+# import imageio
+import lz4.frame
+import gzip
+import random
+import json
+import sys
+import os
+import lmdb
+from tqdm import tqdm
+sys.path.append('.')
+import torch.distributed as dist
+from pathlib import Path
+import pickle
+import traceback
+from PIL import Image
+import torch as th
+if th.cuda.is_available():
+    from xformers.triton import FusedLayerNorm as LayerNorm
+import torch.multiprocessing as mp
+import lzma
+import webdataset as wds
+import numpy as np
+
+from torch.utils.data import DataLoader, Dataset
+import imageio.v3 as iio
+
+import argparse
+import dnnlib
+from guided_diffusion import dist_util, logger
+from guided_diffusion.script_util import (
+    args_to_dict,
+    add_dict_to_argparser,
+)
+# from nsr.train_util import TrainLoop3DRec as TrainLoop
+# from nsr.train_nv_util import TrainLoop3DRecNV, TrainLoop3DRec, TrainLoop3DRecNVPatch
+from nsr.script_util import create_3DAE_model, encoder_and_nsr_defaults, loss_defaults, rendering_options_defaults, eg3d_options_default
+from datasets.shapenet import load_data, load_data_for_lmdb, load_eval_data, load_memory_data
+from nsr.losses.builder import E3DGELossClass
+from datasets.eg3d_dataset import init_dataset_kwargs
+
+# from .lmdb_create import encode_and_compress_image
+
+def encode_and_compress_image(inp_array, is_image=False, compress=True):
+    # Read the image using imageio
+    # image = imageio.v3.imread(image_path)
+
+    # Convert the image to bytes
+    # with io.BytesIO() as byte_buffer:
+    #     imageio.imsave(byte_buffer, image, format="png")
+    #     image_bytes = byte_buffer.getvalue()
+    if is_image:
+        inp_bytes = iio.imwrite("<bytes>", inp_array, extension=".png")
+    else:
+        inp_bytes = inp_array.tobytes()
+
+    # Compress the image data using gzip
+    if compress:
+        # compressed_data = gzip.compress(inp_bytes)
+        compressed_data = lz4.frame.compress(inp_bytes)
+        return compressed_data
+    else:
+        return inp_bytes
+
+
+
+
+from pdb import set_trace as st
+import bz2
+
+# th.backends.cuda.matmul.allow_tf32 = True # https://huggingface.co/docs/diffusers/optimization/fp16
+
+
+
+def training_loop(args):
+    # def training_loop(args):
+    # dist_util.setup_dist(args)
+    # th.autograd.set_detect_anomaly(True) # type: ignore
+    th.autograd.set_detect_anomaly(False)  # type: ignore
+    # https://blog.csdn.net/qq_41682740/article/details/126304613
+
+    SEED = args.seed
+
+    # dist.init_process_group(backend='nccl', init_method='env://', rank=args.local_rank, world_size=th.cuda.device_count())
+    # logger.log(f"{args.local_rank=} init complete, seed={SEED}")
+    # th.cuda.set_device(args.local_rank)
+    th.cuda.empty_cache()
+
+    # * deterministic algorithms flags
+    th.cuda.manual_seed_all(SEED)
+    np.random.seed(SEED)
+    random.seed(SEED)
+
+    # logger.configure(dir=args.logdir, format_strs=["tensorboard", "csv"])
+    logger.configure(dir=args.logdir)
+
+    logger.log("creating encoder and NSR decoder...")
+    # device = dist_util.dev()
+    # device = th.device("cuda", args.local_rank)
+
+    # shared eg3d opts
+    opts = eg3d_options_default()
+
+    if args.sr_training:
+        args.sr_kwargs = dnnlib.EasyDict(
+            channel_base=opts.cbase,
+            channel_max=opts.cmax,
+            fused_modconv_default='inference_only',
+            use_noise=True
+        )  # ! close noise injection? since noise_mode='none' in eg3d
+
+
+    if args.objv_dataset:
+        from datasets.g_buffer_objaverse import load_data, load_eval_data, load_memory_data, load_data_for_lmdb
+    else: # shapenet
+        from datasets.shapenet import load_data, load_eval_data, load_memory_data, load_data_for_lmdb
+
+    # auto_encoder = create_3DAE_model(
+    #     **args_to_dict(args,
+    #                    encoder_and_nsr_defaults().keys()))
+    # auto_encoder.to(device)
+    # auto_encoder.train()
+
+    logger.log("creating data loader...")
+    # data = load_data(
+    # st()
+    # if args.overfitting:
+    #     data = load_memory_data(
+    #         file_path=args.data_dir,
+    #         batch_size=args.batch_size,
+    #         reso=args.image_size,
+    #         reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #         num_workers=args.num_workers,
+    #         # load_depth=args.depth_lambda > 0
+    #         load_depth=True  # for evaluation
+    #     )
+    # else:
+    if args.cfg in ('afhq', 'ffhq'):
+        # ! load data
+        logger.log("creating eg3d data loader...")
+        training_set_kwargs, dataset_name = init_dataset_kwargs(data=args.data_dir, 
+                                                                class_name='datasets.eg3d_dataset.ImageFolderDatasetLMDB',
+                                                                reso_gt=args.image_size) # only load pose here
+        # if args.cond and not training_set_kwargs.use_labels:
+        # raise Exception('check here')
+
+        # training_set_kwargs.use_labels = args.cond
+        training_set_kwargs.use_labels = True
+        training_set_kwargs.xflip = False
+        training_set_kwargs.random_seed = SEED
+        # training_set_kwargs.max_size = args.dataset_size
+        # desc = f'{args.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
+
+        # * construct ffhq/afhq dataset
+        training_set = dnnlib.util.construct_class_by_name(
+            **training_set_kwargs)  # subclass of training.dataset.Dataset
+        dataset_size = len(training_set)
+
+        # training_set_sampler = InfiniteSampler(
+        #     dataset=training_set,
+        #     rank=dist_util.get_rank(),
+        #     num_replicas=dist_util.get_world_size(),
+        #     seed=SEED)
+
+        data = DataLoader(
+            training_set,
+            shuffle=False,
+            batch_size=1,
+            num_workers=16,
+            drop_last=False,
+            # prefetch_factor=2,
+            pin_memory=True,
+            persistent_workers=True,
+        )
+
+    else:
+        # data, dataset_name, dataset_size, dataset = load_data_for_lmdb(
+        data, dataset_name, dataset_size = load_data_for_lmdb(
+            file_path=args.data_dir,
+            batch_size=args.batch_size,
+            reso=args.image_size,
+            reso_encoder=args.image_size_encoder,  # 224 -> 128
+            num_workers=args.num_workers,
+            load_depth=True,
+            preprocess=None,
+            dataset_size=args.dataset_size,
+            trainer_name=args.trainer_name,
+            shuffle_across_cls=args.shuffle_across_cls,
+            wds_split=args.wds_split,
+            # wds_output_path=os.path.join(logger.get_dir(), f'wds-%06d.tar')
+            # load_depth=True # for evaluation
+        )
+    #     if args.pose_warm_up_iter > 0:
+    #         overfitting_dataset = load_memory_data(
+    #             file_path=args.data_dir,
+    #             batch_size=args.batch_size,
+    #             reso=args.image_size,
+    #             reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #             num_workers=args.num_workers,
+    #             # load_depth=args.depth_lambda > 0
+    #             load_depth=True  # for evaluation
+    #         )
+    #         data = [data, overfitting_dataset, args.pose_warm_up_iter]
+    # eval_data = load_eval_data(
+    #     file_path=args.eval_data_dir,
+    #     batch_size=args.eval_batch_size,
+    #     reso=args.image_size,
+    #     reso_encoder=args.image_size_encoder,  # 224 -> 128
+    #     num_workers=args.num_workers,
+    #     load_depth=True,  # for evaluation
+    #     preprocess=auto_encoder.preprocess)
+    args.img_size = [args.image_size_encoder]
+    # try dry run
+    # batch = next(data)
+    # batch = None
+
+    # logger.log("creating model and diffusion...")
+
+    # let all processes sync up before starting with a new epoch of training
+    dist_util.synchronize()
+
+    # schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+    opt = dnnlib.EasyDict(args_to_dict(args, loss_defaults().keys()))
+    # opt.max_depth, opt.min_depth = args.rendering_kwargs.ray_end, args.rendering_kwargs.ray_start
+    # loss_class = E3DGELossClass(device, opt).to(device)
+
+    # writer = SummaryWriter() # TODO, add log dir
+
+    logger.log("training...")
+
+    # TrainLoop = {
+    #     'input_rec': TrainLoop3DRec,
+    #     'nv_rec': TrainLoop3DRecNV,
+    #     'nv_rec_patch': TrainLoop3DRecNVPatch,
+    # }[args.trainer_name]
+
+    # TrainLoop(rec_model=auto_encoder,
+    #           loss_class=loss_class,
+    #           data=data,
+    #           eval_data=eval_data,
+    #           **vars(args)).run_loop()  # ! overfitting
+
+
+    # Function to compress an image using gzip
+    # def compress_image_gzip(image_path):
+    # def encode_and_compress_image(inp_array, is_image=False):
+    #     # Read the image using imageio
+    #     # image = imageio.v3.imread(image_path)
+
+    #     # Convert the image to bytes
+    #     # with io.BytesIO() as byte_buffer:
+    #     #     imageio.imsave(byte_buffer, image, format="png")
+    #     #     image_bytes = byte_buffer.getvalue()
+    #     if is_image:
+    #         inp_bytes = iio.imwrite("<bytes>", inp_array, extension=".png")
+    #     else:
+    #         inp_bytes = inp_array.tobytes()
+
+    #     # Compress the image data using gzip
+    #     compressed_data = gzip.compress(inp_bytes)
+
+    #     return compressed_data
+
+
+    def convert_to_wds_compressed(dataset_loader, dataset_size, lmdb_path, start_shard, wds_split):
+        """
+        Convert a PyTorch dataset to LMDB format.
+
+        Parameters:
+        - dataset: PyTorch dataset
+        - lmdb_path: Path to store the LMDB database
+        """
+        # env = lmdb.open(lmdb_path, map_size=1024 ** 4, readahead=False)  # Adjust map_size based on your dataset size
+        # sink = wds.ShardWriter(lmdb_path, start_shard=start_shard, compress=True)
+        # sink = wds.ShardWriter(lmdb_path, start_shard=start_shard, compress=False)
+
+        # with env.begin(write=True) as txn:
+
+        # with env.begin(write=True) as txn:
+            # txn.put("length".encode("utf-8"), str(dataset_size).encode("utf-8"))
+
+
+        for idx, sample in enumerate(tqdm(dataset_loader)):
+            # if idx > 10:
+            #     break
+
+            # pass
+            # remove the batch index of returned dict sample
+
+
+            # st()
+
+            sample_ins = sample.pop('ins')
+            assert all([sample_ins[i]==sample_ins[0] for i in range(0,len(sample_ins))]), sample_ins # check the batch is the same instnace
+            ins = sample_ins[0]
+
+
+            sample = {
+                # k:v.squeeze(0).cpu().numpy() if isinstance(v, th.Tensor) else v[0] for k, v in sample.items()
+                k:v.cpu().numpy() if isinstance(v, th.Tensor) else v for k, v in sample.items()
+                # k:v.cpu().numpy() if isinstance(v, torch.Tensor) else v for k, v in sample.items()
+            }
+
+            # encode batch images/depths/strings?  no need to encode ins/fname here; just save the caption
+
+            # sample = dataset_loader[idx]
+            # compressed_sample = {}
+            caption = sample.pop('caption')[0]
+
+            # root_dir = os.path.join(logger.get_dir(), ins.split('/')[0], idx)
+
+            # root_dir = Path(os.path.join(logger.get_dir(), str(idx)) )
+            root_dir = Path(os.path.join(logger.get_dir(), ins) )
+
+            if root_dir.exists(): # find where to save what
+                save_idx = int(sorted(os.listdir(root_dir) )[-1] )
+                if save_idx >= 3: # may have duplicated stuffs.
+                    continue
+            else:
+                root_dir.mkdir(parents=True, exist_ok=True)
+                save_idx = 0
+
+            root_dir = root_dir / f'{save_idx+1}'
+            # assert not root_dir.exists()
+            root_dir.mkdir(parents=True, exist_ok=False)
+
+            raw_img = sample.pop('raw_img')
+            # depth = sample.pop('depth') # just c and bbox
+            # depth = sample.pop('d_normalized') # just c and bbox
+            depth = sample.pop('depth') # just c and bbox
+            # d_near = sample.pop('d_near') # just c and bbox
+            # d_far = sample.pop('d_far') # just c and bbox
+            # d_near_far = np.stack([d_near, d_far])
+            normal = sample.pop('normal') # shall in [-1,1]
+
+            alpha_mask = (sample.pop('alpha_mask')*255).astype(np.uint8)
+
+            raw_img = np.concatenate([raw_img[i] for i in range(raw_img.shape[0])], axis=1) # concat png in w dim
+            # depth  = np.concatenate([depth[i] for i in range(depth.shape[0])], axis=1) # concat png in w dim
+            alpha_mask  = np.concatenate([alpha_mask[i] for i in range(alpha_mask.shape[0])], axis=1) # concat png in w dim
+            # depth_alpha = np.concatenate([depth, alpha_mask], axis=0)
+            normal = np.concatenate([normal[i] for i in range(normal.shape[0])], axis=1) # concat png in w dim
+            # nlrmal
+            # img_depth_alpha = np.concatenate([raw_img, depth, alpha_mask], axis=-1)
+
+            cv2.imwrite(os.path.join(str(root_dir), 'raw_img.jpg'), cv2.cvtColor(raw_img, cv2.COLOR_RGB2BGR), [int(cv2.IMWRITE_JPEG_QUALITY), 95])
+            # cv2.imwrite(os.path.join(str(root_dir), 'raw_img.png'), cv2.cvtColor(raw_img, cv2.COLOR_RGB2BGR),) # ! save a png version, v=5.
+            # cv2.imwrite(str(root_dir / 'depth_alpha.jpg'), depth_alpha, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
+            cv2.imwrite(str(root_dir / 'alpha.jpg'), alpha_mask, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
+            cv2.imwrite(str(root_dir / 'normal.png'), (normal*127.5+127.5)) # no quant error here
+
+            # st()
+            np.savez_compressed(str(root_dir / 'depth.npz'), depth=depth)
+
+            # np.save(root_dir / 'd_near_far.npy', d_near_far) # save high-res depth with npz
+
+            np.save(root_dir / 'c.npy', sample['c'].astype(np.float32))
+            # np.save(root_dir / 'bbox.npy', sample['bbox'].astype(np.uint8))
+
+            with open(root_dir / 'caption.txt', 'w') as f:
+                f.write(caption)
+
+            with open(root_dir / 'ins.txt', 'w') as f:
+                # f.write(ins.encode('utf8'))
+                f.write(ins)
+
+
+            # sink.write({
+            #     "__key__": f"sample_{wds_split:03d}_{idx:07d}",
+            #     # 'sample.npz': sample, # {str: ndarray}
+
+            #     # 'c.npy': sample['c'].astype(np.float32),
+            #     # 'bbox.npy': sample['bbox'].astype(np.uint8),
+            #     # 'ins.txt': ins,
+            #     # 'caption.txt': caption,
+            #     # 'd_near.npy': d_near.astype(np.float32),
+            #     # 'd_far.npy': d_far.astype(np.float32),
+            #     # 'raw_img.jpeg': raw_img,
+            #     # # 'raw_img.png': raw_img,
+            #     # 'alpha_mask.jpeg': alpha_mask, # lossless compression
+
+            #     'depth_alpha.png': depth_alpha,
+            #     'raw_img.png': raw_img,
+
+            # })
+
+        # sink.close()
+
+
+    # convert_to_wds_compressed(data, dataset_size, os.path.join(logger.get_dir(), f'wds-%06d.tar.gz'), args.start_shard, args.wds_split) 
+    convert_to_wds_compressed(data, dataset_size, os.path.join(logger.get_dir(), f'wds-%06d.tar'), args.start_shard, args.wds_split) 
+
+
+
+def create_argparser(**kwargs):
+    # defaults.update(model_and_diffusion_defaults())
+
+    defaults = dict(
+        seed=0,
+        dataset_size=-1,
+        trainer_name='input_rec',
+        use_amp=False,
+        overfitting=False,
+        num_workers=4,
+        image_size=128,
+        image_size_encoder=224,
+        iterations=150000,
+        anneal_lr=False,
+        lr=5e-5,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        eval_batch_size=12,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=50,
+        eval_interval=2500,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        data_dir="",
+        eval_data_dir="",
+        # load_depth=False, # TODO
+        logdir="/mnt/lustre/yslan/logs/nips23/",
+        # test warm up pose sampling training
+        objv_dataset=False,
+        pose_warm_up_iter=-1,
+        start_shard=0,
+        shuffle_across_cls=False,
+        wds_split=1, # out of 4
+    )
+
+    defaults.update(encoder_and_nsr_defaults())  # type: ignore
+    defaults.update(loss_defaults())
+
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+
+    return parser
+
+
+if __name__ == "__main__":
+    th.multiprocessing.set_start_method('spawn')
+    # os.environ[
+    # "TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"  # set to DETAIL for runtime logging.
+    # os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
+    # os.environ["NCCL_DEBUG"]="INFO"
+
+    args = create_argparser().parse_args()
+    # args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.gpus = th.cuda.device_count()
+
+    opts = args
+
+    args.rendering_kwargs = rendering_options_defaults(opts)
+
+    # print(args)
+    with open(os.path.join(args.logdir, 'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=2)
+
+    # Launch processes.
+    print('Launching processes...')
+
+    # try:
+    training_loop(args)
+    # except KeyboardInterrupt as e:
+    # except Exception as e:
+    #     # print(e)
+    #     traceback.print_exc()
+    #     dist_util.cleanup() # clean port and socket when ctrl+c
diff --git a/sgm/__init__.py b/sgm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..24bc84af8b1041de34b9816e0507cb1ac207bd13
--- /dev/null
+++ b/sgm/__init__.py
@@ -0,0 +1,4 @@
+from .models import AutoencodingEngine, DiffusionEngine
+from .util import get_configs_path, instantiate_from_config
+
+__version__ = "0.1.0"
diff --git a/sgm/__pycache__/__init__.cpython-39.pyc b/sgm/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..164425bec332c817387b87a72fecb906ef4678ed
Binary files /dev/null and b/sgm/__pycache__/__init__.cpython-39.pyc differ
diff --git a/sgm/__pycache__/util.cpython-39.pyc b/sgm/__pycache__/util.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f7e4c94b5bafb342981a65bb189bbaa3d2491342
Binary files /dev/null and b/sgm/__pycache__/util.cpython-39.pyc differ
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond-clay.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond-clay.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9ad8224db0cd6792a0aac13e357ac782eab034de
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond-clay.yaml
@@ -0,0 +1,72 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   ucg_rate: 0.33
+        #   # legacy_ucg_value: None
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+        #   params:
+        #       arch: 'ViT-L-14'
+        #       version: 'openai'
+        #       # version: 'laion2b_s32b_b82k'
+        #       freeze: True
+        #       output_tokens: True
+        #       # inp_size: 364
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitg
+              # inp_size: 364
+
+        - is_trainable: False
+          input_key: 'fps-xyz'
+          ucg_rate: 0.0
+          target: sgm.modules.encoders.modules.PCD_Scaler
+          params:
+              scaling_factor: 0.45
+              perturb_pcd_scale: 0.05
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6276d67855b3ad202ac5116e5a11734e9d811fc3
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform-pcdcond.yaml
@@ -0,0 +1,73 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.33
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+              # inp_size: 364
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.33
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              # inp_size: 364
+
+        - is_trainable: False
+          input_key: 'fps-xyz'
+          ucg_rate: 0.0
+          target: sgm.modules.encoders.modules.PCD_Scaler
+          params:
+              scaling_factor: 0.45
+              # perturb_pcd_scale: 0.05
+              perturb_pcd_scale: 0.0
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..90c0590ae2fad15ba9fe48d2dc5df2914ea99697
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-336-uniform.yaml
@@ -0,0 +1,64 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.33
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+              # inp_size: 364
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.33
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              # inp_size: 364
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-336.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-336.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5794b75216453fc1f63621fb6d5bcaff917a3af4
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-336.yaml
@@ -0,0 +1,61 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.33
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+              # inp_size: 364
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.33
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              # inp_size: 364
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: lognorm
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly copy.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly copy.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..63ac25fd9fd71b70aab3e9f82c1a24304fc0d7f1
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly copy.yaml	
@@ -0,0 +1,79 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   # ucg_rate: 0.463
+        #   ucg_rate: 0.562 # 0.316 ** 0.5
+        #   # legacy_ucg_value: None
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+        #   params:
+        #       arch: 'ViT-L-14'
+        #       version: 'openai'
+        #       # version: 'laion2b_s32b_b82k'
+        #       freeze: True
+        #       output_tokens: True
+        #       # inp_size: 448
+        #       inp_size: 224
+
+        - is_trainable: False
+          input_key: 'img'
+          # ucg_rate: 0.463
+          ucg_rate: 0.1 # 0.316 ** 0.5
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              inp_size: 518
+              # inp_size: 224
+
+        # - is_trainable: False
+        #   input_key: caption
+        #   # ucg_rate: 0.463
+        #   ucg_rate: 0.316
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+        #   params:
+        #     always_return_pooled: True
+        #     legacy: False
+        #     arch: 'ViT-L-14'
+        #     version: 'openai'
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6eb9fd9c12c42150b2cef3f7589f1d7b629ed0f0
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay-dinoonly.yaml
@@ -0,0 +1,80 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   # ucg_rate: 0.463
+        #   ucg_rate: 0.562 # 0.316 ** 0.5
+        #   # legacy_ucg_value: None
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+        #   params:
+        #       arch: 'ViT-L-14'
+        #       version: 'openai'
+        #       # version: 'laion2b_s32b_b82k'
+        #       freeze: True
+        #       output_tokens: True
+        #       # inp_size: 448
+        #       inp_size: 224
+
+        - is_trainable: False
+          input_key: 'img'
+          # ucg_rate: 0.463
+          ucg_rate: 0.1 # 0.316 ** 0.5
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              inp_size: 518
+              output_cls: True
+              # inp_size: 224
+
+        # - is_trainable: False
+        #   input_key: caption
+        #   # ucg_rate: 0.463
+        #   ucg_rate: 0.316
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+        #   params:
+        #     always_return_pooled: True
+        #     legacy: False
+        #     arch: 'ViT-L-14'
+        #     version: 'openai'
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..32169616d4db0e9b2375a21e79bcdbc71ec1d4dd
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform-clay.yaml
@@ -0,0 +1,80 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   # ucg_rate: 0.463
+        #   ucg_rate: 0.562 # 0.316 ** 0.5
+        #   # legacy_ucg_value: None
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+        #   params:
+        #       arch: 'ViT-L-14'
+        #       version: 'openai'
+        #       # version: 'laion2b_s32b_b82k'
+        #       freeze: True
+        #       output_tokens: True
+        #       # inp_size: 448
+        #       inp_size: 224
+
+        - is_trainable: False
+          input_key: 'img'
+          # ucg_rate: 0.463
+          # ucg_rate: 0.316 # 
+          ucg_rate: 0.15 # 
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              inp_size: 518
+              output_cls: True
+
+        - is_trainable: False
+          input_key: caption
+          # ucg_rate: 0.316
+          ucg_rate: 0.15
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+          params:
+            always_return_pooled: True
+            legacy: False
+            arch: 'ViT-L-14'
+            version: 'openai'
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1b59fa2e9b92d238e04a5005d9f101bcd015e206
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm-480-uniform.yaml
@@ -0,0 +1,66 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.316
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+              # inp_size: 448
+              inp_size: 224
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.316
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              # inp_size: 448
+              inp_size: 224
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm-lognorm.yaml b/sgm/configs/img23d-clipl-compat-fm-lognorm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c2ed1d97c1b75d800500b436c858e332c2521d01
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm-lognorm.yaml
@@ -0,0 +1,57 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: lognorm
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat-fm.yaml b/sgm/configs/img23d-clipl-compat-fm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d2431bc90ea99b9429d433f49d57dc19820c8368
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat-fm.yaml
@@ -0,0 +1,57 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        # params: # all follow default
+        #   path_type: 1000
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/img23d-clipl-compat.yaml b/sgm/configs/img23d-clipl-compat.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..52c691a5fe20ef60c964578faeda403ad85f51a6
--- /dev/null
+++ b/sgm/configs/img23d-clipl-compat.yaml
@@ -0,0 +1,71 @@
+ldm_configs:
+
+  scheduler_config:
+    target: sgm.lr_scheduler.LambdaLinearScheduler
+    params:
+      warm_up_steps: [10000]
+      cycle_lengths: [10000000000000]
+      f_start: [1.e-6]
+      f_max: [1.]
+      f_min: [1.]
+
+  denoiser_config:
+    target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+    params:
+      num_idx: 1000
+
+      scaling_config:
+        target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+      discretization_config:
+        target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+          params:
+              arch: 'ViT-L-14'
+              version: 'openai'
+              # version: 'laion2b_s32b_b82k'
+              freeze: True
+              output_tokens: True
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+    params:
+      loss_weighting_config:
+        target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
+      sigma_sampler_config:
+        target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+        params:
+          num_idx: 1000
+
+          discretization_config:
+            target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  sampler_config:
+    target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+    params:
+      # num_steps: 250
+      num_steps: 50
+
+      discretization_config:
+        target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+      guider_config:
+        target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+        params:
+          # scale: 1.0
+          scale: 6.5
\ No newline at end of file
diff --git a/sgm/configs/mv23d-clipl-compat-fm-lognorm.yaml b/sgm/configs/mv23d-clipl-compat-fm-lognorm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a0f1f480b82b309e8ee3635f0561f9d5b9d7c92c
--- /dev/null
+++ b/sgm/configs/mv23d-clipl-compat-fm-lognorm.yaml
@@ -0,0 +1,65 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.32
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageMVEmbedder
+          params:
+            open_clip_embedding_config:
+                target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+                params:
+                  arch: 'ViT-L-14'
+                  version: 'openai'
+                  freeze: True # TODO, add ModLN later
+                  output_tokens: True
+
+        - is_trainable: True
+          input_key: 'img-c'
+          ucg_rate: 0.32
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMV
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              arch: vitb
+              n_cond_frames: 4 # first 4 views as cond
+              modLN: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: lognorm
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm-noclip.yaml b/sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm-noclip.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dfa4832e97cb2ff0a4ba4e8932f0687f6f5834c7
--- /dev/null
+++ b/sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm-noclip.yaml
@@ -0,0 +1,67 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   ucg_rate: 0.32
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPImageMVEmbedder
+        #   params:
+        #     open_clip_embedding_config:
+        #         target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+        #         params:
+        #           arch: 'ViT-L-14'
+        #           version: 'openai'
+        #           freeze: True # TODO, add ModLN later
+        #           output_tokens: True
+
+        - is_trainable: True
+          input_key: 'img-c'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMVPlucker
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              arch: vitb
+              # n_cond_frames: 4 # first 4 views as cond
+              n_cond_frames: 6 # use all 6 views, to match instantMesh MV23D.
+              modLN: False
+              aug_c: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: lognorm
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm.yaml b/sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..37095be240e40a97efa5470d6a54b368bf0e4ead
--- /dev/null
+++ b/sgm/configs/mv23d-plucker-clipl-compat-fm-lognorm.yaml
@@ -0,0 +1,65 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+
+        - is_trainable: False
+          input_key: 'img'
+          ucg_rate: 0.32
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImageMVEmbedder
+          params:
+            open_clip_embedding_config:
+                target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+                params:
+                  arch: 'ViT-L-14'
+                  version: 'openai'
+                  freeze: True # TODO, add ModLN later
+                  output_tokens: True
+
+        - is_trainable: True
+          input_key: 'img-c'
+          ucg_rate: 0.32
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMVPlucker
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              arch: vitb
+              n_cond_frames: 4 # first 4 views as cond
+              modLN: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: lognorm
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage1-mv23d-i23dpt-noi23d.yaml b/sgm/configs/stage1-mv23d-i23dpt-noi23d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..12c562340d267802a4ef42df01a4f7b7a2314231
--- /dev/null
+++ b/sgm/configs/stage1-mv23d-i23dpt-noi23d.yaml
@@ -0,0 +1,80 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: caption
+        #   ucg_rate: 0.316
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+        #   params:
+        #     always_return_pooled: True
+        #     legacy: False
+        #     arch: 'ViT-L-14'
+        #     version: 'openai'
+
+        - is_trainable: True
+          input_key: 'img-c'
+          ucg_rate: 0.1
+          # ucg_rate: 0.316
+          # ucg_rate: 0.167 # overall 0.1 dropout.
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMVPlucker
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              # arch: vitb
+              arch: vits
+              inp_size: 322
+              n_cond_frames: 6 # first 4 views as cond
+              modLN: False
+              aug_c: True
+
+
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   ucg_rate: 0.6
+        #   # legacy_ucg_value: None
+        #   target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+        #   params:
+        #       freeze: True
+        #       arch: vitl
+        #       inp_size: 518
+        #       output_cls: True
+              # inp_size: 224
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: uniform
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage1-mv23d-i23dpt.yaml b/sgm/configs/stage1-mv23d-i23dpt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..704ef41226e68307324b25c9736df16ff3fbdb76
--- /dev/null
+++ b/sgm/configs/stage1-mv23d-i23dpt.yaml
@@ -0,0 +1,91 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: caption
+        #   ucg_rate: 0.316
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+        #   params:
+        #     always_return_pooled: True
+        #     legacy: False
+        #     arch: 'ViT-L-14'
+        #     version: 'openai'
+
+        - is_trainable: True
+          input_key: 'img-c'
+          # ucg_rate: 0.1
+          # ucg_rate: 0.316
+          ucg_rate: 0.167 # overall 0.1 dropout.
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMVPlucker
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              arch: vits
+              inp_size: 308
+              n_cond_frames: 6 # first 4 views as cond
+              modLN: False
+              aug_c: True
+
+
+        - is_trainable: False
+          input_key: 'img'
+          # ucg_rate: 0.463
+          # ucg_rate: 0.1 # 0.316 ** 0.5
+          # ucg_rate: 0.316
+          ucg_rate: 0.6
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              inp_size: 518
+              output_cls: True
+              # inp_size: 224
+
+        # - is_trainable: False
+        #   input_key: 'fps-xyz'
+        #   ucg_rate: 0.0
+        #   target: sgm.modules.encoders.modules.PCD_Scaler
+        #   params:
+        #       scaling_factor: 0.45
+        #       perturb_pcd_scale: 0.015
+        #       # perturb_pcd_scale: 0.0
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: uniform
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage1-mv23d-t23dpt.yaml b/sgm/configs/stage1-mv23d-t23dpt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b1c946266c913b08f50f9d767b93317f109f23da
--- /dev/null
+++ b/sgm/configs/stage1-mv23d-t23dpt.yaml
@@ -0,0 +1,74 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: caption
+          ucg_rate: 0.1
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+          params:
+            always_return_pooled: True
+            legacy: False
+            arch: 'ViT-L-14'
+            version: 'openai'
+
+        - is_trainable: True
+          input_key: 'img-c'
+          ucg_rate: 0.1
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMVPlucker
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              # arch: vits
+              arch: vitb
+              inp_size: 280
+              n_cond_frames: 4 # first 4 views as cond
+              modLN: False
+              aug_c: True
+
+        # - is_trainable: False
+        #   input_key: 'fps-xyz'
+        #   ucg_rate: 0.0
+        #   target: sgm.modules.encoders.modules.PCD_Scaler
+        #   params:
+        #       scaling_factor: 0.45
+        #       perturb_pcd_scale: 0.015
+        #       # perturb_pcd_scale: 0.0
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: uniform
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage1-t23d.yaml b/sgm/configs/stage1-t23d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f0c4eae6204014f921998cdcb1c219b11d7ef22c
--- /dev/null
+++ b/sgm/configs/stage1-t23d.yaml
@@ -0,0 +1,58 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: caption
+          ucg_rate: 0.1
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+          params:
+            always_return_pooled: True
+            legacy: False
+            arch: 'ViT-L-14'
+            version: 'openai'
+
+        # - is_trainable: False
+        #   input_key: 'fps-xyz'
+        #   ucg_rate: 0.0
+        #   target: sgm.modules.encoders.modules.PCD_Scaler
+        #   params:
+        #       scaling_factor: 0.45
+        #       perturb_pcd_scale: 0.015
+        #       # perturb_pcd_scale: 0.0
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: uniform
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage2-i23d.yaml b/sgm/configs/stage2-i23d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f3306f244bb13208b5d8c489670172fc40da39cc
--- /dev/null
+++ b/sgm/configs/stage2-i23d.yaml
@@ -0,0 +1,80 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: 'img'
+        #   # ucg_rate: 0.463
+        #   ucg_rate: 0.562 # 0.316 ** 0.5
+        #   # legacy_ucg_value: None
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+        #   params:
+        #       arch: 'ViT-L-14'
+        #       version: 'openai'
+        #       # version: 'laion2b_s32b_b82k'
+        #       freeze: True
+        #       output_tokens: True
+        #       # inp_size: 448
+        #       inp_size: 224
+
+        - is_trainable: False
+          input_key: 'img'
+          # ucg_rate: 0.463
+          ucg_rate: 0.1 # 0.316 ** 0.5
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              inp_size: 518
+              output_cls: True
+              # inp_size: 224
+
+        - is_trainable: False
+          input_key: 'fps-xyz'
+          ucg_rate: 0.0
+          target: sgm.modules.encoders.modules.PCD_Scaler
+          params:
+              scaling_factor: 0.45
+              # perturb_pcd_scale: 0.01 # as in VAE
+              # perturb_pcd_scale: 0.015
+              perturb_pcd_scale: 0.0
+
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          # snr_type: lognorm # too noisy?
+          snr_type: uniform
+          # path_type: Linear
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage2-mv23d-i23dpt.yaml b/sgm/configs/stage2-mv23d-i23dpt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..15eac078fe98763821dfe6c35791968d1fab9fff
--- /dev/null
+++ b/sgm/configs/stage2-mv23d-i23dpt.yaml
@@ -0,0 +1,90 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        # - is_trainable: False
+        #   input_key: caption
+        #   ucg_rate: 0.316
+        #   target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+        #   params:
+        #     always_return_pooled: True
+        #     legacy: False
+        #     arch: 'ViT-L-14'
+        #     version: 'openai'
+
+        - is_trainable: True
+          input_key: 'img-c'
+          # ucg_rate: 0.1
+          # ucg_rate: 0.316
+          ucg_rate: 0.167 # overall 0.1 dropout.
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedderMVPlucker
+          params:
+              freeze: False
+              enable_bf16: True
+              output_cls: False # return pooling
+              arch: vits
+              inp_size: 308
+              n_cond_frames: 6 # first 4 views as cond
+              modLN: False
+              aug_c: True
+
+
+        - is_trainable: False
+          input_key: 'img'
+          # ucg_rate: 0.463
+          # ucg_rate: 0.1 # 0.316 ** 0.5
+          # ucg_rate: 0.316
+          ucg_rate: 0.6
+          # legacy_ucg_value: None
+          target: sgm.modules.encoders.modules.FrozenDinov2ImageEmbedder
+          params:
+              freeze: True
+              arch: vitl
+              inp_size: 518
+              output_cls: True
+              # inp_size: 224
+
+        - is_trainable: False
+          input_key: 'fps-xyz'
+          ucg_rate: 0.0
+          target: sgm.modules.encoders.modules.PCD_Scaler
+          params:
+              scaling_factor: 0.45
+              perturb_pcd_scale: 0.0
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: uniform
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/stage2-t23d.yaml b/sgm/configs/stage2-t23d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..df4e81672b89c9da38b4410d0f1b7594a8e2549f
--- /dev/null
+++ b/sgm/configs/stage2-t23d.yaml
@@ -0,0 +1,57 @@
+ldm_configs:
+
+  # scheduler_config:
+  #   target: sgm.lr_scheduler.LambdaLinearScheduler
+  #   params:
+  #     warm_up_steps: [10000]
+  #     cycle_lengths: [10000000000000]
+  #     f_start: [1.e-6]
+  #     f_max: [1.]
+  #     f_min: [1.]
+
+  # denoiser_config:
+  #   target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+  #   params:
+  #     num_idx: 1000
+
+  #     scaling_config:
+  #       target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+  #     discretization_config:
+  #       target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: caption
+          ucg_rate: 0.1
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+          params:
+            always_return_pooled: True
+            legacy: False
+            arch: 'ViT-L-14'
+            version: 'openai'
+
+        - is_trainable: False
+          input_key: 'fps-xyz'
+          ucg_rate: 0.0
+          target: sgm.modules.encoders.modules.PCD_Scaler
+          params:
+              scaling_factor: 0.45
+              perturb_pcd_scale: 0.0
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: uniform
+          path_type: GVP
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      # scale: 1.0
+      scale: 5.0
\ No newline at end of file
diff --git a/sgm/configs/t23d-clipl-compat-fm.yaml b/sgm/configs/t23d-clipl-compat-fm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e9da4da9ac3d1d61df3bf2c9c914a06dc37f2cce
--- /dev/null
+++ b/sgm/configs/t23d-clipl-compat-fm.yaml
@@ -0,0 +1,29 @@
+ldm_configs:
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: caption
+          ucg_rate: 0.1
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+          params:
+            always_return_pooled: True
+            legacy: False
+            arch: 'ViT-L-14'
+            version: 'openai'
+
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.FMLoss
+    params:
+      transport_config:
+        target: transport.create_transport
+        params: # all follow default
+          snr_type: lognorm
+
+  guider_config:
+    target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+    params:
+      scale: 4.0
\ No newline at end of file
diff --git a/sgm/configs/txt2img-clipl-compat.yaml b/sgm/configs/txt2img-clipl-compat.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..62ffa17bec26733f49bfc034737bda860a572102
--- /dev/null
+++ b/sgm/configs/txt2img-clipl-compat.yaml
@@ -0,0 +1,60 @@
+ldm_configs:
+
+  scheduler_config:
+    target: sgm.lr_scheduler.LambdaLinearScheduler
+    params:
+      warm_up_steps: [10000]
+      cycle_lengths: [10000000000000]
+      f_start: [1.e-6]
+      f_max: [1.]
+      f_min: [1.]
+
+  denoiser_config:
+    target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+    params:
+      num_idx: 1000
+
+      scaling_config:
+        target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+      discretization_config:
+        target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  conditioner_config:
+    target: sgm.modules.GeneralConditioner
+    params:
+      emb_models:
+        - is_trainable: False
+          input_key: caption
+          ucg_rate: 0.1
+          legacy_ucg_value: ""
+          target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+          params:
+            always_return_pooled: True
+
+  loss_fn_config:
+    target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+    params:
+      loss_weighting_config:
+        target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
+      sigma_sampler_config:
+        target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+        params:
+          num_idx: 1000
+
+          discretization_config:
+            target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+  sampler_config:
+    target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+    params:
+      # num_steps: 250
+      num_steps: 50
+
+      discretization_config:
+        target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+      guider_config:
+        target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+        params:
+          # scale: 1.0
+          scale: 6.5
\ No newline at end of file
diff --git a/sgm/configs/txt2img-clipl.yaml b/sgm/configs/txt2img-clipl.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cb66ede901b1aa1acb18d162b88912a2e6eab0ce
--- /dev/null
+++ b/sgm/configs/txt2img-clipl.yaml
@@ -0,0 +1,184 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+    log_keys:
+      - txt
+
+    scheduler_config:
+      target: sgm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [10000]
+        cycle_lengths: [10000000000000]
+        f_start: [1.e-6]
+        f_max: [1.]
+        f_min: [1.]
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [1, 2, 4]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        num_classes: sequential
+        adm_in_channels: 1792
+        num_heads: 1
+        transformer_depth: 1
+        context_dim: 768
+        spatial_transformer_attn_type: softmax-xformers
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: True
+            input_key: txt
+            ucg_rate: 0.1
+            legacy_ucg_value: ""
+            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+            params:
+              always_return_pooled: True
+
+          - is_trainable: False
+            ucg_rate: 0.1
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            ucg_rate: 0.1
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        ckpt_path: CKPT_PATH
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+          params:
+            num_idx: 1000
+
+            discretization_config:
+              target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 7.5
+
+data:
+  target: sgm.data.dataset.StableDataModuleFromConfig
+  params:
+    train:
+      datapipeline:
+        urls:
+          # USER: adapt this path the root of your custom dataset
+          - DATA_PATH
+        pipeline_config:
+          shardshuffle: 10000
+          sample_shuffle: 10000
+
+
+        decoders:
+          - pil
+
+        postprocessors:
+          - target: sdata.mappers.TorchVisionImageTransforms
+            params:
+              key: jpg # USER: you might wanna adapt this for your custom dataset
+              transforms:
+                - target: torchvision.transforms.Resize
+                  params:
+                    size: 256
+                    interpolation: 3
+                - target: torchvision.transforms.ToTensor
+          - target: sdata.mappers.Rescaler
+            # USER: you might wanna use non-default parameters due to your custom dataset
+          - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
+            # USER: you might wanna use non-default parameters due to your custom dataset
+
+      loader:
+        batch_size: 64
+        num_workers: 6
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        enable_autocast: False
+        batch_frequency: 1000
+        max_images: 8
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 8
+          n_rows: 2
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 1000
\ No newline at end of file
diff --git a/sgm/data/__init__.py b/sgm/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7664a25c655c376bd1a7b0ccbaca7b983a2bf9ad
--- /dev/null
+++ b/sgm/data/__init__.py
@@ -0,0 +1 @@
+from .dataset import StableDataModuleFromConfig
diff --git a/sgm/data/cifar10.py b/sgm/data/cifar10.py
new file mode 100644
index 0000000000000000000000000000000000000000..6083646f136bad308a0485843b89234cf7a9d6cd
--- /dev/null
+++ b/sgm/data/cifar10.py
@@ -0,0 +1,67 @@
+import pytorch_lightning as pl
+import torchvision
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+
+
+class CIFAR10DataDictWrapper(Dataset):
+    def __init__(self, dset):
+        super().__init__()
+        self.dset = dset
+
+    def __getitem__(self, i):
+        x, y = self.dset[i]
+        return {"jpg": x, "cls": y}
+
+    def __len__(self):
+        return len(self.dset)
+
+
+class CIFAR10Loader(pl.LightningDataModule):
+    def __init__(self, batch_size, num_workers=0, shuffle=True):
+        super().__init__()
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+        )
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.shuffle = shuffle
+        self.train_dataset = CIFAR10DataDictWrapper(
+            torchvision.datasets.CIFAR10(
+                root=".data/", train=True, download=True, transform=transform
+            )
+        )
+        self.test_dataset = CIFAR10DataDictWrapper(
+            torchvision.datasets.CIFAR10(
+                root=".data/", train=False, download=True, transform=transform
+            )
+        )
+
+    def prepare_data(self):
+        pass
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
diff --git a/sgm/data/dataset.py b/sgm/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..b726149996591c6c3db69230e1bb68c07d2faa12
--- /dev/null
+++ b/sgm/data/dataset.py
@@ -0,0 +1,80 @@
+from typing import Optional
+
+import torchdata.datapipes.iter
+import webdataset as wds
+from omegaconf import DictConfig
+from pytorch_lightning import LightningDataModule
+
+try:
+    from sdata import create_dataset, create_dummy_dataset, create_loader
+except ImportError as e:
+    print("#" * 100)
+    print("Datasets not yet available")
+    print("to enable, we need to add stable-datasets as a submodule")
+    print("please use ``git submodule update --init --recursive``")
+    print("and do ``pip install -e stable-datasets/`` from the root of this repo")
+    print("#" * 100)
+    exit(1)
+
+
+class StableDataModuleFromConfig(LightningDataModule):
+    def __init__(
+        self,
+        train: DictConfig,
+        validation: Optional[DictConfig] = None,
+        test: Optional[DictConfig] = None,
+        skip_val_loader: bool = False,
+        dummy: bool = False,
+    ):
+        super().__init__()
+        self.train_config = train
+        assert (
+            "datapipeline" in self.train_config and "loader" in self.train_config
+        ), "train config requires the fields `datapipeline` and `loader`"
+
+        self.val_config = validation
+        if not skip_val_loader:
+            if self.val_config is not None:
+                assert (
+                    "datapipeline" in self.val_config and "loader" in self.val_config
+                ), "validation config requires the fields `datapipeline` and `loader`"
+            else:
+                print(
+                    "Warning: No Validation datapipeline defined, using that one from training"
+                )
+                self.val_config = train
+
+        self.test_config = test
+        if self.test_config is not None:
+            assert (
+                "datapipeline" in self.test_config and "loader" in self.test_config
+            ), "test config requires the fields `datapipeline` and `loader`"
+
+        self.dummy = dummy
+        if self.dummy:
+            print("#" * 100)
+            print("USING DUMMY DATASET: HOPE YOU'RE DEBUGGING ;)")
+            print("#" * 100)
+
+    def setup(self, stage: str) -> None:
+        print("Preparing datasets")
+        if self.dummy:
+            data_fn = create_dummy_dataset
+        else:
+            data_fn = create_dataset
+
+        self.train_datapipeline = data_fn(**self.train_config.datapipeline)
+        if self.val_config:
+            self.val_datapipeline = data_fn(**self.val_config.datapipeline)
+        if self.test_config:
+            self.test_datapipeline = data_fn(**self.test_config.datapipeline)
+
+    def train_dataloader(self) -> torchdata.datapipes.iter.IterDataPipe:
+        loader = create_loader(self.train_datapipeline, **self.train_config.loader)
+        return loader
+
+    def val_dataloader(self) -> wds.DataPipeline:
+        return create_loader(self.val_datapipeline, **self.val_config.loader)
+
+    def test_dataloader(self) -> wds.DataPipeline:
+        return create_loader(self.test_datapipeline, **self.test_config.loader)
diff --git a/sgm/data/mnist.py b/sgm/data/mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..dea4d7e670666bec80ecb22aa89603345e173d09
--- /dev/null
+++ b/sgm/data/mnist.py
@@ -0,0 +1,85 @@
+import pytorch_lightning as pl
+import torchvision
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+
+
+class MNISTDataDictWrapper(Dataset):
+    def __init__(self, dset):
+        super().__init__()
+        self.dset = dset
+
+    def __getitem__(self, i):
+        x, y = self.dset[i]
+        return {"jpg": x, "cls": y}
+
+    def __len__(self):
+        return len(self.dset)
+
+
+class MNISTLoader(pl.LightningDataModule):
+    def __init__(self, batch_size, num_workers=0, prefetch_factor=2, shuffle=True):
+        super().__init__()
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+        )
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.prefetch_factor = prefetch_factor if num_workers > 0 else 0
+        self.shuffle = shuffle
+        self.train_dataset = MNISTDataDictWrapper(
+            torchvision.datasets.MNIST(
+                root=".data/", train=True, download=True, transform=transform
+            )
+        )
+        self.test_dataset = MNISTDataDictWrapper(
+            torchvision.datasets.MNIST(
+                root=".data/", train=False, download=True, transform=transform
+            )
+        )
+
+    def prepare_data(self):
+        pass
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+
+if __name__ == "__main__":
+    dset = MNISTDataDictWrapper(
+        torchvision.datasets.MNIST(
+            root=".data/",
+            train=False,
+            download=True,
+            transform=transforms.Compose(
+                [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+            ),
+        )
+    )
+    ex = dset[0]
diff --git a/sgm/inference/api.py b/sgm/inference/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..a359a67bcd9740acc9e320d2f26dc6a3befb36e0
--- /dev/null
+++ b/sgm/inference/api.py
@@ -0,0 +1,385 @@
+import pathlib
+from dataclasses import asdict, dataclass
+from enum import Enum
+from typing import Optional
+
+from omegaconf import OmegaConf
+
+from sgm.inference.helpers import (Img2ImgDiscretizationWrapper, do_img2img,
+                                   do_sample)
+from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
+                                                   DPMPP2SAncestralSampler,
+                                                   EulerAncestralSampler,
+                                                   EulerEDMSampler,
+                                                   HeunEDMSampler,
+                                                   LinearMultistepSampler)
+from sgm.util import load_model_from_config
+
+
+class ModelArchitecture(str, Enum):
+    SD_2_1 = "stable-diffusion-v2-1"
+    SD_2_1_768 = "stable-diffusion-v2-1-768"
+    SDXL_V0_9_BASE = "stable-diffusion-xl-v0-9-base"
+    SDXL_V0_9_REFINER = "stable-diffusion-xl-v0-9-refiner"
+    SDXL_V1_BASE = "stable-diffusion-xl-v1-base"
+    SDXL_V1_REFINER = "stable-diffusion-xl-v1-refiner"
+
+
+class Sampler(str, Enum):
+    EULER_EDM = "EulerEDMSampler"
+    HEUN_EDM = "HeunEDMSampler"
+    EULER_ANCESTRAL = "EulerAncestralSampler"
+    DPMPP2S_ANCESTRAL = "DPMPP2SAncestralSampler"
+    DPMPP2M = "DPMPP2MSampler"
+    LINEAR_MULTISTEP = "LinearMultistepSampler"
+
+
+class Discretization(str, Enum):
+    LEGACY_DDPM = "LegacyDDPMDiscretization"
+    EDM = "EDMDiscretization"
+
+
+class Guider(str, Enum):
+    VANILLA = "VanillaCFG"
+    IDENTITY = "IdentityGuider"
+
+
+class Thresholder(str, Enum):
+    NONE = "None"
+
+
+@dataclass
+class SamplingParams:
+    width: int = 1024
+    height: int = 1024
+    steps: int = 50
+    sampler: Sampler = Sampler.DPMPP2M
+    discretization: Discretization = Discretization.LEGACY_DDPM
+    guider: Guider = Guider.VANILLA
+    thresholder: Thresholder = Thresholder.NONE
+    scale: float = 6.0
+    aesthetic_score: float = 5.0
+    negative_aesthetic_score: float = 5.0
+    img2img_strength: float = 1.0
+    orig_width: int = 1024
+    orig_height: int = 1024
+    crop_coords_top: int = 0
+    crop_coords_left: int = 0
+    sigma_min: float = 0.0292
+    sigma_max: float = 14.6146
+    rho: float = 3.0
+    s_churn: float = 0.0
+    s_tmin: float = 0.0
+    s_tmax: float = 999.0
+    s_noise: float = 1.0
+    eta: float = 1.0
+    order: int = 4
+
+
+@dataclass
+class SamplingSpec:
+    width: int
+    height: int
+    channels: int
+    factor: int
+    is_legacy: bool
+    config: str
+    ckpt: str
+    is_guided: bool
+
+
+model_specs = {
+    ModelArchitecture.SD_2_1: SamplingSpec(
+        height=512,
+        width=512,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_2_1.yaml",
+        ckpt="v2-1_512-ema-pruned.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SD_2_1_768: SamplingSpec(
+        height=768,
+        width=768,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_2_1_768.yaml",
+        ckpt="v2-1_768-ema-pruned.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V0_9_BASE: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=False,
+        config="sd_xl_base.yaml",
+        ckpt="sd_xl_base_0.9.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V0_9_REFINER: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_xl_refiner.yaml",
+        ckpt="sd_xl_refiner_0.9.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V1_BASE: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=False,
+        config="sd_xl_base.yaml",
+        ckpt="sd_xl_base_1.0.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V1_REFINER: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_xl_refiner.yaml",
+        ckpt="sd_xl_refiner_1.0.safetensors",
+        is_guided=True,
+    ),
+}
+
+
+class SamplingPipeline:
+    def __init__(
+        self,
+        model_id: ModelArchitecture,
+        model_path="checkpoints",
+        config_path="configs/inference",
+        device="cuda",
+        use_fp16=True,
+    ) -> None:
+        if model_id not in model_specs:
+            raise ValueError(f"Model {model_id} not supported")
+        self.model_id = model_id
+        self.specs = model_specs[self.model_id]
+        self.config = str(pathlib.Path(config_path, self.specs.config))
+        self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
+        self.device = device
+        self.model = self._load_model(device=device, use_fp16=use_fp16)
+
+    def _load_model(self, device="cuda", use_fp16=True):
+        config = OmegaConf.load(self.config)
+        model = load_model_from_config(config, self.ckpt)
+        if model is None:
+            raise ValueError(f"Model {self.model_id} could not be loaded")
+        model.to(device)
+        if use_fp16:
+            model.conditioner.half()
+            model.model.half()
+        return model
+
+    def text_to_image(
+        self,
+        params: SamplingParams,
+        prompt: str,
+        negative_prompt: str = "",
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+        value_dict = asdict(params)
+        value_dict["prompt"] = prompt
+        value_dict["negative_prompt"] = negative_prompt
+        value_dict["target_width"] = params.width
+        value_dict["target_height"] = params.height
+        return do_sample(
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            params.height,
+            params.width,
+            self.specs.channels,
+            self.specs.factor,
+            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
+            return_latents=return_latents,
+            filter=None,
+        )
+
+    def image_to_image(
+        self,
+        params: SamplingParams,
+        image,
+        prompt: str,
+        negative_prompt: str = "",
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+
+        if params.img2img_strength < 1.0:
+            sampler.discretization = Img2ImgDiscretizationWrapper(
+                sampler.discretization,
+                strength=params.img2img_strength,
+            )
+        height, width = image.shape[2], image.shape[3]
+        value_dict = asdict(params)
+        value_dict["prompt"] = prompt
+        value_dict["negative_prompt"] = negative_prompt
+        value_dict["target_width"] = width
+        value_dict["target_height"] = height
+        return do_img2img(
+            image,
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
+            return_latents=return_latents,
+            filter=None,
+        )
+
+    def refiner(
+        self,
+        params: SamplingParams,
+        image,
+        prompt: str,
+        negative_prompt: Optional[str] = None,
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+        value_dict = {
+            "orig_width": image.shape[3] * 8,
+            "orig_height": image.shape[2] * 8,
+            "target_width": image.shape[3] * 8,
+            "target_height": image.shape[2] * 8,
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "crop_coords_top": 0,
+            "crop_coords_left": 0,
+            "aesthetic_score": 6.0,
+            "negative_aesthetic_score": 2.5,
+        }
+
+        return do_img2img(
+            image,
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            skip_encode=True,
+            return_latents=return_latents,
+            filter=None,
+        )
+
+
+def get_guider_config(params: SamplingParams):
+    if params.guider == Guider.IDENTITY:
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
+        }
+    elif params.guider == Guider.VANILLA:
+        scale = params.scale
+
+        thresholder = params.thresholder
+
+        if thresholder == Thresholder.NONE:
+            dyn_thresh_config = {
+                "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
+            }
+        else:
+            raise NotImplementedError
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
+            "params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
+        }
+    else:
+        raise NotImplementedError
+    return guider_config
+
+
+def get_discretization_config(params: SamplingParams):
+    if params.discretization == Discretization.LEGACY_DDPM:
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
+        }
+    elif params.discretization == Discretization.EDM:
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
+            "params": {
+                "sigma_min": params.sigma_min,
+                "sigma_max": params.sigma_max,
+                "rho": params.rho,
+            },
+        }
+    else:
+        raise ValueError(f"unknown discretization {params.discretization}")
+    return discretization_config
+
+
+def get_sampler_config(params: SamplingParams):
+    discretization_config = get_discretization_config(params)
+    guider_config = get_guider_config(params)
+    sampler = None
+    if params.sampler == Sampler.EULER_EDM:
+        return EulerEDMSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            s_churn=params.s_churn,
+            s_tmin=params.s_tmin,
+            s_tmax=params.s_tmax,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.HEUN_EDM:
+        return HeunEDMSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            s_churn=params.s_churn,
+            s_tmin=params.s_tmin,
+            s_tmax=params.s_tmax,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.EULER_ANCESTRAL:
+        return EulerAncestralSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            eta=params.eta,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.DPMPP2S_ANCESTRAL:
+        return DPMPP2SAncestralSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            eta=params.eta,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.DPMPP2M:
+        return DPMPP2MSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            verbose=True,
+        )
+    if params.sampler == Sampler.LINEAR_MULTISTEP:
+        return LinearMultistepSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            order=params.order,
+            verbose=True,
+        )
+
+    raise ValueError(f"unknown sampler {params.sampler}!")
diff --git a/sgm/inference/helpers.py b/sgm/inference/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..31b0ec3dc414bf522261e35f73805810cd35582d
--- /dev/null
+++ b/sgm/inference/helpers.py
@@ -0,0 +1,305 @@
+import math
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+from einops import rearrange
+from imwatermark import WatermarkEncoder
+from omegaconf import ListConfig
+from PIL import Image
+from torch import autocast
+
+from sgm.util import append_dims
+
+
+class WatermarkEmbedder:
+    def __init__(self, watermark):
+        self.watermark = watermark
+        self.num_bits = len(WATERMARK_BITS)
+        self.encoder = WatermarkEncoder()
+        self.encoder.set_watermark("bits", self.watermark)
+
+    def __call__(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Adds a predefined watermark to the input image
+
+        Args:
+            image: ([N,] B, RGB, H, W) in range [0, 1]
+
+        Returns:
+            same as input but watermarked
+        """
+        squeeze = len(image.shape) == 4
+        if squeeze:
+            image = image[None, ...]
+        n = image.shape[0]
+        image_np = rearrange(
+            (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
+        ).numpy()[:, :, :, ::-1]
+        # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
+        # watermarking libary expects input as cv2 BGR format
+        for k in range(image_np.shape[0]):
+            image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
+        image = torch.from_numpy(
+            rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)
+        ).to(image.device)
+        image = torch.clamp(image / 255, min=0.0, max=1.0)
+        if squeeze:
+            image = image[0]
+        return image
+
+
+# A fixed 48-bit message that was choosen at random
+# WATERMARK_MESSAGE = 0xB3EC907BB19E
+WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
+# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
+WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
+embed_watermark = WatermarkEmbedder(WATERMARK_BITS)
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list({x.input_key for x in conditioner.embedders})
+
+
+def perform_save_locally(save_path, samples):
+    os.makedirs(os.path.join(save_path), exist_ok=True)
+    base_count = len(os.listdir(os.path.join(save_path)))
+    samples = embed_watermark(samples)
+    for sample in samples:
+        sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
+        Image.fromarray(sample.astype(np.uint8)).save(
+            os.path.join(save_path, f"{base_count:09}.png")
+        )
+        base_count += 1
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+def do_sample(
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    H,
+    W,
+    C,
+    F,
+    force_uc_zero_embeddings: Optional[List] = None,
+    batch2model_input: Optional[List] = None,
+    return_latents=False,
+    filter=None,
+    device="cuda",
+):
+    if force_uc_zero_embeddings is None:
+        force_uc_zero_embeddings = []
+    if batch2model_input is None:
+        batch2model_input = []
+
+    with torch.no_grad():
+        with autocast(device) as precision_scope:
+            with model.ema_scope():
+                num_samples = [num_samples]
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    num_samples,
+                )
+                for key in batch:
+                    if isinstance(batch[key], torch.Tensor):
+                        print(key, batch[key].shape)
+                    elif isinstance(batch[key], list):
+                        print(key, [len(l) for l in batch[key]])
+                    else:
+                        print(key, batch[key])
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                )
+
+                for k in c:
+                    if not k == "crossattn":
+                        c[k], uc[k] = map(
+                            lambda y: y[k][: math.prod(num_samples)].to(device), (c, uc)
+                        )
+
+                additional_model_inputs = {}
+                for k in batch2model_input:
+                    additional_model_inputs[k] = batch[k]
+
+                shape = (math.prod(num_samples), C, H // F, W // F)
+                randn = torch.randn(shape).to(device)
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
+    # Hardcoded demo setups; might undergo some changes in the future
+
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "txt":
+            batch["txt"] = (
+                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+            batch_uc["txt"] = (
+                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+        elif key == "original_size_as_tuple":
+            batch["original_size_as_tuple"] = (
+                torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "crop_coords_top_left":
+            batch["crop_coords_top_left"] = (
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "aesthetic_score":
+            batch["aesthetic_score"] = (
+                torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
+            )
+            batch_uc["aesthetic_score"] = (
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+
+        elif key == "target_size_as_tuple":
+            batch["target_size_as_tuple"] = (
+                torch.tensor([value_dict["target_height"], value_dict["target_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+    return batch, batch_uc
+
+
+def get_input_image_tensor(image: Image.Image, device="cuda"):
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+    width, height = map(
+        lambda x: x - x % 64, (w, h)
+    )  # resize to integer multiple of 64
+    image = image.resize((width, height))
+    image_array = np.array(image.convert("RGB"))
+    image_array = image_array[None].transpose(0, 3, 1, 2)
+    image_tensor = torch.from_numpy(image_array).to(dtype=torch.float32) / 127.5 - 1.0
+    return image_tensor.to(device)
+
+
+def do_img2img(
+    img,
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    force_uc_zero_embeddings=[],
+    additional_kwargs={},
+    offset_noise_level: float = 0.0,
+    return_latents=False,
+    skip_encode=False,
+    filter=None,
+    device="cuda",
+):
+    with torch.no_grad():
+        with autocast(device) as precision_scope:
+            with model.ema_scope():
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [num_samples],
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                )
+
+                for k in c:
+                    c[k], uc[k] = map(lambda y: y[k][:num_samples].to(device), (c, uc))
+
+                for k in additional_kwargs:
+                    c[k] = uc[k] = additional_kwargs[k]
+                if skip_encode:
+                    z = img
+                else:
+                    z = model.encode_first_stage(img)
+                noise = torch.randn_like(z)
+                sigmas = sampler.discretization(sampler.num_steps)
+                sigma = sigmas[0].to(z.device)
+
+                if offset_noise_level > 0.0:
+                    noise = noise + offset_noise_level * append_dims(
+                        torch.randn(z.shape[0], device=z.device), z.ndim
+                    )
+                noised_z = z + noise * append_dims(sigma, z.ndim)
+                noised_z = noised_z / torch.sqrt(
+                    1.0 + sigmas[0] ** 2.0
+                )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+
+                def denoiser(x, sigma, c):
+                    return model.denoiser(model.model, x, sigma, c)
+
+                samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if return_latents:
+                    return samples, samples_z
+                return samples
diff --git a/sgm/lr_scheduler.py b/sgm/lr_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f4d384c1fcaff0df13e0564450d3fa972ace42
--- /dev/null
+++ b/sgm/lr_scheduler.py
@@ -0,0 +1,135 @@
+import numpy as np
+
+
+class LambdaWarmUpCosineScheduler:
+    """
+    note: use with a base_lr of 1.0
+    """
+
+    def __init__(
+        self,
+        warm_up_steps,
+        lr_min,
+        lr_max,
+        lr_start,
+        max_decay_steps,
+        verbosity_interval=0,
+    ):
+        self.lr_warm_up_steps = warm_up_steps
+        self.lr_start = lr_start
+        self.lr_min = lr_min
+        self.lr_max = lr_max
+        self.lr_max_decay_steps = max_decay_steps
+        self.last_lr = 0.0
+        self.verbosity_interval = verbosity_interval
+
+    def schedule(self, n, **kwargs):
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+        if n < self.lr_warm_up_steps:
+            lr = (
+                self.lr_max - self.lr_start
+            ) / self.lr_warm_up_steps * n + self.lr_start
+            self.last_lr = lr
+            return lr
+        else:
+            t = (n - self.lr_warm_up_steps) / (
+                self.lr_max_decay_steps - self.lr_warm_up_steps
+            )
+            t = min(t, 1.0)
+            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
+                1 + np.cos(t * np.pi)
+            )
+            self.last_lr = lr
+            return lr
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaWarmUpCosineScheduler2:
+    """
+    supports repeated iterations, configurable via lists
+    note: use with a base_lr of 1.0.
+    """
+
+    def __init__(
+        self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0
+    ):
+        assert (
+            len(warm_up_steps)
+            == len(f_min)
+            == len(f_max)
+            == len(f_start)
+            == len(cycle_lengths)
+        )
+        self.lr_warm_up_steps = warm_up_steps
+        self.f_start = f_start
+        self.f_min = f_min
+        self.f_max = f_max
+        self.cycle_lengths = cycle_lengths
+        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
+        self.last_f = 0.0
+        self.verbosity_interval = verbosity_interval
+
+    def find_in_interval(self, n):
+        interval = 0
+        for cl in self.cum_cycles[1:]:
+            if n <= cl:
+                return interval
+            interval += 1
+
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(
+                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                    f"current cycle {cycle}"
+                )
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
+                cycle
+            ] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            t = (n - self.lr_warm_up_steps[cycle]) / (
+                self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
+            )
+            t = min(t, 1.0)
+            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
+                1 + np.cos(t * np.pi)
+            )
+            self.last_f = f
+            return f
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(
+                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                    f"current cycle {cycle}"
+                )
+
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
+                cycle
+            ] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
+                self.cycle_lengths[cycle] - n
+            ) / (self.cycle_lengths[cycle])
+            self.last_f = f
+            return f
diff --git a/sgm/models/__init__.py b/sgm/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c410b3747afc208e4204c8f140170e0a7808eace
--- /dev/null
+++ b/sgm/models/__init__.py
@@ -0,0 +1,2 @@
+from .autoencoder import AutoencodingEngine
+from .diffusion import DiffusionEngine
diff --git a/sgm/models/__pycache__/__init__.cpython-39.pyc b/sgm/models/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6011db5f39a94820a93ede8e0d10c17236d404ce
Binary files /dev/null and b/sgm/models/__pycache__/__init__.cpython-39.pyc differ
diff --git a/sgm/models/__pycache__/autoencoder.cpython-39.pyc b/sgm/models/__pycache__/autoencoder.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..176dd9d2c131e5440e0db6250e99cdd04ed8225b
Binary files /dev/null and b/sgm/models/__pycache__/autoencoder.cpython-39.pyc differ
diff --git a/sgm/models/__pycache__/diffusion.cpython-39.pyc b/sgm/models/__pycache__/diffusion.cpython-39.pyc
new file mode 100644
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Binary files /dev/null and b/sgm/models/__pycache__/diffusion.cpython-39.pyc differ
diff --git a/sgm/models/autoencoder.py b/sgm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..2949b91011a2be7a6b8ca17ce260812f20ce8b75
--- /dev/null
+++ b/sgm/models/autoencoder.py
@@ -0,0 +1,615 @@
+import logging
+import math
+import re
+from abc import abstractmethod
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import pytorch_lightning as pl
+import torch
+import torch.nn as nn
+from einops import rearrange
+from packaging import version
+
+from ..modules.autoencoding.regularizers import AbstractRegularizer
+from ..modules.ema import LitEma
+from ..util import (default, get_nested_attribute, get_obj_from_str,
+                    instantiate_from_config)
+
+logpy = logging.getLogger(__name__)
+
+
+class AbstractAutoencoder(pl.LightningModule):
+    """
+    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
+    unCLIP models, etc. Hence, it is fairly general, and specific features
+    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
+    """
+
+    def __init__(
+        self,
+        ema_decay: Union[None, float] = None,
+        monitor: Union[None, str] = None,
+        input_key: str = "jpg",
+    ):
+        super().__init__()
+
+        self.input_key = input_key
+        self.use_ema = ema_decay is not None
+        if monitor is not None:
+            self.monitor = monitor
+
+        if self.use_ema:
+            self.model_ema = LitEma(self, decay=ema_decay)
+            logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if version.parse(torch.__version__) >= version.parse("2.0.0"):
+            self.automatic_optimization = False
+
+    def apply_ckpt(self, ckpt: Union[None, str, dict]):
+        if ckpt is None:
+            return
+        if isinstance(ckpt, str):
+            ckpt = {
+                "target": "sgm.modules.checkpoint.CheckpointEngine",
+                "params": {"ckpt_path": ckpt},
+            }
+        engine = instantiate_from_config(ckpt)
+        engine(self)
+
+    @abstractmethod
+    def get_input(self, batch) -> Any:
+        raise NotImplementedError()
+
+    def on_train_batch_end(self, *args, **kwargs):
+        # for EMA computation
+        if self.use_ema:
+            self.model_ema(self)
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                logpy.info(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    logpy.info(f"{context}: Restored training weights")
+
+    @abstractmethod
+    def encode(self, *args, **kwargs) -> torch.Tensor:
+        raise NotImplementedError("encode()-method of abstract base class called")
+
+    @abstractmethod
+    def decode(self, *args, **kwargs) -> torch.Tensor:
+        raise NotImplementedError("decode()-method of abstract base class called")
+
+    def instantiate_optimizer_from_config(self, params, lr, cfg):
+        logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
+        return get_obj_from_str(cfg["target"])(
+            params, lr=lr, **cfg.get("params", dict())
+        )
+
+    def configure_optimizers(self) -> Any:
+        raise NotImplementedError()
+
+
+class AutoencodingEngine(AbstractAutoencoder):
+    """
+    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
+    (we also restore them explicitly as special cases for legacy reasons).
+    Regularizations such as KL or VQ are moved to the regularizer class.
+    """
+
+    def __init__(
+        self,
+        *args,
+        encoder_config: Dict,
+        decoder_config: Dict,
+        loss_config: Dict,
+        regularizer_config: Dict,
+        optimizer_config: Union[Dict, None] = None,
+        lr_g_factor: float = 1.0,
+        trainable_ae_params: Optional[List[List[str]]] = None,
+        ae_optimizer_args: Optional[List[dict]] = None,
+        trainable_disc_params: Optional[List[List[str]]] = None,
+        disc_optimizer_args: Optional[List[dict]] = None,
+        disc_start_iter: int = 0,
+        diff_boost_factor: float = 3.0,
+        ckpt_engine: Union[None, str, dict] = None,
+        ckpt_path: Optional[str] = None,
+        additional_decode_keys: Optional[List[str]] = None,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.automatic_optimization = False  # pytorch lightning
+
+        self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
+        self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
+        self.loss: torch.nn.Module = instantiate_from_config(loss_config)
+        self.regularization: AbstractRegularizer = instantiate_from_config(
+            regularizer_config
+        )
+        self.optimizer_config = default(
+            optimizer_config, {"target": "torch.optim.Adam"}
+        )
+        self.diff_boost_factor = diff_boost_factor
+        self.disc_start_iter = disc_start_iter
+        self.lr_g_factor = lr_g_factor
+        self.trainable_ae_params = trainable_ae_params
+        if self.trainable_ae_params is not None:
+            self.ae_optimizer_args = default(
+                ae_optimizer_args,
+                [{} for _ in range(len(self.trainable_ae_params))],
+            )
+            assert len(self.ae_optimizer_args) == len(self.trainable_ae_params)
+        else:
+            self.ae_optimizer_args = [{}]  # makes type consitent
+
+        self.trainable_disc_params = trainable_disc_params
+        if self.trainable_disc_params is not None:
+            self.disc_optimizer_args = default(
+                disc_optimizer_args,
+                [{} for _ in range(len(self.trainable_disc_params))],
+            )
+            assert len(self.disc_optimizer_args) == len(self.trainable_disc_params)
+        else:
+            self.disc_optimizer_args = [{}]  # makes type consitent
+
+        if ckpt_path is not None:
+            assert ckpt_engine is None, "Can't set ckpt_engine and ckpt_path"
+            logpy.warn("Checkpoint path is deprecated, use `checkpoint_egnine` instead")
+        self.apply_ckpt(default(ckpt_path, ckpt_engine))
+        self.additional_decode_keys = set(default(additional_decode_keys, []))
+
+    def get_input(self, batch: Dict) -> torch.Tensor:
+        # assuming unified data format, dataloader returns a dict.
+        # image tensors should be scaled to -1 ... 1 and in channels-first
+        # format (e.g., bchw instead if bhwc)
+        return batch[self.input_key]
+
+    def get_autoencoder_params(self) -> list:
+        params = []
+        if hasattr(self.loss, "get_trainable_autoencoder_parameters"):
+            params += list(self.loss.get_trainable_autoencoder_parameters())
+        if hasattr(self.regularization, "get_trainable_parameters"):
+            params += list(self.regularization.get_trainable_parameters())
+        params = params + list(self.encoder.parameters())
+        params = params + list(self.decoder.parameters())
+        return params
+
+    def get_discriminator_params(self) -> list:
+        if hasattr(self.loss, "get_trainable_parameters"):
+            params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
+        else:
+            params = []
+        return params
+
+    def get_last_layer(self):
+        return self.decoder.get_last_layer()
+
+    def encode(
+        self,
+        x: torch.Tensor,
+        return_reg_log: bool = False,
+        unregularized: bool = False,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+        z = self.encoder(x)
+        if unregularized:
+            return z, dict()
+        z, reg_log = self.regularization(z)
+        if return_reg_log:
+            return z, reg_log
+        return z
+
+    def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
+        x = self.decoder(z, **kwargs)
+        return x
+
+    def forward(
+        self, x: torch.Tensor, **additional_decode_kwargs
+    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
+        z, reg_log = self.encode(x, return_reg_log=True)
+        dec = self.decode(z, **additional_decode_kwargs)
+        return z, dec, reg_log
+
+    def inner_training_step(
+        self, batch: dict, batch_idx: int, optimizer_idx: int = 0
+    ) -> torch.Tensor:
+        x = self.get_input(batch)
+        additional_decode_kwargs = {
+            key: batch[key] for key in self.additional_decode_keys.intersection(batch)
+        }
+        z, xrec, regularization_log = self(x, **additional_decode_kwargs)
+        if hasattr(self.loss, "forward_keys"):
+            extra_info = {
+                "z": z,
+                "optimizer_idx": optimizer_idx,
+                "global_step": self.global_step,
+                "last_layer": self.get_last_layer(),
+                "split": "train",
+                "regularization_log": regularization_log,
+                "autoencoder": self,
+            }
+            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
+        else:
+            extra_info = dict()
+
+        if optimizer_idx == 0:
+            # autoencode
+            out_loss = self.loss(x, xrec, **extra_info)
+            if isinstance(out_loss, tuple):
+                aeloss, log_dict_ae = out_loss
+            else:
+                # simple loss function
+                aeloss = out_loss
+                log_dict_ae = {"train/loss/rec": aeloss.detach()}
+
+            self.log_dict(
+                log_dict_ae,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+                sync_dist=False,
+            )
+            self.log(
+                "loss",
+                aeloss.mean().detach(),
+                prog_bar=True,
+                logger=False,
+                on_epoch=False,
+                on_step=True,
+            )
+            return aeloss
+        elif optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
+            # -> discriminator always needs to return a tuple
+            self.log_dict(
+                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
+            )
+            return discloss
+        else:
+            raise NotImplementedError(f"Unknown optimizer {optimizer_idx}")
+
+    def training_step(self, batch: dict, batch_idx: int):
+        opts = self.optimizers()
+        if not isinstance(opts, list):
+            # Non-adversarial case
+            opts = [opts]
+        optimizer_idx = batch_idx % len(opts)
+        if self.global_step < self.disc_start_iter:
+            optimizer_idx = 0
+        opt = opts[optimizer_idx]
+        opt.zero_grad()
+        with opt.toggle_model():
+            loss = self.inner_training_step(
+                batch, batch_idx, optimizer_idx=optimizer_idx
+            )
+            self.manual_backward(loss)
+        opt.step()
+
+    def validation_step(self, batch: dict, batch_idx: int) -> Dict:
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+            log_dict.update(log_dict_ema)
+        return log_dict
+
+    def _validation_step(self, batch: dict, batch_idx: int, postfix: str = "") -> Dict:
+        x = self.get_input(batch)
+
+        z, xrec, regularization_log = self(x)
+        if hasattr(self.loss, "forward_keys"):
+            extra_info = {
+                "z": z,
+                "optimizer_idx": 0,
+                "global_step": self.global_step,
+                "last_layer": self.get_last_layer(),
+                "split": "val" + postfix,
+                "regularization_log": regularization_log,
+                "autoencoder": self,
+            }
+            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
+        else:
+            extra_info = dict()
+        out_loss = self.loss(x, xrec, **extra_info)
+        if isinstance(out_loss, tuple):
+            aeloss, log_dict_ae = out_loss
+        else:
+            # simple loss function
+            aeloss = out_loss
+            log_dict_ae = {f"val{postfix}/loss/rec": aeloss.detach()}
+        full_log_dict = log_dict_ae
+
+        if "optimizer_idx" in extra_info:
+            extra_info["optimizer_idx"] = 1
+            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
+            full_log_dict.update(log_dict_disc)
+        self.log(
+            f"val{postfix}/loss/rec",
+            log_dict_ae[f"val{postfix}/loss/rec"],
+            sync_dist=True,
+        )
+        self.log_dict(full_log_dict, sync_dist=True)
+        return full_log_dict
+
+    def get_param_groups(
+        self, parameter_names: List[List[str]], optimizer_args: List[dict]
+    ) -> Tuple[List[Dict[str, Any]], int]:
+        groups = []
+        num_params = 0
+        for names, args in zip(parameter_names, optimizer_args):
+            params = []
+            for pattern_ in names:
+                pattern_params = []
+                pattern = re.compile(pattern_)
+                for p_name, param in self.named_parameters():
+                    if re.match(pattern, p_name):
+                        pattern_params.append(param)
+                        num_params += param.numel()
+                if len(pattern_params) == 0:
+                    logpy.warn(f"Did not find parameters for pattern {pattern_}")
+                params.extend(pattern_params)
+            groups.append({"params": params, **args})
+        return groups, num_params
+
+    def configure_optimizers(self) -> List[torch.optim.Optimizer]:
+        if self.trainable_ae_params is None:
+            ae_params = self.get_autoencoder_params()
+        else:
+            ae_params, num_ae_params = self.get_param_groups(
+                self.trainable_ae_params, self.ae_optimizer_args
+            )
+            logpy.info(f"Number of trainable autoencoder parameters: {num_ae_params:,}")
+        if self.trainable_disc_params is None:
+            disc_params = self.get_discriminator_params()
+        else:
+            disc_params, num_disc_params = self.get_param_groups(
+                self.trainable_disc_params, self.disc_optimizer_args
+            )
+            logpy.info(
+                f"Number of trainable discriminator parameters: {num_disc_params:,}"
+            )
+        opt_ae = self.instantiate_optimizer_from_config(
+            ae_params,
+            default(self.lr_g_factor, 1.0) * self.learning_rate,
+            self.optimizer_config,
+        )
+        opts = [opt_ae]
+        if len(disc_params) > 0:
+            opt_disc = self.instantiate_optimizer_from_config(
+                disc_params, self.learning_rate, self.optimizer_config
+            )
+            opts.append(opt_disc)
+
+        return opts
+
+    @torch.no_grad()
+    def log_images(
+        self, batch: dict, additional_log_kwargs: Optional[Dict] = None, **kwargs
+    ) -> dict:
+        log = dict()
+        additional_decode_kwargs = {}
+        x = self.get_input(batch)
+        additional_decode_kwargs.update(
+            {key: batch[key] for key in self.additional_decode_keys.intersection(batch)}
+        )
+
+        _, xrec, _ = self(x, **additional_decode_kwargs)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        diff = 0.5 * torch.abs(torch.clamp(xrec, -1.0, 1.0) - x)
+        diff.clamp_(0, 1.0)
+        log["diff"] = 2.0 * diff - 1.0
+        # diff_boost shows location of small errors, by boosting their
+        # brightness.
+        log["diff_boost"] = (
+            2.0 * torch.clamp(self.diff_boost_factor * diff, 0.0, 1.0) - 1
+        )
+        if hasattr(self.loss, "log_images"):
+            log.update(self.loss.log_images(x, xrec))
+        with self.ema_scope():
+            _, xrec_ema, _ = self(x, **additional_decode_kwargs)
+            log["reconstructions_ema"] = xrec_ema
+            diff_ema = 0.5 * torch.abs(torch.clamp(xrec_ema, -1.0, 1.0) - x)
+            diff_ema.clamp_(0, 1.0)
+            log["diff_ema"] = 2.0 * diff_ema - 1.0
+            log["diff_boost_ema"] = (
+                2.0 * torch.clamp(self.diff_boost_factor * diff_ema, 0.0, 1.0) - 1
+            )
+        if additional_log_kwargs:
+            additional_decode_kwargs.update(additional_log_kwargs)
+            _, xrec_add, _ = self(x, **additional_decode_kwargs)
+            log_str = "reconstructions-" + "-".join(
+                [f"{key}={additional_log_kwargs[key]}" for key in additional_log_kwargs]
+            )
+            log[log_str] = xrec_add
+        return log
+
+
+class AutoencodingEngineLegacy(AutoencodingEngine):
+    def __init__(self, embed_dim: int, **kwargs):
+        self.max_batch_size = kwargs.pop("max_batch_size", None)
+        ddconfig = kwargs.pop("ddconfig")
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ckpt_engine = kwargs.pop("ckpt_engine", None)
+        super().__init__(
+            encoder_config={
+                "target": "sgm.modules.diffusionmodules.model.Encoder",
+                "params": ddconfig,
+            },
+            decoder_config={
+                "target": "sgm.modules.diffusionmodules.model.Decoder",
+                "params": ddconfig,
+            },
+            **kwargs,
+        )
+        self.quant_conv = torch.nn.Conv2d(
+            (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
+            (1 + ddconfig["double_z"]) * embed_dim,
+            1,
+        )
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.embed_dim = embed_dim
+
+        self.apply_ckpt(default(ckpt_path, ckpt_engine))
+
+    def get_autoencoder_params(self) -> list:
+        params = super().get_autoencoder_params()
+        return params
+
+    def encode(
+        self, x: torch.Tensor, return_reg_log: bool = False
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+        if self.max_batch_size is None:
+            z = self.encoder(x)
+            z = self.quant_conv(z)
+        else:
+            N = x.shape[0]
+            bs = self.max_batch_size
+            n_batches = int(math.ceil(N / bs))
+            z = list()
+            for i_batch in range(n_batches):
+                z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
+                z_batch = self.quant_conv(z_batch)
+                z.append(z_batch)
+            z = torch.cat(z, 0)
+
+        z, reg_log = self.regularization(z)
+        if return_reg_log:
+            return z, reg_log
+        return z
+
+    def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
+        if self.max_batch_size is None:
+            dec = self.post_quant_conv(z)
+            dec = self.decoder(dec, **decoder_kwargs)
+        else:
+            N = z.shape[0]
+            bs = self.max_batch_size
+            n_batches = int(math.ceil(N / bs))
+            dec = list()
+            for i_batch in range(n_batches):
+                dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
+                dec_batch = self.decoder(dec_batch, **decoder_kwargs)
+                dec.append(dec_batch)
+            dec = torch.cat(dec, 0)
+
+        return dec
+
+
+class AutoencoderKL(AutoencodingEngineLegacy):
+    def __init__(self, **kwargs):
+        if "lossconfig" in kwargs:
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers"
+                    ".DiagonalGaussianRegularizer"
+                )
+            },
+            **kwargs,
+        )
+
+
+class AutoencoderLegacyVQ(AutoencodingEngineLegacy):
+    def __init__(
+        self,
+        embed_dim: int,
+        n_embed: int,
+        sane_index_shape: bool = False,
+        **kwargs,
+    ):
+        if "lossconfig" in kwargs:
+            logpy.warn(f"Parameter `lossconfig` is deprecated, use `loss_config`.")
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers.quantize" ".VectorQuantizer"
+                ),
+                "params": {
+                    "n_e": n_embed,
+                    "e_dim": embed_dim,
+                    "sane_index_shape": sane_index_shape,
+                },
+            },
+            **kwargs,
+        )
+
+
+class IdentityFirstStage(AbstractAutoencoder):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def get_input(self, x: Any) -> Any:
+        return x
+
+    def encode(self, x: Any, *args, **kwargs) -> Any:
+        return x
+
+    def decode(self, x: Any, *args, **kwargs) -> Any:
+        return x
+
+
+class AEIntegerWrapper(nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        shape: Union[None, Tuple[int, int], List[int]] = (16, 16),
+        regularization_key: str = "regularization",
+        encoder_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self.model = model
+        assert hasattr(model, "encode") and hasattr(
+            model, "decode"
+        ), "Need AE interface"
+        self.regularization = get_nested_attribute(model, regularization_key)
+        self.shape = shape
+        self.encoder_kwargs = default(encoder_kwargs, {"return_reg_log": True})
+
+    def encode(self, x) -> torch.Tensor:
+        assert (
+            not self.training
+        ), f"{self.__class__.__name__} only supports inference currently"
+        _, log = self.model.encode(x, **self.encoder_kwargs)
+        assert isinstance(log, dict)
+        inds = log["min_encoding_indices"]
+        return rearrange(inds, "b ... -> b (...)")
+
+    def decode(
+        self, inds: torch.Tensor, shape: Union[None, tuple, list] = None
+    ) -> torch.Tensor:
+        # expect inds shape (b, s) with s = h*w
+        shape = default(shape, self.shape)  # Optional[(h, w)]
+        if shape is not None:
+            assert len(shape) == 2, f"Unhandeled shape {shape}"
+            inds = rearrange(inds, "b (h w) -> b h w", h=shape[0], w=shape[1])
+        h = self.regularization.get_codebook_entry(inds)  # (b, h, w, c)
+        h = rearrange(h, "b h w c -> b c h w")
+        return self.model.decode(h)
+
+
+class AutoencoderKLModeOnly(AutoencodingEngineLegacy):
+    def __init__(self, **kwargs):
+        if "lossconfig" in kwargs:
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers"
+                    ".DiagonalGaussianRegularizer"
+                ),
+                "params": {"sample": False},
+            },
+            **kwargs,
+        )
diff --git a/sgm/models/diffusion.py b/sgm/models/diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f3efd3c7e1e37bf4f78673ae72021f1f968e116
--- /dev/null
+++ b/sgm/models/diffusion.py
@@ -0,0 +1,341 @@
+import math
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import pytorch_lightning as pl
+import torch
+from omegaconf import ListConfig, OmegaConf
+from safetensors.torch import load_file as load_safetensors
+from torch.optim.lr_scheduler import LambdaLR
+
+from ..modules import UNCONDITIONAL_CONFIG
+from ..modules.autoencoding.temporal_ae import VideoDecoder
+from ..modules.diffusionmodules.wrappers import OPENAIUNETWRAPPER
+from ..modules.ema import LitEma
+from ..util import (default, disabled_train, get_obj_from_str,
+                    instantiate_from_config, log_txt_as_img)
+
+
+class DiffusionEngine(pl.LightningModule):
+    def __init__(
+        self,
+        network_config,
+        denoiser_config,
+        first_stage_config,
+        conditioner_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        sampler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        optimizer_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        scheduler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        loss_fn_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        network_wrapper: Union[None, str] = None,
+        ckpt_path: Union[None, str] = None,
+        use_ema: bool = False,
+        ema_decay_rate: float = 0.9999,
+        scale_factor: float = 1.0,
+        disable_first_stage_autocast=False,
+        input_key: str = "jpg",
+        log_keys: Union[List, None] = None,
+        no_cond_log: bool = False,
+        compile_model: bool = False,
+        en_and_decode_n_samples_a_time: Optional[int] = None,
+    ):
+        super().__init__()
+        self.log_keys = log_keys
+        self.input_key = input_key
+        self.optimizer_config = default(
+            optimizer_config, {"target": "torch.optim.AdamW"}
+        )
+        model = instantiate_from_config(network_config)
+        self.model = get_obj_from_str(default(network_wrapper, OPENAIUNETWRAPPER))(
+            model, compile_model=compile_model
+        )
+
+        self.denoiser = instantiate_from_config(denoiser_config)
+        self.sampler = (
+            instantiate_from_config(sampler_config)
+            if sampler_config is not None
+            else None
+        )
+        self.conditioner = instantiate_from_config(
+            default(conditioner_config, UNCONDITIONAL_CONFIG)
+        )
+        self.scheduler_config = scheduler_config
+        self._init_first_stage(first_stage_config)
+
+        self.loss_fn = (
+            instantiate_from_config(loss_fn_config)
+            if loss_fn_config is not None
+            else None
+        )
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model, decay=ema_decay_rate)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.scale_factor = scale_factor
+        self.disable_first_stage_autocast = disable_first_stage_autocast
+        self.no_cond_log = no_cond_log
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path)
+
+        self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time
+
+    def init_from_ckpt(
+        self,
+        path: str,
+    ) -> None:
+        if path.endswith("ckpt"):
+            sd = torch.load(path, map_location="cpu")["state_dict"]
+        elif path.endswith("safetensors"):
+            sd = load_safetensors(path)
+        else:
+            raise NotImplementedError
+
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(
+            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
+        )
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def _init_first_stage(self, config):
+        model = instantiate_from_config(config).eval()
+        model.train = disabled_train
+        for param in model.parameters():
+            param.requires_grad = False
+        self.first_stage_model = model
+
+    def get_input(self, batch):
+        # assuming unified data format, dataloader returns a dict.
+        # image tensors should be scaled to -1 ... 1 and in bchw format
+        return batch[self.input_key]
+
+    @torch.no_grad()
+    def decode_first_stage(self, z):
+        z = 1.0 / self.scale_factor * z
+        n_samples = default(self.en_and_decode_n_samples_a_time, z.shape[0])
+
+        n_rounds = math.ceil(z.shape[0] / n_samples)
+        all_out = []
+        with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
+            for n in range(n_rounds):
+                if isinstance(self.first_stage_model.decoder, VideoDecoder):
+                    kwargs = {"timesteps": len(z[n * n_samples : (n + 1) * n_samples])}
+                else:
+                    kwargs = {}
+                out = self.first_stage_model.decode(
+                    z[n * n_samples : (n + 1) * n_samples], **kwargs
+                )
+                all_out.append(out)
+        out = torch.cat(all_out, dim=0)
+        return out
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        n_samples = default(self.en_and_decode_n_samples_a_time, x.shape[0])
+        n_rounds = math.ceil(x.shape[0] / n_samples)
+        all_out = []
+        with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
+            for n in range(n_rounds):
+                out = self.first_stage_model.encode(
+                    x[n * n_samples : (n + 1) * n_samples]
+                )
+                all_out.append(out)
+        z = torch.cat(all_out, dim=0)
+        z = self.scale_factor * z
+        return z
+
+    def forward(self, x, batch):
+        loss = self.loss_fn(self.model, self.denoiser, self.conditioner, x, batch)
+        loss_mean = loss.mean()
+        loss_dict = {"loss": loss_mean}
+        return loss_mean, loss_dict
+
+    def shared_step(self, batch: Dict) -> Any:
+        x = self.get_input(batch)
+        x = self.encode_first_stage(x)
+        batch["global_step"] = self.global_step
+        loss, loss_dict = self(x, batch)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(
+            loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=False
+        )
+
+        self.log(
+            "global_step",
+            self.global_step,
+            prog_bar=True,
+            logger=True,
+            on_step=True,
+            on_epoch=False,
+        )
+
+        if self.scheduler_config is not None:
+            lr = self.optimizers().param_groups[0]["lr"]
+            self.log(
+                "lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
+            )
+
+        return loss
+
+    def on_train_start(self, *args, **kwargs):
+        if self.sampler is None or self.loss_fn is None:
+            raise ValueError("Sampler and loss function need to be set for training.")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def instantiate_optimizer_from_config(self, params, lr, cfg):
+        return get_obj_from_str(cfg["target"])(
+            params, lr=lr, **cfg.get("params", dict())
+        )
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        for embedder in self.conditioner.embedders:
+            if embedder.is_trainable:
+                params = params + list(embedder.parameters())
+        opt = self.instantiate_optimizer_from_config(params, lr, self.optimizer_config)
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    "scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    "interval": "step",
+                    "frequency": 1,
+                }
+            ]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: Dict,
+        uc: Union[Dict, None] = None,
+        batch_size: int = 16,
+        shape: Union[None, Tuple, List] = None,
+        **kwargs,
+    ):
+        randn = torch.randn(batch_size, *shape).to(self.device)
+
+        denoiser = lambda input, sigma, c: self.denoiser(
+            self.model, input, sigma, c, **kwargs
+        )
+        samples = self.sampler(denoiser, randn, cond, uc=uc)
+        return samples
+
+    @torch.no_grad()
+    def log_conditionings(self, batch: Dict, n: int) -> Dict:
+        """
+        Defines heuristics to log different conditionings.
+        These can be lists of strings (text-to-image), tensors, ints, ...
+        """
+        image_h, image_w = batch[self.input_key].shape[2:]
+        log = dict()
+
+        for embedder in self.conditioner.embedders:
+            if (
+                (self.log_keys is None) or (embedder.input_key in self.log_keys)
+            ) and not self.no_cond_log:
+                x = batch[embedder.input_key][:n]
+                if isinstance(x, torch.Tensor):
+                    if x.dim() == 1:
+                        # class-conditional, convert integer to string
+                        x = [str(x[i].item()) for i in range(x.shape[0])]
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 4)
+                    elif x.dim() == 2:
+                        # size and crop cond and the like
+                        x = [
+                            "x".join([str(xx) for xx in x[i].tolist()])
+                            for i in range(x.shape[0])
+                        ]
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
+                    else:
+                        raise NotImplementedError()
+                elif isinstance(x, (List, ListConfig)):
+                    if isinstance(x[0], str):
+                        # strings
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
+                    else:
+                        raise NotImplementedError()
+                else:
+                    raise NotImplementedError()
+                log[embedder.input_key] = xc
+        return log
+
+    @torch.no_grad()
+    def log_images(
+        self,
+        batch: Dict,
+        N: int = 8,
+        sample: bool = True,
+        ucg_keys: List[str] = None,
+        **kwargs,
+    ) -> Dict:
+        conditioner_input_keys = [e.input_key for e in self.conditioner.embedders]
+        if ucg_keys:
+            assert all(map(lambda x: x in conditioner_input_keys, ucg_keys)), (
+                "Each defined ucg key for sampling must be in the provided conditioner input keys,"
+                f"but we have {ucg_keys} vs. {conditioner_input_keys}"
+            )
+        else:
+            ucg_keys = conditioner_input_keys
+        log = dict()
+
+        x = self.get_input(batch)
+
+        c, uc = self.conditioner.get_unconditional_conditioning(
+            batch,
+            force_uc_zero_embeddings=ucg_keys
+            if len(self.conditioner.embedders) > 0
+            else [],
+        )
+
+        sampling_kwargs = {}
+
+        N = min(x.shape[0], N)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+        z = self.encode_first_stage(x)
+        log["reconstructions"] = self.decode_first_stage(z)
+        log.update(self.log_conditionings(batch, N))
+
+        for k in c:
+            if isinstance(c[k], torch.Tensor):
+                c[k], uc[k] = map(lambda y: y[k][:N].to(self.device), (c, uc))
+
+        if sample:
+            with self.ema_scope("Plotting"):
+                samples = self.sample(
+                    c, shape=z.shape[1:], uc=uc, batch_size=N, **sampling_kwargs
+                )
+            samples = self.decode_first_stage(samples)
+            log["samples"] = samples
+        return log
diff --git a/sgm/modules/__init__.py b/sgm/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0db1d7716a6e48f77b86a4b59c9289d6fb76b50b
--- /dev/null
+++ b/sgm/modules/__init__.py
@@ -0,0 +1,6 @@
+from .encoders.modules import GeneralConditioner
+
+UNCONDITIONAL_CONFIG = {
+    "target": "sgm.modules.GeneralConditioner",
+    "params": {"emb_models": []},
+}
diff --git a/sgm/modules/__pycache__/__init__.cpython-39.pyc b/sgm/modules/__pycache__/__init__.cpython-39.pyc
new file mode 100644
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diff --git a/sgm/modules/__pycache__/attention.cpython-39.pyc b/sgm/modules/__pycache__/attention.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..21763def6537a0166a2bd1ef1beda043d751932c
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diff --git a/sgm/modules/__pycache__/ema.cpython-39.pyc b/sgm/modules/__pycache__/ema.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0f97aac98fc610c36d888cf8e5b64177d42cf5e2
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diff --git a/sgm/modules/__pycache__/video_attention.cpython-39.pyc b/sgm/modules/__pycache__/video_attention.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7e7f1e82ec4bfdb45f99fd2e978b52856a3bd0ce
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diff --git a/sgm/modules/attention.py b/sgm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..52a50b7bd744bea0f0cdca23b0cfd14ad87794be
--- /dev/null
+++ b/sgm/modules/attention.py
@@ -0,0 +1,759 @@
+import logging
+import math
+from inspect import isfunction
+from typing import Any, Optional
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from packaging import version
+from torch import nn
+from torch.utils.checkpoint import checkpoint
+
+logpy = logging.getLogger(__name__)
+
+if version.parse(torch.__version__) >= version.parse("2.0.0"):
+    SDP_IS_AVAILABLE = True
+    from torch.backends.cuda import SDPBackend, sdp_kernel
+
+    BACKEND_MAP = {
+        SDPBackend.MATH: {
+            "enable_math": True,
+            "enable_flash": False,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.FLASH_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": True,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.EFFICIENT_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": False,
+            "enable_mem_efficient": True,
+        },
+        None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True},
+    }
+else:
+    from contextlib import nullcontext
+
+    SDP_IS_AVAILABLE = False
+    sdp_kernel = nullcontext
+    BACKEND_MAP = {}
+    logpy.warn(
+        f"No SDP backend available, likely because you are running in pytorch "
+        f"versions < 2.0. In fact, you are using PyTorch {torch.__version__}. "
+        f"You might want to consider upgrading."
+    )
+
+try:
+    import xformers
+    import xformers.ops
+
+    XFORMERS_IS_AVAILABLE = True
+except:
+    XFORMERS_IS_AVAILABLE = False
+    logpy.warn("no module 'xformers'. Processing without...")
+
+# from .diffusionmodules.util import mixed_checkpoint as checkpoint
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return {el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
+
+        self.net = nn.Sequential(
+            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(
+        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(
+            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
+        )
+        k = k.softmax(dim=-1)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        out = rearrange(
+            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
+        )
+        return self.to_out(out)
+
+
+class SelfAttention(nn.Module):
+    ATTENTION_MODES = ("xformers", "torch", "math")
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_scale: Optional[float] = None,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        attn_mode: str = "xformers",
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        assert attn_mode in self.ATTENTION_MODES
+        self.attn_mode = attn_mode
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, L, C = x.shape
+
+        qkv = self.qkv(x)
+        if self.attn_mode == "torch":
+            qkv = rearrange(
+                qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads
+            ).float()
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+            x = rearrange(x, "B H L D -> B L (H D)")
+        elif self.attn_mode == "xformers":
+            qkv = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B L H D
+            x = xformers.ops.memory_efficient_attention(q, k, v)
+            x = rearrange(x, "B L H D -> B L (H D)", H=self.num_heads)
+        elif self.attn_mode == "math":
+            qkv = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = (attn @ v).transpose(1, 2).reshape(B, L, C)
+        else:
+            raise NotImplemented
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b (h w) c")
+        k = rearrange(k, "b c h w -> b c (h w)")
+        w_ = torch.einsum("bij,bjk->bik", q, k)
+
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, "b c h w -> b c (h w)")
+        w_ = rearrange(w_, "b i j -> b j i")
+        h_ = torch.einsum("bij,bjk->bik", v, w_)
+        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        query_dim,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        backend=None,
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head**-0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.backend = backend
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        h = self.heads
+
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            n_cp = x.shape[0] // n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+
+        ## old
+        """
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+        del q, k
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        sim = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim, v)
+        """
+        ## new
+        with sdp_kernel(**BACKEND_MAP[self.backend]):
+            # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
+            out = F.scaled_dot_product_attention(
+                q, k, v, attn_mask=mask
+            )  # scale is dim_head ** -0.5 per default
+
+        del q, k, v
+        out = rearrange(out, "b h n d -> b n (h d)", h=h)
+
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    def __init__(
+        self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs
+    ):
+        super().__init__()
+        logpy.debug(
+            f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, "
+            f"context_dim is {context_dim} and using {heads} heads with a "
+            f"dimension of {dim_head}."
+        )
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.attention_op: Optional[Any] = None
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            # n_cp = x.shape[0]//n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self],
+                "b ... -> (b n) ...",
+                n=n_times_crossframe_attn_in_self,
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self],
+                "b ... -> (b n) ...",
+                n=n_times_crossframe_attn_in_self,
+            )
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        # actually compute the attention, what we cannot get enough of
+        if version.parse(xformers.__version__) >= version.parse("0.0.21"):
+            # NOTE: workaround for
+            # https://github.com/facebookresearch/xformers/issues/845
+            max_bs = 32768
+            N = q.shape[0]
+            n_batches = math.ceil(N / max_bs)
+            out = list()
+            for i_batch in range(n_batches):
+                batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs)
+                out.append(
+                    xformers.ops.memory_efficient_attention(
+                        q[batch],
+                        k[batch],
+                        v[batch],
+                        attn_bias=None,
+                        op=self.attention_op,
+                    )
+                )
+            out = torch.cat(out, 0)
+        else:
+            out = xformers.ops.memory_efficient_attention(
+                q, k, v, attn_bias=None, op=self.attention_op
+            )
+
+        # TODO: Use this directly in the attention operation, as a bias
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention,  # ampere
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        disable_self_attn=False,
+        attn_mode="softmax",
+        sdp_backend=None,
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        if attn_mode != "softmax" and not XFORMERS_IS_AVAILABLE:
+            logpy.warn(
+                f"Attention mode '{attn_mode}' is not available. Falling "
+                f"back to native attention. This is not a problem in "
+                f"Pytorch >= 2.0. FYI, you are running with PyTorch "
+                f"version {torch.__version__}."
+            )
+            attn_mode = "softmax"
+        elif attn_mode == "softmax" and not SDP_IS_AVAILABLE:
+            logpy.warn(
+                "We do not support vanilla attention anymore, as it is too "
+                "expensive. Sorry."
+            )
+            if not XFORMERS_IS_AVAILABLE:
+                assert (
+                    False
+                ), "Please install xformers via e.g. 'pip install xformers==0.0.16'"
+            else:
+                logpy.info("Falling back to xformers efficient attention.")
+                attn_mode = "softmax-xformers"
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        if version.parse(torch.__version__) >= version.parse("2.0.0"):
+            assert sdp_backend is None or isinstance(sdp_backend, SDPBackend)
+        else:
+            assert sdp_backend is None
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = attn_cls(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            context_dim=context_dim if self.disable_self_attn else None,
+            backend=sdp_backend,
+        )  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = attn_cls(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            backend=sdp_backend,
+        )  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+        if self.checkpoint:
+            logpy.debug(f"{self.__class__.__name__} is using checkpointing")
+
+    def forward(
+        self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
+    ):
+        kwargs = {"x": x}
+
+        if context is not None:
+            kwargs.update({"context": context})
+
+        if additional_tokens is not None:
+            kwargs.update({"additional_tokens": additional_tokens})
+
+        if n_times_crossframe_attn_in_self:
+            kwargs.update(
+                {"n_times_crossframe_attn_in_self": n_times_crossframe_attn_in_self}
+            )
+
+        # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint)
+        if self.checkpoint:
+            # inputs = {"x": x, "context": context}
+            return checkpoint(self._forward, x, context)
+            # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint)
+        else:
+            return self._forward(**kwargs)
+
+    def _forward(
+        self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
+    ):
+        x = (
+            self.attn1(
+                self.norm1(x),
+                context=context if self.disable_self_attn else None,
+                additional_tokens=additional_tokens,
+                n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
+                if not self.disable_self_attn
+                else 0,
+            )
+            + x
+        )
+        x = (
+            self.attn2(
+                self.norm2(x), context=context, additional_tokens=additional_tokens
+            )
+            + x
+        )
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class BasicTransformerSingleLayerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention  # on the A100s not quite as fast as the above version
+        # (todo might depend on head_dim, check, falls back to semi-optimized kernels for dim!=[16,32,64,128])
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        attn_mode="softmax",
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        self.attn1 = attn_cls(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            context_dim=context_dim,
+        )
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        # inputs = {"x": x, "context": context}
+        # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint)
+        return checkpoint(self._forward, x, context)
+
+    def _forward(self, x, context=None):
+        x = self.attn1(self.norm1(x), context=context) + x
+        x = self.ff(self.norm2(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        n_heads,
+        d_head,
+        depth=1,
+        dropout=0.0,
+        context_dim=None,
+        disable_self_attn=False,
+        use_linear=False,
+        attn_type="softmax",
+        use_checkpoint=True,
+        # sdp_backend=SDPBackend.FLASH_ATTENTION
+        sdp_backend=None,
+    ):
+        super().__init__()
+        logpy.debug(
+            f"constructing {self.__class__.__name__} of depth {depth} w/ "
+            f"{in_channels} channels and {n_heads} heads."
+        )
+
+        if exists(context_dim) and not isinstance(context_dim, list):
+            context_dim = [context_dim]
+        if exists(context_dim) and isinstance(context_dim, list):
+            if depth != len(context_dim):
+                logpy.warn(
+                    f"{self.__class__.__name__}: Found context dims "
+                    f"{context_dim} of depth {len(context_dim)}, which does not "
+                    f"match the specified 'depth' of {depth}. Setting context_dim "
+                    f"to {depth * [context_dim[0]]} now."
+                )
+                # depth does not match context dims.
+                assert all(
+                    map(lambda x: x == context_dim[0], context_dim)
+                ), "need homogenous context_dim to match depth automatically"
+                context_dim = depth * [context_dim[0]]
+        elif context_dim is None:
+            context_dim = [None] * depth
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+        if not use_linear:
+            self.proj_in = nn.Conv2d(
+                in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+            )
+        else:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    n_heads,
+                    d_head,
+                    dropout=dropout,
+                    context_dim=context_dim[d],
+                    disable_self_attn=disable_self_attn,
+                    attn_mode=attn_type,
+                    checkpoint=use_checkpoint,
+                    sdp_backend=sdp_backend,
+                )
+                for d in range(depth)
+            ]
+        )
+        if not use_linear:
+            self.proj_out = zero_module(
+                nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+            )
+        else:
+            # self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+            self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context]
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, "b c h w -> b (h w) c").contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            if i > 0 and len(context) == 1:
+                i = 0  # use same context for each block
+            x = block(x, context=context[i])
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        return x + x_in
+
+
+class SimpleTransformer(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        depth: int,
+        heads: int,
+        dim_head: int,
+        context_dim: Optional[int] = None,
+        dropout: float = 0.0,
+        checkpoint: bool = True,
+    ):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                BasicTransformerBlock(
+                    dim,
+                    heads,
+                    dim_head,
+                    dropout=dropout,
+                    context_dim=context_dim,
+                    attn_mode="softmax-xformers",
+                    checkpoint=checkpoint,
+                )
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        for layer in self.layers:
+            x = layer(x, context)
+        return x
diff --git a/sgm/modules/autoencoding/__init__.py b/sgm/modules/autoencoding/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/modules/autoencoding/__pycache__/__init__.cpython-39.pyc b/sgm/modules/autoencoding/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..eaf2bfca7b7785b3090fb24b276291a9754e14be
Binary files /dev/null and b/sgm/modules/autoencoding/__pycache__/__init__.cpython-39.pyc differ
diff --git a/sgm/modules/autoencoding/__pycache__/temporal_ae.cpython-39.pyc b/sgm/modules/autoencoding/__pycache__/temporal_ae.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2428596ecf918ece4be6c0f5259a18367f784369
Binary files /dev/null and b/sgm/modules/autoencoding/__pycache__/temporal_ae.cpython-39.pyc differ
diff --git a/sgm/modules/autoencoding/losses/__init__.py b/sgm/modules/autoencoding/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b316c7aa6ea1c5e31a58987aa3b37b2933eb7e2
--- /dev/null
+++ b/sgm/modules/autoencoding/losses/__init__.py
@@ -0,0 +1,7 @@
+__all__ = [
+    "GeneralLPIPSWithDiscriminator",
+    "LatentLPIPS",
+]
+
+from .discriminator_loss import GeneralLPIPSWithDiscriminator
+from .lpips import LatentLPIPS
diff --git a/sgm/modules/autoencoding/losses/discriminator_loss.py b/sgm/modules/autoencoding/losses/discriminator_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..09b6829267bf8e4d98c3f29abdc19e58dcbcbe64
--- /dev/null
+++ b/sgm/modules/autoencoding/losses/discriminator_loss.py
@@ -0,0 +1,306 @@
+from typing import Dict, Iterator, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+from einops import rearrange
+from matplotlib import colormaps
+from matplotlib import pyplot as plt
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+from ..lpips.model.model import weights_init
+from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
+
+
+class GeneralLPIPSWithDiscriminator(nn.Module):
+    def __init__(
+        self,
+        disc_start: int,
+        logvar_init: float = 0.0,
+        disc_num_layers: int = 3,
+        disc_in_channels: int = 3,
+        disc_factor: float = 1.0,
+        disc_weight: float = 1.0,
+        perceptual_weight: float = 1.0,
+        disc_loss: str = "hinge",
+        scale_input_to_tgt_size: bool = False,
+        dims: int = 2,
+        learn_logvar: bool = False,
+        regularization_weights: Union[None, Dict[str, float]] = None,
+        additional_log_keys: Optional[List[str]] = None,
+        discriminator_config: Optional[Dict] = None,
+    ):
+        super().__init__()
+        self.dims = dims
+        if self.dims > 2:
+            print(
+                f"running with dims={dims}. This means that for perceptual loss "
+                f"calculation, the LPIPS loss will be applied to each frame "
+                f"independently."
+            )
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        assert disc_loss in ["hinge", "vanilla"]
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        # output log variance
+        self.logvar = nn.Parameter(
+            torch.full((), logvar_init), requires_grad=learn_logvar
+        )
+        self.learn_logvar = learn_logvar
+
+        discriminator_config = default(
+            discriminator_config,
+            {
+                "target": "sgm.modules.autoencoding.lpips.model.model.NLayerDiscriminator",
+                "params": {
+                    "input_nc": disc_in_channels,
+                    "n_layers": disc_num_layers,
+                    "use_actnorm": False,
+                },
+            },
+        )
+
+        self.discriminator = instantiate_from_config(discriminator_config).apply(
+            weights_init
+        )
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.regularization_weights = default(regularization_weights, {})
+
+        self.forward_keys = [
+            "optimizer_idx",
+            "global_step",
+            "last_layer",
+            "split",
+            "regularization_log",
+        ]
+
+        self.additional_log_keys = set(default(additional_log_keys, []))
+        self.additional_log_keys.update(set(self.regularization_weights.keys()))
+
+    def get_trainable_parameters(self) -> Iterator[nn.Parameter]:
+        return self.discriminator.parameters()
+
+    def get_trainable_autoencoder_parameters(self) -> Iterator[nn.Parameter]:
+        if self.learn_logvar:
+            yield self.logvar
+        yield from ()
+
+    @torch.no_grad()
+    def log_images(
+        self, inputs: torch.Tensor, reconstructions: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        # calc logits of real/fake
+        logits_real = self.discriminator(inputs.contiguous().detach())
+        if len(logits_real.shape) < 4:
+            # Non patch-discriminator
+            return dict()
+        logits_fake = self.discriminator(reconstructions.contiguous().detach())
+        # -> (b, 1, h, w)
+
+        # parameters for colormapping
+        high = max(logits_fake.abs().max(), logits_real.abs().max()).item()
+        cmap = colormaps["PiYG"]  # diverging colormap
+
+        def to_colormap(logits: torch.Tensor) -> torch.Tensor:
+            """(b, 1, ...) -> (b, 3, ...)"""
+            logits = (logits + high) / (2 * high)
+            logits_np = cmap(logits.cpu().numpy())[..., :3]  # truncate alpha channel
+            # -> (b, 1, ..., 3)
+            logits = torch.from_numpy(logits_np).to(logits.device)
+            return rearrange(logits, "b 1 ... c -> b c ...")
+
+        logits_real = torch.nn.functional.interpolate(
+            logits_real,
+            size=inputs.shape[-2:],
+            mode="nearest",
+            antialias=False,
+        )
+        logits_fake = torch.nn.functional.interpolate(
+            logits_fake,
+            size=reconstructions.shape[-2:],
+            mode="nearest",
+            antialias=False,
+        )
+
+        # alpha value of logits for overlay
+        alpha_real = torch.abs(logits_real) / high
+        alpha_fake = torch.abs(logits_fake) / high
+        # -> (b, 1, h, w) in range [0, 0.5]
+        # alpha value of lines don't really matter, since the values are the same
+        # for both images and logits anyway
+        grid_alpha_real = torchvision.utils.make_grid(alpha_real, nrow=4)
+        grid_alpha_fake = torchvision.utils.make_grid(alpha_fake, nrow=4)
+        grid_alpha = 0.8 * torch.cat((grid_alpha_real, grid_alpha_fake), dim=1)
+        # -> (1, h, w)
+        # blend logits and images together
+
+        # prepare logits for plotting
+        logits_real = to_colormap(logits_real)
+        logits_fake = to_colormap(logits_fake)
+        # resize logits
+        # -> (b, 3, h, w)
+
+        # make some grids
+        # add all logits to one plot
+        logits_real = torchvision.utils.make_grid(logits_real, nrow=4)
+        logits_fake = torchvision.utils.make_grid(logits_fake, nrow=4)
+        # I just love how torchvision calls the number of columns `nrow`
+        grid_logits = torch.cat((logits_real, logits_fake), dim=1)
+        # -> (3, h, w)
+
+        grid_images_real = torchvision.utils.make_grid(0.5 * inputs + 0.5, nrow=4)
+        grid_images_fake = torchvision.utils.make_grid(
+            0.5 * reconstructions + 0.5, nrow=4
+        )
+        grid_images = torch.cat((grid_images_real, grid_images_fake), dim=1)
+        # -> (3, h, w) in range [0, 1]
+
+        grid_blend = grid_alpha * grid_logits + (1 - grid_alpha) * grid_images
+
+        # Create labeled colorbar
+        dpi = 100
+        height = 128 / dpi
+        width = grid_logits.shape[2] / dpi
+        fig, ax = plt.subplots(figsize=(width, height), dpi=dpi)
+        img = ax.imshow(np.array([[-high, high]]), cmap=cmap)
+        plt.colorbar(
+            img,
+            cax=ax,
+            orientation="horizontal",
+            fraction=0.9,
+            aspect=width / height,
+            pad=0.0,
+        )
+        img.set_visible(False)
+        fig.tight_layout()
+        fig.canvas.draw()
+        # manually convert figure to numpy
+        cbar_np = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+        cbar_np = cbar_np.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+        cbar = torch.from_numpy(cbar_np.copy()).to(grid_logits.dtype) / 255.0
+        cbar = rearrange(cbar, "h w c -> c h w").to(grid_logits.device)
+
+        # Add colorbar to plot
+        annotated_grid = torch.cat((grid_logits, cbar), dim=1)
+        blended_grid = torch.cat((grid_blend, cbar), dim=1)
+        return {
+            "vis_logits": 2 * annotated_grid[None, ...] - 1,
+            "vis_logits_blended": 2 * blended_grid[None, ...] - 1,
+        }
+
+    def calculate_adaptive_weight(
+        self, nll_loss: torch.Tensor, g_loss: torch.Tensor, last_layer: torch.Tensor
+    ) -> torch.Tensor:
+        nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+        g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        inputs: torch.Tensor,
+        reconstructions: torch.Tensor,
+        *,  # added because I changed the order here
+        regularization_log: Dict[str, torch.Tensor],
+        optimizer_idx: int,
+        global_step: int,
+        last_layer: torch.Tensor,
+        split: str = "train",
+        weights: Union[None, float, torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, dict]:
+        if self.scale_input_to_tgt_size:
+            inputs = torch.nn.functional.interpolate(
+                inputs, reconstructions.shape[2:], mode="bicubic", antialias=True
+            )
+
+        if self.dims > 2:
+            inputs, reconstructions = map(
+                lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
+                (inputs, reconstructions),
+            )
+
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(
+                inputs.contiguous(), reconstructions.contiguous()
+            )
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+
+        nll_loss, weighted_nll_loss = self.get_nll_loss(rec_loss, weights)
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if global_step >= self.discriminator_iter_start or not self.training:
+                logits_fake = self.discriminator(reconstructions.contiguous())
+                g_loss = -torch.mean(logits_fake)
+                if self.training:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+
+            loss = weighted_nll_loss + d_weight * self.disc_factor * g_loss
+            log = dict()
+            for k in regularization_log:
+                if k in self.regularization_weights:
+                    loss = loss + self.regularization_weights[k] * regularization_log[k]
+                if k in self.additional_log_keys:
+                    log[f"{split}/{k}"] = regularization_log[k].detach().float().mean()
+
+            log.update(
+                {
+                    f"{split}/loss/total": loss.clone().detach().mean(),
+                    f"{split}/loss/nll": nll_loss.detach().mean(),
+                    f"{split}/loss/rec": rec_loss.detach().mean(),
+                    f"{split}/loss/g": g_loss.detach().mean(),
+                    f"{split}/scalars/logvar": self.logvar.detach(),
+                    f"{split}/scalars/d_weight": d_weight.detach(),
+                }
+            )
+
+            return loss, log
+        elif optimizer_idx == 1:
+            # second pass for discriminator update
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+            if global_step >= self.discriminator_iter_start or not self.training:
+                d_loss = self.disc_factor * self.disc_loss(logits_real, logits_fake)
+            else:
+                d_loss = torch.tensor(0.0, requires_grad=True)
+
+            log = {
+                f"{split}/loss/disc": d_loss.clone().detach().mean(),
+                f"{split}/logits/real": logits_real.detach().mean(),
+                f"{split}/logits/fake": logits_fake.detach().mean(),
+            }
+            return d_loss, log
+        else:
+            raise NotImplementedError(f"Unknown optimizer_idx {optimizer_idx}")
+
+    def get_nll_loss(
+        self,
+        rec_loss: torch.Tensor,
+        weights: Optional[Union[float, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+        weighted_nll_loss = nll_loss
+        if weights is not None:
+            weighted_nll_loss = weights * nll_loss
+        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+
+        return nll_loss, weighted_nll_loss
diff --git a/sgm/modules/autoencoding/losses/lpips.py b/sgm/modules/autoencoding/losses/lpips.py
new file mode 100644
index 0000000000000000000000000000000000000000..b329fcc2ee9477f0122aa7d066866cdfe71ce521
--- /dev/null
+++ b/sgm/modules/autoencoding/losses/lpips.py
@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+
+
+class LatentLPIPS(nn.Module):
+    def __init__(
+        self,
+        decoder_config,
+        perceptual_weight=1.0,
+        latent_weight=1.0,
+        scale_input_to_tgt_size=False,
+        scale_tgt_to_input_size=False,
+        perceptual_weight_on_inputs=0.0,
+    ):
+        super().__init__()
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        self.scale_tgt_to_input_size = scale_tgt_to_input_size
+        self.init_decoder(decoder_config)
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.latent_weight = latent_weight
+        self.perceptual_weight_on_inputs = perceptual_weight_on_inputs
+
+    def init_decoder(self, config):
+        self.decoder = instantiate_from_config(config)
+        if hasattr(self.decoder, "encoder"):
+            del self.decoder.encoder
+
+    def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"):
+        log = dict()
+        loss = (latent_inputs - latent_predictions) ** 2
+        log[f"{split}/latent_l2_loss"] = loss.mean().detach()
+        image_reconstructions = None
+        if self.perceptual_weight > 0.0:
+            image_reconstructions = self.decoder.decode(latent_predictions)
+            image_targets = self.decoder.decode(latent_inputs)
+            perceptual_loss = self.perceptual_loss(
+                image_targets.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = (
+                self.latent_weight * loss.mean()
+                + self.perceptual_weight * perceptual_loss.mean()
+            )
+            log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach()
+
+        if self.perceptual_weight_on_inputs > 0.0:
+            image_reconstructions = default(
+                image_reconstructions, self.decoder.decode(latent_predictions)
+            )
+            if self.scale_input_to_tgt_size:
+                image_inputs = torch.nn.functional.interpolate(
+                    image_inputs,
+                    image_reconstructions.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+            elif self.scale_tgt_to_input_size:
+                image_reconstructions = torch.nn.functional.interpolate(
+                    image_reconstructions,
+                    image_inputs.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+
+            perceptual_loss2 = self.perceptual_loss(
+                image_inputs.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean()
+            log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach()
+        return loss, log
diff --git a/sgm/modules/autoencoding/lpips/__init__.py b/sgm/modules/autoencoding/lpips/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/modules/autoencoding/lpips/__pycache__/__init__.cpython-39.pyc b/sgm/modules/autoencoding/lpips/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e6c5ccdfa4d4f8027fd00bb88d060b9328a3447b
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diff --git a/sgm/modules/autoencoding/lpips/__pycache__/util.cpython-39.pyc b/sgm/modules/autoencoding/lpips/__pycache__/util.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fb078428aa63f32ff975a1178f3d88ff37989b84
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diff --git a/sgm/modules/autoencoding/lpips/loss/.gitignore b/sgm/modules/autoencoding/lpips/loss/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a92958a1cd4ffe005e1f5448ab3e6fd9c795a43a
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/loss/.gitignore
@@ -0,0 +1 @@
+vgg.pth
\ No newline at end of file
diff --git a/sgm/modules/autoencoding/lpips/loss/LICENSE b/sgm/modules/autoencoding/lpips/loss/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..924cfc85b8d63ef538f5676f830a2a8497932108
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/loss/LICENSE
@@ -0,0 +1,23 @@
+Copyright (c) 2018, Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/sgm/modules/autoencoding/lpips/loss/__init__.py b/sgm/modules/autoencoding/lpips/loss/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/modules/autoencoding/lpips/loss/__pycache__/__init__.cpython-39.pyc b/sgm/modules/autoencoding/lpips/loss/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ed8d29cbb2259e563654ecaed32f4f25378a210b
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diff --git a/sgm/modules/autoencoding/lpips/loss/__pycache__/lpips.cpython-39.pyc b/sgm/modules/autoencoding/lpips/loss/__pycache__/lpips.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3fb860d74c38805d32597c4045829ab47dd63fa1
Binary files /dev/null and b/sgm/modules/autoencoding/lpips/loss/__pycache__/lpips.cpython-39.pyc differ
diff --git a/sgm/modules/autoencoding/lpips/loss/lpips.py b/sgm/modules/autoencoding/lpips/loss/lpips.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e34f3d083674f675a5ca024e9bd27fb77e2b6b5
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/loss/lpips.py
@@ -0,0 +1,147 @@
+"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
+
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+from torchvision import models
+
+from ..util import get_ckpt_path
+
+
+class LPIPS(nn.Module):
+    # Learned perceptual metric
+    def __init__(self, use_dropout=True):
+        super().__init__()
+        self.scaling_layer = ScalingLayer()
+        self.chns = [64, 128, 256, 512, 512]  # vg16 features
+        self.net = vgg16(pretrained=True, requires_grad=False)
+        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.load_from_pretrained()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def load_from_pretrained(self, name="vgg_lpips"):
+        ckpt = get_ckpt_path(name, "sgm/modules/autoencoding/lpips/loss")
+        self.load_state_dict(
+            torch.load(ckpt, map_location=torch.device("cpu")), strict=False
+        )
+        print("loaded pretrained LPIPS loss from {}".format(ckpt))
+
+    @classmethod
+    def from_pretrained(cls, name="vgg_lpips"):
+        if name != "vgg_lpips":
+            raise NotImplementedError
+        model = cls()
+        ckpt = get_ckpt_path(name)
+        model.load_state_dict(
+            torch.load(ckpt, map_location=torch.device("cpu")), strict=False
+        )
+        return model
+
+    def forward(self, input, target):
+        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
+        outs0, outs1 = self.net(in0_input), self.net(in1_input)
+        feats0, feats1, diffs = {}, {}, {}
+        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+        for kk in range(len(self.chns)):
+            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(
+                outs1[kk]
+            )
+            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
+
+        res = [
+            spatial_average(lins[kk].model(diffs[kk]), keepdim=True)
+            for kk in range(len(self.chns))
+        ]
+        val = res[0]
+        for l in range(1, len(self.chns)):
+            val += res[l]
+        return val
+
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        self.register_buffer(
+            "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None]
+        )
+        self.register_buffer(
+            "scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None]
+        )
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    """A single linear layer which does a 1x1 conv"""
+
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+        layers = (
+            [
+                nn.Dropout(),
+            ]
+            if (use_dropout)
+            else []
+        )
+        layers += [
+            nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),
+        ]
+        self.model = nn.Sequential(*layers)
+
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple(
+            "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]
+        )
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+        return out
+
+
+def normalize_tensor(x, eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
+    return x / (norm_factor + eps)
+
+
+def spatial_average(x, keepdim=True):
+    return x.mean([2, 3], keepdim=keepdim)
diff --git a/sgm/modules/autoencoding/lpips/model/LICENSE b/sgm/modules/autoencoding/lpips/model/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..4b356e66b5aa689b339f1a80a9f1b5ba378003bb
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/model/LICENSE
@@ -0,0 +1,58 @@
+Copyright (c) 2017, Jun-Yan Zhu and Taesung Park
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+--------------------------- LICENSE FOR pix2pix --------------------------------
+BSD License
+
+For pix2pix software
+Copyright (c) 2016, Phillip Isola and Jun-Yan Zhu
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+----------------------------- LICENSE FOR DCGAN --------------------------------
+BSD License
+
+For dcgan.torch software
+
+Copyright (c) 2015, Facebook, Inc. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+Neither the name Facebook nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/sgm/modules/autoencoding/lpips/model/__init__.py b/sgm/modules/autoencoding/lpips/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/modules/autoencoding/lpips/model/model.py b/sgm/modules/autoencoding/lpips/model/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..66357d4e627f9a69a5abbbad15546c96fcd758fe
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/model/model.py
@@ -0,0 +1,88 @@
+import functools
+
+import torch.nn as nn
+
+from ..util import ActNorm
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm") != -1:
+        nn.init.normal_(m.weight.data, 1.0, 0.02)
+        nn.init.constant_(m.bias.data, 0)
+
+
+class NLayerDiscriminator(nn.Module):
+    """Defines a PatchGAN discriminator as in Pix2Pix
+    --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+    """
+
+    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
+        """Construct a PatchGAN discriminator
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super(NLayerDiscriminator, self).__init__()
+        if not use_actnorm:
+            norm_layer = nn.BatchNorm2d
+        else:
+            norm_layer = ActNorm
+        if (
+            type(norm_layer) == functools.partial
+        ):  # no need to use bias as BatchNorm2d has affine parameters
+            use_bias = norm_layer.func != nn.BatchNorm2d
+        else:
+            use_bias = norm_layer != nn.BatchNorm2d
+
+        kw = 4
+        padw = 1
+        sequence = [
+            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
+            nn.LeakyReLU(0.2, True),
+        ]
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = min(2**n, 8)
+            sequence += [
+                nn.Conv2d(
+                    ndf * nf_mult_prev,
+                    ndf * nf_mult,
+                    kernel_size=kw,
+                    stride=2,
+                    padding=padw,
+                    bias=use_bias,
+                ),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True),
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2**n_layers, 8)
+        sequence += [
+            nn.Conv2d(
+                ndf * nf_mult_prev,
+                ndf * nf_mult,
+                kernel_size=kw,
+                stride=1,
+                padding=padw,
+                bias=use_bias,
+            ),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True),
+        ]
+
+        sequence += [
+            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
+        ]  # output 1 channel prediction map
+        self.main = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        """Standard forward."""
+        return self.main(input)
diff --git a/sgm/modules/autoencoding/lpips/util.py b/sgm/modules/autoencoding/lpips/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..49c76e370bf16888ab61f42844b3c9f14ad9014c
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/util.py
@@ -0,0 +1,128 @@
+import hashlib
+import os
+
+import requests
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+
+URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"}
+
+CKPT_MAP = {"vgg_lpips": "vgg.pth"}
+
+MD5_MAP = {"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"}
+
+
+def download(url, local_path, chunk_size=1024):
+    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
+    with requests.get(url, stream=True) as r:
+        total_size = int(r.headers.get("content-length", 0))
+        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
+            with open(local_path, "wb") as f:
+                for data in r.iter_content(chunk_size=chunk_size):
+                    if data:
+                        f.write(data)
+                        pbar.update(chunk_size)
+
+
+def md5_hash(path):
+    with open(path, "rb") as f:
+        content = f.read()
+    return hashlib.md5(content).hexdigest()
+
+
+def get_ckpt_path(name, root, check=False):
+    assert name in URL_MAP
+    path = os.path.join(root, CKPT_MAP[name])
+    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
+        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
+        download(URL_MAP[name], path)
+        md5 = md5_hash(path)
+        assert md5 == MD5_MAP[name], md5
+    return path
+
+
+class ActNorm(nn.Module):
+    def __init__(
+        self, num_features, logdet=False, affine=True, allow_reverse_init=False
+    ):
+        assert affine
+        super().__init__()
+        self.logdet = logdet
+        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
+        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
+        self.allow_reverse_init = allow_reverse_init
+
+        self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))
+
+    def initialize(self, input):
+        with torch.no_grad():
+            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
+            mean = (
+                flatten.mean(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+            std = (
+                flatten.std(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+
+            self.loc.data.copy_(-mean)
+            self.scale.data.copy_(1 / (std + 1e-6))
+
+    def forward(self, input, reverse=False):
+        if reverse:
+            return self.reverse(input)
+        if len(input.shape) == 2:
+            input = input[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        _, _, height, width = input.shape
+
+        if self.training and self.initialized.item() == 0:
+            self.initialize(input)
+            self.initialized.fill_(1)
+
+        h = self.scale * (input + self.loc)
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+
+        if self.logdet:
+            log_abs = torch.log(torch.abs(self.scale))
+            logdet = height * width * torch.sum(log_abs)
+            logdet = logdet * torch.ones(input.shape[0]).to(input)
+            return h, logdet
+
+        return h
+
+    def reverse(self, output):
+        if self.training and self.initialized.item() == 0:
+            if not self.allow_reverse_init:
+                raise RuntimeError(
+                    "Initializing ActNorm in reverse direction is "
+                    "disabled by default. Use allow_reverse_init=True to enable."
+                )
+            else:
+                self.initialize(output)
+                self.initialized.fill_(1)
+
+        if len(output.shape) == 2:
+            output = output[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        h = output / self.scale - self.loc
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+        return h
diff --git a/sgm/modules/autoencoding/lpips/vqperceptual.py b/sgm/modules/autoencoding/lpips/vqperceptual.py
new file mode 100644
index 0000000000000000000000000000000000000000..6195f0a6ed7ee6fd32c1bccea071e6075e95ee43
--- /dev/null
+++ b/sgm/modules/autoencoding/lpips/vqperceptual.py
@@ -0,0 +1,17 @@
+import torch
+import torch.nn.functional as F
+
+
+def hinge_d_loss(logits_real, logits_fake):
+    loss_real = torch.mean(F.relu(1.0 - logits_real))
+    loss_fake = torch.mean(F.relu(1.0 + logits_fake))
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+    d_loss = 0.5 * (
+        torch.mean(torch.nn.functional.softplus(-logits_real))
+        + torch.mean(torch.nn.functional.softplus(logits_fake))
+    )
+    return d_loss
diff --git a/sgm/modules/autoencoding/regularizers/__init__.py b/sgm/modules/autoencoding/regularizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff2b1815a5ba88892375e8ec9bedacea49024113
--- /dev/null
+++ b/sgm/modules/autoencoding/regularizers/__init__.py
@@ -0,0 +1,31 @@
+from abc import abstractmethod
+from typing import Any, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ....modules.distributions.distributions import \
+    DiagonalGaussianDistribution
+from .base import AbstractRegularizer
+
+
+class DiagonalGaussianRegularizer(AbstractRegularizer):
+    def __init__(self, sample: bool = True):
+        super().__init__()
+        self.sample = sample
+
+    def get_trainable_parameters(self) -> Any:
+        yield from ()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        log = dict()
+        posterior = DiagonalGaussianDistribution(z)
+        if self.sample:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+        kl_loss = posterior.kl()
+        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+        log["kl_loss"] = kl_loss
+        return z, log
diff --git a/sgm/modules/autoencoding/regularizers/__pycache__/__init__.cpython-39.pyc b/sgm/modules/autoencoding/regularizers/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1fed79c95b42eeecc1b332024666fd2c010b1349
Binary files /dev/null and b/sgm/modules/autoencoding/regularizers/__pycache__/__init__.cpython-39.pyc differ
diff --git a/sgm/modules/autoencoding/regularizers/__pycache__/base.cpython-39.pyc b/sgm/modules/autoencoding/regularizers/__pycache__/base.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3e43fb5e04c3b3762bad8fb1fad6bc57ef160e33
Binary files /dev/null and b/sgm/modules/autoencoding/regularizers/__pycache__/base.cpython-39.pyc differ
diff --git a/sgm/modules/autoencoding/regularizers/base.py b/sgm/modules/autoencoding/regularizers/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..fca681bb3c1f4818b57e956e31b98f76077ccb67
--- /dev/null
+++ b/sgm/modules/autoencoding/regularizers/base.py
@@ -0,0 +1,40 @@
+from abc import abstractmethod
+from typing import Any, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class AbstractRegularizer(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def get_trainable_parameters(self) -> Any:
+        raise NotImplementedError()
+
+
+class IdentityRegularizer(AbstractRegularizer):
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        return z, dict()
+
+    def get_trainable_parameters(self) -> Any:
+        yield from ()
+
+
+def measure_perplexity(
+    predicted_indices: torch.Tensor, num_centroids: int
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
+    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
+    encodings = (
+        F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
+    )
+    avg_probs = encodings.mean(0)
+    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
+    cluster_use = torch.sum(avg_probs > 0)
+    return perplexity, cluster_use
diff --git a/sgm/modules/autoencoding/regularizers/quantize.py b/sgm/modules/autoencoding/regularizers/quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..86a4dbdd10101b24f03bba134c4f8d2ab007f0db
--- /dev/null
+++ b/sgm/modules/autoencoding/regularizers/quantize.py
@@ -0,0 +1,487 @@
+import logging
+from abc import abstractmethod
+from typing import Dict, Iterator, Literal, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch import einsum
+
+from .base import AbstractRegularizer, measure_perplexity
+
+logpy = logging.getLogger(__name__)
+
+
+class AbstractQuantizer(AbstractRegularizer):
+    def __init__(self):
+        super().__init__()
+        # Define these in your init
+        # shape (N,)
+        self.used: Optional[torch.Tensor]
+        self.re_embed: int
+        self.unknown_index: Union[Literal["random"], int]
+
+    def remap_to_used(self, inds: torch.Tensor) -> torch.Tensor:
+        assert self.used is not None, "You need to define used indices for remap"
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        match = (inds[:, :, None] == used[None, None, ...]).long()
+        new = match.argmax(-1)
+        unknown = match.sum(2) < 1
+        if self.unknown_index == "random":
+            new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(
+                device=new.device
+            )
+        else:
+            new[unknown] = self.unknown_index
+        return new.reshape(ishape)
+
+    def unmap_to_all(self, inds: torch.Tensor) -> torch.Tensor:
+        assert self.used is not None, "You need to define used indices for remap"
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        if self.re_embed > self.used.shape[0]:  # extra token
+            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
+        back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
+        return back.reshape(ishape)
+
+    @abstractmethod
+    def get_codebook_entry(
+        self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None
+    ) -> torch.Tensor:
+        raise NotImplementedError()
+
+    def get_trainable_parameters(self) -> Iterator[torch.nn.Parameter]:
+        yield from self.parameters()
+
+
+class GumbelQuantizer(AbstractQuantizer):
+    """
+    credit to @karpathy:
+    https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
+    Gumbel Softmax trick quantizer
+    Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
+    https://arxiv.org/abs/1611.01144
+    """
+
+    def __init__(
+        self,
+        num_hiddens: int,
+        embedding_dim: int,
+        n_embed: int,
+        straight_through: bool = True,
+        kl_weight: float = 5e-4,
+        temp_init: float = 1.0,
+        remap: Optional[str] = None,
+        unknown_index: str = "random",
+        loss_key: str = "loss/vq",
+    ) -> None:
+        super().__init__()
+
+        self.loss_key = loss_key
+        self.embedding_dim = embedding_dim
+        self.n_embed = n_embed
+
+        self.straight_through = straight_through
+        self.temperature = temp_init
+        self.kl_weight = kl_weight
+
+        self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
+        self.embed = nn.Embedding(n_embed, embedding_dim)
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+        else:
+            self.used = None
+            self.re_embed = n_embed
+        if unknown_index == "extra":
+            self.unknown_index = self.re_embed
+            self.re_embed = self.re_embed + 1
+        else:
+            assert unknown_index == "random" or isinstance(
+                unknown_index, int
+            ), "unknown index needs to be 'random', 'extra' or any integer"
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+        if self.remap is not None:
+            logpy.info(
+                f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
+                f"Using {self.unknown_index} for unknown indices."
+            )
+
+    def forward(
+        self, z: torch.Tensor, temp: Optional[float] = None, return_logits: bool = False
+    ) -> Tuple[torch.Tensor, Dict]:
+        # force hard = True when we are in eval mode, as we must quantize.
+        # actually, always true seems to work
+        hard = self.straight_through if self.training else True
+        temp = self.temperature if temp is None else temp
+        out_dict = {}
+        logits = self.proj(z)
+        if self.remap is not None:
+            # continue only with used logits
+            full_zeros = torch.zeros_like(logits)
+            logits = logits[:, self.used, ...]
+
+        soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
+        if self.remap is not None:
+            # go back to all entries but unused set to zero
+            full_zeros[:, self.used, ...] = soft_one_hot
+            soft_one_hot = full_zeros
+        z_q = einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
+
+        # + kl divergence to the prior loss
+        qy = F.softmax(logits, dim=1)
+        diff = (
+            self.kl_weight
+            * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
+        )
+        out_dict[self.loss_key] = diff
+
+        ind = soft_one_hot.argmax(dim=1)
+        out_dict["indices"] = ind
+        if self.remap is not None:
+            ind = self.remap_to_used(ind)
+
+        if return_logits:
+            out_dict["logits"] = logits
+
+        return z_q, out_dict
+
+    def get_codebook_entry(self, indices, shape):
+        # TODO: shape not yet optional
+        b, h, w, c = shape
+        assert b * h * w == indices.shape[0]
+        indices = rearrange(indices, "(b h w) -> b h w", b=b, h=h, w=w)
+        if self.remap is not None:
+            indices = self.unmap_to_all(indices)
+        one_hot = (
+            F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
+        )
+        z_q = einsum("b n h w, n d -> b d h w", one_hot, self.embed.weight)
+        return z_q
+
+
+class VectorQuantizer(AbstractQuantizer):
+    """
+    ____________________________________________
+    Discretization bottleneck part of the VQ-VAE.
+    Inputs:
+    - n_e : number of embeddings
+    - e_dim : dimension of embedding
+    - beta : commitment cost used in loss term,
+        beta * ||z_e(x)-sg[e]||^2
+    _____________________________________________
+    """
+
+    def __init__(
+        self,
+        n_e: int,
+        e_dim: int,
+        beta: float = 0.25,
+        remap: Optional[str] = None,
+        unknown_index: str = "random",
+        sane_index_shape: bool = False,
+        log_perplexity: bool = False,
+        embedding_weight_norm: bool = False,
+        loss_key: str = "loss/vq",
+    ):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.beta = beta
+        self.loss_key = loss_key
+
+        if not embedding_weight_norm:
+            self.embedding = nn.Embedding(self.n_e, self.e_dim)
+            self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+        else:
+            self.embedding = torch.nn.utils.weight_norm(
+                nn.Embedding(self.n_e, self.e_dim), dim=1
+            )
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+        else:
+            self.used = None
+            self.re_embed = n_e
+        if unknown_index == "extra":
+            self.unknown_index = self.re_embed
+            self.re_embed = self.re_embed + 1
+        else:
+            assert unknown_index == "random" or isinstance(
+                unknown_index, int
+            ), "unknown index needs to be 'random', 'extra' or any integer"
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+        if self.remap is not None:
+            logpy.info(
+                f"Remapping {self.n_e} indices to {self.re_embed} indices. "
+                f"Using {self.unknown_index} for unknown indices."
+            )
+
+        self.sane_index_shape = sane_index_shape
+        self.log_perplexity = log_perplexity
+
+    def forward(
+        self,
+        z: torch.Tensor,
+    ) -> Tuple[torch.Tensor, Dict]:
+        do_reshape = z.ndim == 4
+        if do_reshape:
+            #     # reshape z -> (batch, height, width, channel) and flatten
+            z = rearrange(z, "b c h w -> b h w c").contiguous()
+
+        else:
+            assert z.ndim < 4, "No reshaping strategy for inputs > 4 dimensions defined"
+            z = z.contiguous()
+
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        d = (
+            torch.sum(z_flattened**2, dim=1, keepdim=True)
+            + torch.sum(self.embedding.weight**2, dim=1)
+            - 2
+            * torch.einsum(
+                "bd,dn->bn", z_flattened, rearrange(self.embedding.weight, "n d -> d n")
+            )
+        )
+
+        min_encoding_indices = torch.argmin(d, dim=1)
+        z_q = self.embedding(min_encoding_indices).view(z.shape)
+        loss_dict = {}
+        if self.log_perplexity:
+            perplexity, cluster_usage = measure_perplexity(
+                min_encoding_indices.detach(), self.n_e
+            )
+            loss_dict.update({"perplexity": perplexity, "cluster_usage": cluster_usage})
+
+        # compute loss for embedding
+        loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean(
+            (z_q - z.detach()) ** 2
+        )
+        loss_dict[self.loss_key] = loss
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        if do_reshape:
+            z_q = rearrange(z_q, "b h w c -> b c h w").contiguous()
+
+        if self.remap is not None:
+            min_encoding_indices = min_encoding_indices.reshape(
+                z.shape[0], -1
+            )  # add batch axis
+            min_encoding_indices = self.remap_to_used(min_encoding_indices)
+            min_encoding_indices = min_encoding_indices.reshape(-1, 1)  # flatten
+
+        if self.sane_index_shape:
+            if do_reshape:
+                min_encoding_indices = min_encoding_indices.reshape(
+                    z_q.shape[0], z_q.shape[2], z_q.shape[3]
+                )
+            else:
+                min_encoding_indices = rearrange(
+                    min_encoding_indices, "(b s) 1 -> b s", b=z_q.shape[0]
+                )
+
+        loss_dict["min_encoding_indices"] = min_encoding_indices
+
+        return z_q, loss_dict
+
+    def get_codebook_entry(
+        self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None
+    ) -> torch.Tensor:
+        # shape specifying (batch, height, width, channel)
+        if self.remap is not None:
+            assert shape is not None, "Need to give shape for remap"
+            indices = indices.reshape(shape[0], -1)  # add batch axis
+            indices = self.unmap_to_all(indices)
+            indices = indices.reshape(-1)  # flatten again
+
+        # get quantized latent vectors
+        z_q = self.embedding(indices)
+
+        if shape is not None:
+            z_q = z_q.view(shape)
+            # reshape back to match original input shape
+            z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q
+
+
+class EmbeddingEMA(nn.Module):
+    def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5):
+        super().__init__()
+        self.decay = decay
+        self.eps = eps
+        weight = torch.randn(num_tokens, codebook_dim)
+        self.weight = nn.Parameter(weight, requires_grad=False)
+        self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False)
+        self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False)
+        self.update = True
+
+    def forward(self, embed_id):
+        return F.embedding(embed_id, self.weight)
+
+    def cluster_size_ema_update(self, new_cluster_size):
+        self.cluster_size.data.mul_(self.decay).add_(
+            new_cluster_size, alpha=1 - self.decay
+        )
+
+    def embed_avg_ema_update(self, new_embed_avg):
+        self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
+
+    def weight_update(self, num_tokens):
+        n = self.cluster_size.sum()
+        smoothed_cluster_size = (
+            (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
+        )
+        # normalize embedding average with smoothed cluster size
+        embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
+        self.weight.data.copy_(embed_normalized)
+
+
+class EMAVectorQuantizer(AbstractQuantizer):
+    def __init__(
+        self,
+        n_embed: int,
+        embedding_dim: int,
+        beta: float,
+        decay: float = 0.99,
+        eps: float = 1e-5,
+        remap: Optional[str] = None,
+        unknown_index: str = "random",
+        loss_key: str = "loss/vq",
+    ):
+        super().__init__()
+        self.codebook_dim = embedding_dim
+        self.num_tokens = n_embed
+        self.beta = beta
+        self.loss_key = loss_key
+
+        self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps)
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+        else:
+            self.used = None
+            self.re_embed = n_embed
+        if unknown_index == "extra":
+            self.unknown_index = self.re_embed
+            self.re_embed = self.re_embed + 1
+        else:
+            assert unknown_index == "random" or isinstance(
+                unknown_index, int
+            ), "unknown index needs to be 'random', 'extra' or any integer"
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+        if self.remap is not None:
+            logpy.info(
+                f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
+                f"Using {self.unknown_index} for unknown indices."
+            )
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
+        # reshape z -> (batch, height, width, channel) and flatten
+        # z, 'b c h w -> b h w c'
+        z = rearrange(z, "b c h w -> b h w c")
+        z_flattened = z.reshape(-1, self.codebook_dim)
+
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+        d = (
+            z_flattened.pow(2).sum(dim=1, keepdim=True)
+            + self.embedding.weight.pow(2).sum(dim=1)
+            - 2 * torch.einsum("bd,nd->bn", z_flattened, self.embedding.weight)
+        )  # 'n d -> d n'
+
+        encoding_indices = torch.argmin(d, dim=1)
+
+        z_q = self.embedding(encoding_indices).view(z.shape)
+        encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)
+        avg_probs = torch.mean(encodings, dim=0)
+        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
+
+        if self.training and self.embedding.update:
+            # EMA cluster size
+            encodings_sum = encodings.sum(0)
+            self.embedding.cluster_size_ema_update(encodings_sum)
+            # EMA embedding average
+            embed_sum = encodings.transpose(0, 1) @ z_flattened
+            self.embedding.embed_avg_ema_update(embed_sum)
+            # normalize embed_avg and update weight
+            self.embedding.weight_update(self.num_tokens)
+
+        # compute loss for embedding
+        loss = self.beta * F.mse_loss(z_q.detach(), z)
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        # z_q, 'b h w c -> b c h w'
+        z_q = rearrange(z_q, "b h w c -> b c h w")
+
+        out_dict = {
+            self.loss_key: loss,
+            "encodings": encodings,
+            "encoding_indices": encoding_indices,
+            "perplexity": perplexity,
+        }
+
+        return z_q, out_dict
+
+
+class VectorQuantizerWithInputProjection(VectorQuantizer):
+    def __init__(
+        self,
+        input_dim: int,
+        n_codes: int,
+        codebook_dim: int,
+        beta: float = 1.0,
+        output_dim: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(n_codes, codebook_dim, beta, **kwargs)
+        self.proj_in = nn.Linear(input_dim, codebook_dim)
+        self.output_dim = output_dim
+        if output_dim is not None:
+            self.proj_out = nn.Linear(codebook_dim, output_dim)
+        else:
+            self.proj_out = nn.Identity()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
+        rearr = False
+        in_shape = z.shape
+
+        if z.ndim > 3:
+            rearr = self.output_dim is not None
+            z = rearrange(z, "b c ... -> b (...) c")
+        z = self.proj_in(z)
+        z_q, loss_dict = super().forward(z)
+
+        z_q = self.proj_out(z_q)
+        if rearr:
+            if len(in_shape) == 4:
+                z_q = rearrange(z_q, "b (h w) c -> b c h w ", w=in_shape[-1])
+            elif len(in_shape) == 5:
+                z_q = rearrange(
+                    z_q, "b (t h w) c -> b c t h w ", w=in_shape[-1], h=in_shape[-2]
+                )
+            else:
+                raise NotImplementedError(
+                    f"rearranging not available for {len(in_shape)}-dimensional input."
+                )
+
+        return z_q, loss_dict
diff --git a/sgm/modules/autoencoding/temporal_ae.py b/sgm/modules/autoencoding/temporal_ae.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a17a91163469dbd8cfe2373d0d09554d5e25ed9
--- /dev/null
+++ b/sgm/modules/autoencoding/temporal_ae.py
@@ -0,0 +1,347 @@
+from typing import Callable, Iterable, Union
+
+import torch
+from einops import rearrange, repeat
+
+from sgm.modules.diffusionmodules.model import (XFORMERS_IS_AVAILABLE,
+                                                AttnBlock, Decoder,
+                                                MemoryEfficientAttnBlock,
+                                                ResnetBlock)
+from sgm.modules.diffusionmodules.openaimodel import (ResBlock,
+                                                      timestep_embedding)
+from sgm.modules.video_attention import VideoTransformerBlock
+from sgm.util import partialclass
+
+
+class VideoResBlock(ResnetBlock):
+    def __init__(
+        self,
+        out_channels,
+        *args,
+        dropout=0.0,
+        video_kernel_size=3,
+        alpha=0.0,
+        merge_strategy="learned",
+        **kwargs,
+    ):
+        super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs)
+        if video_kernel_size is None:
+            video_kernel_size = [3, 1, 1]
+        self.time_stack = ResBlock(
+            channels=out_channels,
+            emb_channels=0,
+            dropout=dropout,
+            dims=3,
+            use_scale_shift_norm=False,
+            use_conv=False,
+            up=False,
+            down=False,
+            kernel_size=video_kernel_size,
+            use_checkpoint=False,
+            skip_t_emb=True,
+        )
+
+        self.merge_strategy = merge_strategy
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned":
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def get_alpha(self, bs):
+        if self.merge_strategy == "fixed":
+            return self.mix_factor
+        elif self.merge_strategy == "learned":
+            return torch.sigmoid(self.mix_factor)
+        else:
+            raise NotImplementedError()
+
+    def forward(self, x, temb, skip_video=False, timesteps=None):
+        if timesteps is None:
+            timesteps = self.timesteps
+
+        b, c, h, w = x.shape
+
+        x = super().forward(x, temb)
+
+        if not skip_video:
+            x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+
+            x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+
+            x = self.time_stack(x, temb)
+
+            alpha = self.get_alpha(bs=b // timesteps)
+            x = alpha * x + (1.0 - alpha) * x_mix
+
+            x = rearrange(x, "b c t h w -> (b t) c h w")
+        return x
+
+
+class AE3DConv(torch.nn.Conv2d):
+    def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs):
+        super().__init__(in_channels, out_channels, *args, **kwargs)
+        if isinstance(video_kernel_size, Iterable):
+            padding = [int(k // 2) for k in video_kernel_size]
+        else:
+            padding = int(video_kernel_size // 2)
+
+        self.time_mix_conv = torch.nn.Conv3d(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=video_kernel_size,
+            padding=padding,
+        )
+
+    def forward(self, input, timesteps, skip_video=False):
+        x = super().forward(input)
+        if skip_video:
+            return x
+        x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+        x = self.time_mix_conv(x)
+        return rearrange(x, "b c t h w -> (b t) c h w")
+
+
+class VideoBlock(AttnBlock):
+    def __init__(
+        self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned"
+    ):
+        super().__init__(in_channels)
+        # no context, single headed, as in base class
+        self.time_mix_block = VideoTransformerBlock(
+            dim=in_channels,
+            n_heads=1,
+            d_head=in_channels,
+            checkpoint=False,
+            ff_in=True,
+            attn_mode="softmax",
+        )
+
+        time_embed_dim = self.in_channels * 4
+        self.video_time_embed = torch.nn.Sequential(
+            torch.nn.Linear(self.in_channels, time_embed_dim),
+            torch.nn.SiLU(),
+            torch.nn.Linear(time_embed_dim, self.in_channels),
+        )
+
+        self.merge_strategy = merge_strategy
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned":
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def forward(self, x, timesteps, skip_video=False):
+        if skip_video:
+            return super().forward(x)
+
+        x_in = x
+        x = self.attention(x)
+        h, w = x.shape[2:]
+        x = rearrange(x, "b c h w -> b (h w) c")
+
+        x_mix = x
+        num_frames = torch.arange(timesteps, device=x.device)
+        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+        num_frames = rearrange(num_frames, "b t -> (b t)")
+        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)
+        emb = self.video_time_embed(t_emb)  # b, n_channels
+        emb = emb[:, None, :]
+        x_mix = x_mix + emb
+
+        alpha = self.get_alpha()
+        x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
+        x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
+
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        x = self.proj_out(x)
+
+        return x_in + x
+
+    def get_alpha(
+        self,
+    ):
+        if self.merge_strategy == "fixed":
+            return self.mix_factor
+        elif self.merge_strategy == "learned":
+            return torch.sigmoid(self.mix_factor)
+        else:
+            raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}")
+
+
+class MemoryEfficientVideoBlock(MemoryEfficientAttnBlock):
+    def __init__(
+        self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned"
+    ):
+        super().__init__(in_channels)
+        # no context, single headed, as in base class
+        self.time_mix_block = VideoTransformerBlock(
+            dim=in_channels,
+            n_heads=1,
+            d_head=in_channels,
+            checkpoint=False,
+            ff_in=True,
+            attn_mode="softmax-xformers",
+        )
+
+        time_embed_dim = self.in_channels * 4
+        self.video_time_embed = torch.nn.Sequential(
+            torch.nn.Linear(self.in_channels, time_embed_dim),
+            torch.nn.SiLU(),
+            torch.nn.Linear(time_embed_dim, self.in_channels),
+        )
+
+        self.merge_strategy = merge_strategy
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned":
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def forward(self, x, timesteps, skip_time_block=False):
+        if skip_time_block:
+            return super().forward(x)
+
+        x_in = x
+        x = self.attention(x)
+        h, w = x.shape[2:]
+        x = rearrange(x, "b c h w -> b (h w) c")
+
+        x_mix = x
+        num_frames = torch.arange(timesteps, device=x.device)
+        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+        num_frames = rearrange(num_frames, "b t -> (b t)")
+        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)
+        emb = self.video_time_embed(t_emb)  # b, n_channels
+        emb = emb[:, None, :]
+        x_mix = x_mix + emb
+
+        alpha = self.get_alpha()
+        x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
+        x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
+
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        x = self.proj_out(x)
+
+        return x_in + x
+
+    def get_alpha(
+        self,
+    ):
+        if self.merge_strategy == "fixed":
+            return self.mix_factor
+        elif self.merge_strategy == "learned":
+            return torch.sigmoid(self.mix_factor)
+        else:
+            raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}")
+
+
+def make_time_attn(
+    in_channels,
+    attn_type="vanilla",
+    attn_kwargs=None,
+    alpha: float = 0,
+    merge_strategy: str = "learned",
+):
+    assert attn_type in [
+        "vanilla",
+        "vanilla-xformers",
+    ], f"attn_type {attn_type} not supported for spatio-temporal attention"
+    print(
+        f"making spatial and temporal attention of type '{attn_type}' with {in_channels} in_channels"
+    )
+    if not XFORMERS_IS_AVAILABLE and attn_type == "vanilla-xformers":
+        print(
+            f"Attention mode '{attn_type}' is not available. Falling back to vanilla attention. "
+            f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}"
+        )
+        attn_type = "vanilla"
+
+    if attn_type == "vanilla":
+        assert attn_kwargs is None
+        return partialclass(
+            VideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy
+        )
+    elif attn_type == "vanilla-xformers":
+        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+        return partialclass(
+            MemoryEfficientVideoBlock,
+            in_channels,
+            alpha=alpha,
+            merge_strategy=merge_strategy,
+        )
+    else:
+        return NotImplementedError()
+
+
+class Conv2DWrapper(torch.nn.Conv2d):
+    def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor:
+        return super().forward(input)
+
+
+class VideoDecoder(Decoder):
+    available_time_modes = ["all", "conv-only", "attn-only"]
+
+    def __init__(
+        self,
+        *args,
+        video_kernel_size: Union[int, list] = 3,
+        alpha: float = 0.0,
+        merge_strategy: str = "learned",
+        time_mode: str = "conv-only",
+        **kwargs,
+    ):
+        self.video_kernel_size = video_kernel_size
+        self.alpha = alpha
+        self.merge_strategy = merge_strategy
+        self.time_mode = time_mode
+        assert (
+            self.time_mode in self.available_time_modes
+        ), f"time_mode parameter has to be in {self.available_time_modes}"
+        super().__init__(*args, **kwargs)
+
+    def get_last_layer(self, skip_time_mix=False, **kwargs):
+        if self.time_mode == "attn-only":
+            raise NotImplementedError("TODO")
+        else:
+            return (
+                self.conv_out.time_mix_conv.weight
+                if not skip_time_mix
+                else self.conv_out.weight
+            )
+
+    def _make_attn(self) -> Callable:
+        if self.time_mode not in ["conv-only", "only-last-conv"]:
+            return partialclass(
+                make_time_attn,
+                alpha=self.alpha,
+                merge_strategy=self.merge_strategy,
+            )
+        else:
+            return super()._make_attn()
+
+    def _make_conv(self) -> Callable:
+        if self.time_mode != "attn-only":
+            return partialclass(AE3DConv, video_kernel_size=self.video_kernel_size)
+        else:
+            return Conv2DWrapper
+
+    def _make_resblock(self) -> Callable:
+        if self.time_mode not in ["attn-only", "only-last-conv"]:
+            return partialclass(
+                VideoResBlock,
+                video_kernel_size=self.video_kernel_size,
+                alpha=self.alpha,
+                merge_strategy=self.merge_strategy,
+            )
+        else:
+            return super()._make_resblock()
diff --git a/sgm/modules/diffusionmodules/__init__.py b/sgm/modules/diffusionmodules/__init__.py
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diff --git a/sgm/modules/diffusionmodules/denoiser.py b/sgm/modules/diffusionmodules/denoiser.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f2558ece099a4b37a91d1f4f53d29cd57af57f1
--- /dev/null
+++ b/sgm/modules/diffusionmodules/denoiser.py
@@ -0,0 +1,78 @@
+from typing import Dict, Union
+
+import torch
+import torch.nn as nn
+
+from ...util import append_dims, instantiate_from_config
+from .denoiser_scaling import DenoiserScaling
+from .discretizer import Discretization
+
+from pdb import set_trace as st
+
+
+class Denoiser(nn.Module):
+    def __init__(self, scaling_config: Dict):
+        super().__init__()
+
+        self.scaling: DenoiserScaling = instantiate_from_config(scaling_config)
+
+    def possibly_quantize_sigma(self, sigma: torch.Tensor) -> torch.Tensor:
+        return sigma
+
+    def possibly_quantize_c_noise(self, c_noise: torch.Tensor) -> torch.Tensor:
+        return c_noise
+
+    def forward(
+        self,
+        network: nn.Module,
+        input: torch.Tensor,
+        sigma: torch.Tensor,
+        cond: Dict,
+        **additional_model_inputs,
+    ) -> torch.Tensor:
+        sigma = self.possibly_quantize_sigma(sigma)
+        sigma_shape = sigma.shape
+        sigma = append_dims(sigma, input.ndim)
+        c_skip, c_out, c_in, c_noise = self.scaling(sigma)
+        # st()
+        c_noise = self.possibly_quantize_c_noise(c_noise.reshape(sigma_shape))
+        return (
+            network(input * c_in, c_noise, cond, **additional_model_inputs) * c_out
+            + input * c_skip
+        )
+
+
+class DiscreteDenoiser(Denoiser):
+    def __init__(
+        self,
+        scaling_config: Dict,
+        num_idx: int,
+        discretization_config: Dict,
+        do_append_zero: bool = False,
+        quantize_c_noise: bool = True,
+        flip: bool = True,
+    ):
+        super().__init__(scaling_config)
+        self.discretization: Discretization = instantiate_from_config(
+            discretization_config
+        )
+        sigmas = self.discretization(num_idx, do_append_zero=do_append_zero, flip=flip)
+        self.register_buffer("sigmas", sigmas)
+        self.quantize_c_noise = quantize_c_noise
+        self.num_idx = num_idx
+
+    def sigma_to_idx(self, sigma: torch.Tensor) -> torch.Tensor:
+        dists = sigma - self.sigmas[:, None]
+        return dists.abs().argmin(dim=0).view(sigma.shape)
+
+    def idx_to_sigma(self, idx: Union[torch.Tensor, int]) -> torch.Tensor:
+        return self.sigmas[idx]
+
+    def possibly_quantize_sigma(self, sigma: torch.Tensor) -> torch.Tensor:
+        return self.idx_to_sigma(self.sigma_to_idx(sigma))
+
+    def possibly_quantize_c_noise(self, c_noise: torch.Tensor) -> torch.Tensor:
+        if self.quantize_c_noise:
+            return self.sigma_to_idx(c_noise)
+        else:
+            return c_noise
diff --git a/sgm/modules/diffusionmodules/denoiser_scaling.py b/sgm/modules/diffusionmodules/denoiser_scaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4e287bfe8a82839a9a12fbd25c3446f43ab493b
--- /dev/null
+++ b/sgm/modules/diffusionmodules/denoiser_scaling.py
@@ -0,0 +1,59 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+import torch
+
+
+class DenoiserScaling(ABC):
+    @abstractmethod
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        pass
+
+
+class EDMScaling:
+    def __init__(self, sigma_data: float = 0.5):
+        self.sigma_data = sigma_data
+
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2)
+        c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5
+        c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
+        c_noise = 0.25 * sigma.log()
+        return c_skip, c_out, c_in, c_noise
+
+
+class EpsScaling:
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        c_skip = torch.ones_like(sigma, device=sigma.device)
+        c_out = -sigma
+        c_in = 1 / (sigma**2 + 1.0) ** 0.5
+        c_noise = sigma.clone()
+        return c_skip, c_out, c_in, c_noise
+
+
+class VScaling:
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        c_skip = 1.0 / (sigma**2 + 1.0)
+        c_out = -sigma / (sigma**2 + 1.0) ** 0.5
+        c_in = 1.0 / (sigma**2 + 1.0) ** 0.5
+        c_noise = sigma.clone()
+        return c_skip, c_out, c_in, c_noise
+
+
+class VScalingWithEDMcNoise(DenoiserScaling):
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        c_skip = 1.0 / (sigma**2 + 1.0)
+        c_out = -sigma / (sigma**2 + 1.0) ** 0.5
+        c_in = 1.0 / (sigma**2 + 1.0) ** 0.5
+        c_noise = 0.25 * sigma.log()
+        return c_skip, c_out, c_in, c_noise
diff --git a/sgm/modules/diffusionmodules/denoiser_weighting.py b/sgm/modules/diffusionmodules/denoiser_weighting.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8b03ca58f17ea3d7374f4bbb7bf1d2994755e00
--- /dev/null
+++ b/sgm/modules/diffusionmodules/denoiser_weighting.py
@@ -0,0 +1,24 @@
+import torch
+
+
+class UnitWeighting:
+    def __call__(self, sigma):
+        return torch.ones_like(sigma, device=sigma.device)
+
+
+class EDMWeighting:
+    def __init__(self, sigma_data=0.5):
+        self.sigma_data = sigma_data
+
+    def __call__(self, sigma):
+        return (sigma**2 + self.sigma_data**2) / (sigma * self.sigma_data) ** 2
+
+
+class VWeighting(EDMWeighting):
+    def __init__(self):
+        super().__init__(sigma_data=1.0)
+
+
+class EpsWeighting:
+    def __call__(self, sigma):
+        return sigma**-2.0
diff --git a/sgm/modules/diffusionmodules/discretizer.py b/sgm/modules/diffusionmodules/discretizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..02add6081c5e3164d4402619b44d5be235d3ec58
--- /dev/null
+++ b/sgm/modules/diffusionmodules/discretizer.py
@@ -0,0 +1,69 @@
+from abc import abstractmethod
+from functools import partial
+
+import numpy as np
+import torch
+
+from ...modules.diffusionmodules.util import make_beta_schedule
+from ...util import append_zero
+
+
+def generate_roughly_equally_spaced_steps(
+    num_substeps: int, max_step: int
+) -> np.ndarray:
+    return np.linspace(max_step - 1, 0, num_substeps, endpoint=False).astype(int)[::-1]
+
+
+class Discretization:
+    def __call__(self, n, do_append_zero=True, device="cpu", flip=False):
+        sigmas = self.get_sigmas(n, device=device)
+        sigmas = append_zero(sigmas) if do_append_zero else sigmas
+        return sigmas if not flip else torch.flip(sigmas, (0,))
+
+    @abstractmethod
+    def get_sigmas(self, n, device):
+        pass
+
+
+class EDMDiscretization(Discretization):
+    def __init__(self, sigma_min=0.002, sigma_max=80.0, rho=7.0):
+        self.sigma_min = sigma_min
+        self.sigma_max = sigma_max
+        self.rho = rho
+
+    def get_sigmas(self, n, device="cpu"):
+        ramp = torch.linspace(0, 1, n, device=device)
+        min_inv_rho = self.sigma_min ** (1 / self.rho)
+        max_inv_rho = self.sigma_max ** (1 / self.rho)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** self.rho
+        return sigmas
+
+
+class LegacyDDPMDiscretization(Discretization):
+    def __init__(
+        self,
+        linear_start=0.00085,
+        linear_end=0.0120,
+        num_timesteps=1000,
+    ):
+        super().__init__()
+        self.num_timesteps = num_timesteps
+        betas = make_beta_schedule(
+            "linear", num_timesteps, linear_start=linear_start, linear_end=linear_end
+        )
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.to_torch = partial(torch.tensor, dtype=torch.float32)
+
+    def get_sigmas(self, n, device="cpu"):
+        if n < self.num_timesteps:
+            timesteps = generate_roughly_equally_spaced_steps(n, self.num_timesteps)
+            alphas_cumprod = self.alphas_cumprod[timesteps]
+        elif n == self.num_timesteps:
+            alphas_cumprod = self.alphas_cumprod
+        else:
+            raise ValueError
+
+        to_torch = partial(torch.tensor, dtype=torch.float32, device=device)
+        sigmas = to_torch((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        return torch.flip(sigmas, (0,))
diff --git a/sgm/modules/diffusionmodules/guiders.py b/sgm/modules/diffusionmodules/guiders.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8eca43e8b7b8b7b0e6b9f3e2bddbae6e3456fee
--- /dev/null
+++ b/sgm/modules/diffusionmodules/guiders.py
@@ -0,0 +1,99 @@
+import logging
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+from einops import rearrange, repeat
+
+from ...util import append_dims, default
+
+logpy = logging.getLogger(__name__)
+
+
+class Guider(ABC):
+    @abstractmethod
+    def __call__(self, x: torch.Tensor, sigma: float) -> torch.Tensor:
+        pass
+
+    def prepare_inputs(
+        self, x: torch.Tensor, s: float, c: Dict, uc: Dict
+    ) -> Tuple[torch.Tensor, float, Dict]:
+        pass
+
+
+class VanillaCFG(Guider):
+    def __init__(self, scale: float):
+        self.scale = scale
+
+    def __call__(self, x: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
+        x_u, x_c = x.chunk(2)
+        x_pred = x_u + self.scale * (x_c - x_u)
+        return x_pred
+
+    def prepare_inputs(self, x, s, c, uc):
+        c_out = dict()
+
+        for k in c:
+            if k in ["vector", "crossattn", "concat"]:
+                c_out[k] = torch.cat((uc[k], c[k]), 0)
+            else:
+                assert c[k] == uc[k]
+                c_out[k] = c[k]
+        return torch.cat([x] * 2), torch.cat([s] * 2), c_out
+
+
+class IdentityGuider(Guider):
+    def __call__(self, x: torch.Tensor, sigma: float) -> torch.Tensor:
+        return x
+
+    def prepare_inputs(
+        self, x: torch.Tensor, s: float, c: Dict, uc: Dict
+    ) -> Tuple[torch.Tensor, float, Dict]:
+        c_out = dict()
+
+        for k in c:
+            c_out[k] = c[k]
+
+        return x, s, c_out
+
+
+class LinearPredictionGuider(Guider):
+    def __init__(
+        self,
+        max_scale: float,
+        num_frames: int,
+        min_scale: float = 1.0,
+        additional_cond_keys: Optional[Union[List[str], str]] = None,
+    ):
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.num_frames = num_frames
+        self.scale = torch.linspace(min_scale, max_scale, num_frames).unsqueeze(0)
+
+        additional_cond_keys = default(additional_cond_keys, [])
+        if isinstance(additional_cond_keys, str):
+            additional_cond_keys = [additional_cond_keys]
+        self.additional_cond_keys = additional_cond_keys
+
+    def __call__(self, x: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
+        x_u, x_c = x.chunk(2)
+
+        x_u = rearrange(x_u, "(b t) ... -> b t ...", t=self.num_frames)
+        x_c = rearrange(x_c, "(b t) ... -> b t ...", t=self.num_frames)
+        scale = repeat(self.scale, "1 t -> b t", b=x_u.shape[0])
+        scale = append_dims(scale, x_u.ndim).to(x_u.device)
+
+        return rearrange(x_u + scale * (x_c - x_u), "b t ... -> (b t) ...")
+
+    def prepare_inputs(
+        self, x: torch.Tensor, s: torch.Tensor, c: dict, uc: dict
+    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
+        c_out = dict()
+
+        for k in c:
+            if k in ["vector", "crossattn", "concat"] + self.additional_cond_keys:
+                c_out[k] = torch.cat((uc[k], c[k]), 0)
+            else:
+                assert c[k] == uc[k]
+                c_out[k] = c[k]
+        return torch.cat([x] * 2), torch.cat([s] * 2), c_out
diff --git a/sgm/modules/diffusionmodules/loss.py b/sgm/modules/diffusionmodules/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..a24bda7e6f31b7b3bc0b1c6ecdea62961b2da943
--- /dev/null
+++ b/sgm/modules/diffusionmodules/loss.py
@@ -0,0 +1,147 @@
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from ...modules.autoencoding.lpips.loss.lpips import LPIPS
+from ...modules.encoders.modules import GeneralConditioner
+from ...util import append_dims, instantiate_from_config
+from .denoiser import Denoiser
+
+from transport import create_transport, Sampler
+from pdb import set_trace as st
+
+class FMLoss(nn.Module):
+    def __init__(self, transport_config):
+        super().__init__()
+        self.transport = instantiate_from_config(transport_config)
+
+    def _forward(
+        self,
+        network: nn.Module,
+        cond: Dict,
+        input: torch.Tensor,
+        batch: Dict,
+    ) -> Tuple[torch.Tensor, Dict]:
+        # additional_model_inputs = {
+        #     key: batch[key] for key in self.batch2model_keys.intersection(batch)
+        # }
+        model_kwargs = dict(context=cond)
+
+        loss_dict = self.transport.training_losses(network, input, model_kwargs)
+        # st() # check transport and model_kwargs whether OK
+        loss = loss_dict["loss"].mean()
+        return loss, loss_dict
+
+    
+    def forward(
+        self,
+        network: nn.Module,
+        # denoiser: Denoiser,
+        conditioner: GeneralConditioner,
+        input: torch.Tensor,
+        batch: Dict,
+    ) -> torch.Tensor:
+        cond = conditioner(batch)
+        return self._forward(network, cond, input, batch)
+
+
+class StandardDiffusionLoss(nn.Module):
+    def __init__(
+        self,
+        sigma_sampler_config: dict,
+        loss_weighting_config: dict,
+        loss_type: str = "l2",
+        offset_noise_level: float = 0.0,
+        batch2model_keys: Optional[Union[str, List[str]]] = None,
+    ):
+        super().__init__()
+
+        assert loss_type in ["l2", "l1", "lpips"]
+
+        self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
+        self.loss_weighting = instantiate_from_config(loss_weighting_config)
+
+        self.loss_type = loss_type
+        self.offset_noise_level = offset_noise_level
+
+        if loss_type == "lpips":
+            self.lpips = LPIPS().eval()
+
+        if not batch2model_keys:
+            batch2model_keys = []
+
+        if isinstance(batch2model_keys, str):
+            batch2model_keys = [batch2model_keys]
+
+        self.batch2model_keys = set(batch2model_keys)
+
+    def get_noised_input(
+        self, sigmas_bc: torch.Tensor, noise: torch.Tensor, input: torch.Tensor
+    ) -> torch.Tensor:
+        noised_input = input + noise * sigmas_bc
+        return noised_input
+
+    def forward(
+        self,
+        network: nn.Module,
+        denoiser: Denoiser,
+        conditioner: GeneralConditioner,
+        input: torch.Tensor,
+        batch: Dict,
+    ) -> torch.Tensor:
+        cond = conditioner(batch)
+        return self._forward(network, denoiser, cond, input, batch)
+
+    def _forward(
+        self,
+        network: nn.Module,
+        denoiser: Denoiser,
+        cond: Dict,
+        input: torch.Tensor,
+        batch: Dict,
+    ) -> Tuple[torch.Tensor, Dict]:
+        additional_model_inputs = {
+            key: batch[key] for key in self.batch2model_keys.intersection(batch)
+        }
+        sigmas = self.sigma_sampler(input.shape[0]).to(input)
+
+        noise = torch.randn_like(input)
+        if self.offset_noise_level > 0.0:
+            offset_shape = (
+                (input.shape[0], 1, input.shape[2])
+                if self.n_frames is not None
+                else (input.shape[0], input.shape[1])
+            )
+            noise = noise + self.offset_noise_level * append_dims(
+                torch.randn(offset_shape, device=input.device),
+                input.ndim,
+            )
+        sigmas_bc = append_dims(sigmas, input.ndim)
+        noised_input = self.get_noised_input(sigmas_bc, noise, input)
+
+        model_output = denoiser(
+            network, noised_input, sigmas, cond, **additional_model_inputs
+        )
+        w = append_dims(self.loss_weighting(sigmas), input.ndim)
+        return self.get_loss(model_output, input, w), {
+            'noised_input': noised_input,
+            'sigmas': sigmas,
+            'noise': noise,
+            'model_output': model_output, # x_start
+        }
+
+    def get_loss(self, model_output, target, w):
+        if self.loss_type == "l2":
+            return torch.mean(
+                (w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1
+            )
+        elif self.loss_type == "l1":
+            return torch.mean(
+                (w * (model_output - target).abs()).reshape(target.shape[0], -1), 1
+            )
+        elif self.loss_type == "lpips":
+            loss = self.lpips(model_output, target).reshape(-1)
+            return loss
+        else:
+            raise NotImplementedError(f"Unknown loss type {self.loss_type}")
diff --git a/sgm/modules/diffusionmodules/loss_weighting.py b/sgm/modules/diffusionmodules/loss_weighting.py
new file mode 100644
index 0000000000000000000000000000000000000000..e12c0a76635435babd1af33969e82fa284525af8
--- /dev/null
+++ b/sgm/modules/diffusionmodules/loss_weighting.py
@@ -0,0 +1,32 @@
+from abc import ABC, abstractmethod
+
+import torch
+
+
+class DiffusionLossWeighting(ABC):
+    @abstractmethod
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        pass
+
+
+class UnitWeighting(DiffusionLossWeighting):
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        return torch.ones_like(sigma, device=sigma.device)
+
+
+class EDMWeighting(DiffusionLossWeighting):
+    def __init__(self, sigma_data: float = 0.5):
+        self.sigma_data = sigma_data
+
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        return (sigma**2 + self.sigma_data**2) / (sigma * self.sigma_data) ** 2
+
+
+class VWeighting(EDMWeighting):
+    def __init__(self):
+        super().__init__(sigma_data=1.0)
+
+
+class EpsWeighting(DiffusionLossWeighting):
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        return sigma**-2.0
diff --git a/sgm/modules/diffusionmodules/model.py b/sgm/modules/diffusionmodules/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cf9d92140dee8443a0ea6b5cf218f2879ad88f4
--- /dev/null
+++ b/sgm/modules/diffusionmodules/model.py
@@ -0,0 +1,748 @@
+# pytorch_diffusion + derived encoder decoder
+import logging
+import math
+from typing import Any, Callable, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from packaging import version
+
+logpy = logging.getLogger(__name__)
+
+try:
+    import xformers
+    import xformers.ops
+
+    XFORMERS_IS_AVAILABLE = True
+except:
+    XFORMERS_IS_AVAILABLE = False
+    logpy.warning("no module 'xformers'. Processing without...")
+
+from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models:
+    From Fairseq.
+    Build sinusoidal embeddings.
+    This matches the implementation in tensor2tensor, but differs slightly
+    from the description in Section 3.5 of "Attention Is All You Need".
+    """
+    assert len(timesteps.shape) == 1
+
+    half_dim = embedding_dim // 2
+    emb = math.log(10000) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+    emb = emb.to(device=timesteps.device)
+    emb = timesteps.float()[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+def nonlinearity(x):
+    # swish
+    return x * torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+    return torch.nn.GroupNorm(
+        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(
+                in_channels, in_channels, kernel_size=3, stride=1, padding=1
+            )
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(
+                in_channels, in_channels, kernel_size=3, stride=2, padding=0
+            )
+
+    def forward(self, x):
+        if self.with_conv:
+            pad = (0, 1, 0, 1)
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout,
+        temb_channels=512,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels)
+        self.conv1 = torch.nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = Normalize(out_channels)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv2d(
+                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
+                )
+            else:
+                self.nin_shortcut = torch.nn.Conv2d(
+                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
+                )
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class LinAttnBlock(LinearAttention):
+    """to match AttnBlock usage"""
+
+    def __init__(self, in_channels):
+        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def attention(self, h_: torch.Tensor) -> torch.Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        b, c, h, w = q.shape
+        q, k, v = map(
+            lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v)
+        )
+        h_ = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v
+        )  # scale is dim ** -0.5 per default
+        # compute attention
+
+        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
+
+    def forward(self, x, **kwargs):
+        h_ = x
+        h_ = self.attention(h_)
+        h_ = self.proj_out(h_)
+        return x + h_
+
+
+class MemoryEfficientAttnBlock(nn.Module):
+    """
+    Uses xformers efficient implementation,
+    see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    Note: this is a single-head self-attention operation
+    """
+
+    #
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.attention_op: Optional[Any] = None
+
+    def attention(self, h_: torch.Tensor) -> torch.Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        B, C, H, W = q.shape
+        q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v))
+
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(B, t.shape[1], 1, C)
+            .permute(0, 2, 1, 3)
+            .reshape(B * 1, t.shape[1], C)
+            .contiguous(),
+            (q, k, v),
+        )
+        out = xformers.ops.memory_efficient_attention(
+            q, k, v, attn_bias=None, op=self.attention_op
+        )
+
+        out = (
+            out.unsqueeze(0)
+            .reshape(B, 1, out.shape[1], C)
+            .permute(0, 2, 1, 3)
+            .reshape(B, out.shape[1], C)
+        )
+        return rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C)
+
+    def forward(self, x, **kwargs):
+        h_ = x
+        h_ = self.attention(h_)
+        h_ = self.proj_out(h_)
+        return x + h_
+
+
+class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
+    def forward(self, x, context=None, mask=None, **unused_kwargs):
+        b, c, h, w = x.shape
+        x = rearrange(x, "b c h w -> b (h w) c")
+        out = super().forward(x, context=context, mask=mask)
+        out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c)
+        return x + out
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+    assert attn_type in [
+        "vanilla",
+        "vanilla-xformers",
+        "memory-efficient-cross-attn",
+        "linear",
+        "none",
+    ], f"attn_type {attn_type} unknown"
+    if (
+        version.parse(torch.__version__) < version.parse("2.0.0")
+        and attn_type != "none"
+    ):
+        assert XFORMERS_IS_AVAILABLE, (
+            f"We do not support vanilla attention in {torch.__version__} anymore, "
+            f"as it is too expensive. Please install xformers via e.g. 'pip install xformers==0.0.16'"
+        )
+        attn_type = "vanilla-xformers"
+    logpy.info(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    if attn_type == "vanilla":
+        assert attn_kwargs is None
+        return AttnBlock(in_channels)
+    elif attn_type == "vanilla-xformers":
+        logpy.info(
+            f"building MemoryEfficientAttnBlock with {in_channels} in_channels..."
+        )
+        return MemoryEfficientAttnBlock(in_channels)
+    elif type == "memory-efficient-cross-attn":
+        attn_kwargs["query_dim"] = in_channels
+        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
+    elif attn_type == "none":
+        return nn.Identity(in_channels)
+    else:
+        return LinAttnBlock(in_channels)
+
+
+class Model(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        use_timestep=True,
+        use_linear_attn=False,
+        attn_type="vanilla",
+    ):
+        super().__init__()
+        if use_linear_attn:
+            attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = self.ch * 4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList(
+                [
+                    torch.nn.Linear(self.ch, self.temb_ch),
+                    torch.nn.Linear(self.temb_ch, self.temb_ch),
+                ]
+            )
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(
+            in_channels, self.ch, kernel_size=3, stride=1, padding=1
+        )
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            skip_in = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch * in_ch_mult[i_level]
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in + skip_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(
+            block_in, out_ch, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x, t=None, context=None):
+        # assert x.shape[2] == x.shape[3] == self.resolution
+        if context is not None:
+            # assume aligned context, cat along channel axis
+            x = torch.cat((x, context), dim=1)
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb
+                )
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+    def get_last_layer(self):
+        return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        double_z=True,
+        use_linear_attn=False,
+        attn_type="vanilla",
+        **ignore_kwargs,
+    ):
+        super().__init__()
+        if use_linear_attn:
+            attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(
+            in_channels, self.ch, kernel_size=3, stride=1, padding=1
+        )
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(
+            block_in,
+            2 * z_channels if double_z else z_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+
+    def forward(self, x):
+        # timestep embedding
+        temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        give_pre_end=False,
+        tanh_out=False,
+        use_linear_attn=False,
+        attn_type="vanilla",
+        **ignorekwargs,
+    ):
+        super().__init__()
+        if use_linear_attn:
+            attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,) + tuple(ch_mult)
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+        logpy.info(
+            "Working with z of shape {} = {} dimensions.".format(
+                self.z_shape, np.prod(self.z_shape)
+            )
+        )
+
+        make_attn_cls = self._make_attn()
+        make_resblock_cls = self._make_resblock()
+        make_conv_cls = self._make_conv()
+        # z to block_in
+        self.conv_in = torch.nn.Conv2d(
+            z_channels, block_in, kernel_size=3, stride=1, padding=1
+        )
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = make_resblock_cls(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+        self.mid.attn_1 = make_attn_cls(block_in, attn_type=attn_type)
+        self.mid.block_2 = make_resblock_cls(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                block.append(
+                    make_resblock_cls(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn_cls(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = make_conv_cls(
+            block_in, out_ch, kernel_size=3, stride=1, padding=1
+        )
+
+    def _make_attn(self) -> Callable:
+        return make_attn
+
+    def _make_resblock(self) -> Callable:
+        return ResnetBlock
+
+    def _make_conv(self) -> Callable:
+        return torch.nn.Conv2d
+
+    def get_last_layer(self, **kwargs):
+        return self.conv_out.weight
+
+    def forward(self, z, **kwargs):
+        # assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
+        # timestep embedding
+        temb = None
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h, temb, **kwargs)
+        h = self.mid.attn_1(h, **kwargs)
+        h = self.mid.block_2(h, temb, **kwargs)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h, temb, **kwargs)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h, **kwargs)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h, **kwargs)
+        if self.tanh_out:
+            h = torch.tanh(h)
+        return h
diff --git a/sgm/modules/diffusionmodules/openaimodel.py b/sgm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..b58e1b0e9be031cd09803d451fc59f2a5ce88eea
--- /dev/null
+++ b/sgm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,853 @@
+import logging
+import math
+from abc import abstractmethod
+from typing import Iterable, List, Optional, Tuple, Union
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.utils.checkpoint import checkpoint
+
+from ...modules.attention import SpatialTransformer
+from ...modules.diffusionmodules.util import (avg_pool_nd, conv_nd, linear,
+                                              normalization,
+                                              timestep_embedding, zero_module)
+from ...modules.video_attention import SpatialVideoTransformer
+from ...util import exists
+
+logpy = logging.getLogger(__name__)
+
+
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(
+            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
+        )
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x: th.Tensor) -> th.Tensor:
+        b, c, _ = x.shape
+        x = x.reshape(b, c, -1)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x: th.Tensor, emb: th.Tensor):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(
+        self,
+        x: th.Tensor,
+        emb: th.Tensor,
+        context: Optional[th.Tensor] = None,
+        image_only_indicator: Optional[th.Tensor] = None,
+        time_context: Optional[int] = None,
+        num_video_frames: Optional[int] = None,
+    ):
+        from ...modules.diffusionmodules.video_model import VideoResBlock
+
+        for layer in self:
+            module = layer
+
+            if isinstance(module, TimestepBlock) and not isinstance(
+                module, VideoResBlock
+            ):
+                x = layer(x, emb)
+            elif isinstance(module, VideoResBlock):
+                x = layer(x, emb, num_video_frames, image_only_indicator)
+            elif isinstance(module, SpatialVideoTransformer):
+                x = layer(
+                    x,
+                    context,
+                    time_context,
+                    num_video_frames,
+                    image_only_indicator,
+                )
+            elif isinstance(module, SpatialTransformer):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        use_conv: bool,
+        dims: int = 2,
+        out_channels: Optional[int] = None,
+        padding: int = 1,
+        third_up: bool = False,
+        kernel_size: int = 3,
+        scale_factor: int = 2,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        self.third_up = third_up
+        self.scale_factor = scale_factor
+        if use_conv:
+            self.conv = conv_nd(
+                dims, self.channels, self.out_channels, kernel_size, padding=padding
+            )
+
+    def forward(self, x: th.Tensor) -> th.Tensor:
+        assert x.shape[1] == self.channels
+
+        if self.dims == 3:
+            t_factor = 1 if not self.third_up else self.scale_factor
+            x = F.interpolate(
+                x,
+                (
+                    t_factor * x.shape[2],
+                    x.shape[3] * self.scale_factor,
+                    x.shape[4] * self.scale_factor,
+                ),
+                mode="nearest",
+            )
+        else:
+            x = F.interpolate(x, scale_factor=self.scale_factor, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        use_conv: bool,
+        dims: int = 2,
+        out_channels: Optional[int] = None,
+        padding: int = 1,
+        third_down: bool = False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else ((1, 2, 2) if not third_down else (2, 2, 2))
+        if use_conv:
+            logpy.info(f"Building a Downsample layer with {dims} dims.")
+            logpy.info(
+                f"  --> settings are: \n in-chn: {self.channels}, out-chn: {self.out_channels}, "
+                f"kernel-size: 3, stride: {stride}, padding: {padding}"
+            )
+            if dims == 3:
+                logpy.info(f"  --> Downsampling third axis (time): {third_down}")
+            self.op = conv_nd(
+                dims,
+                self.channels,
+                self.out_channels,
+                3,
+                stride=stride,
+                padding=padding,
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x: th.Tensor) -> th.Tensor:
+        assert x.shape[1] == self.channels
+
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        emb_channels: int,
+        dropout: float,
+        out_channels: Optional[int] = None,
+        use_conv: bool = False,
+        use_scale_shift_norm: bool = False,
+        dims: int = 2,
+        use_checkpoint: bool = False,
+        up: bool = False,
+        down: bool = False,
+        kernel_size: int = 3,
+        exchange_temb_dims: bool = False,
+        skip_t_emb: bool = False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+        self.exchange_temb_dims = exchange_temb_dims
+
+        if isinstance(kernel_size, Iterable):
+            padding = [k // 2 for k in kernel_size]
+        else:
+            padding = kernel_size // 2
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.skip_t_emb = skip_t_emb
+        self.emb_out_channels = (
+            2 * self.out_channels if use_scale_shift_norm else self.out_channels
+        )
+        if self.skip_t_emb:
+            logpy.info(f"Skipping timestep embedding in {self.__class__.__name__}")
+            assert not self.use_scale_shift_norm
+            self.emb_layers = None
+            self.exchange_temb_dims = False
+        else:
+            self.emb_layers = nn.Sequential(
+                nn.SiLU(),
+                linear(
+                    emb_channels,
+                    self.emb_out_channels,
+                ),
+            )
+
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(
+                    dims,
+                    self.out_channels,
+                    self.out_channels,
+                    kernel_size,
+                    padding=padding,
+                )
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, kernel_size, padding=padding
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x: th.Tensor, emb: th.Tensor) -> th.Tensor:
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        if self.use_checkpoint:
+            return checkpoint(self._forward, x, emb)
+        else:
+            return self._forward(x, emb)
+
+    def _forward(self, x: th.Tensor, emb: th.Tensor) -> th.Tensor:
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+
+        if self.skip_t_emb:
+            emb_out = th.zeros_like(h)
+        else:
+            emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            if self.exchange_temb_dims:
+                emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int = 1,
+        num_head_channels: int = -1,
+        use_checkpoint: bool = False,
+        use_new_attention_order: bool = False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x: th.Tensor, **kwargs) -> th.Tensor:
+        return checkpoint(self._forward, x)
+
+    def _forward(self, x: th.Tensor) -> th.Tensor:
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads: int):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv: th.Tensor) -> th.Tensor:
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads: int):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv: th.Tensor) -> th.Tensor:
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+
+class Timestep(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, t: th.Tensor) -> th.Tensor:
+        return timestep_embedding(t, self.dim)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        model_channels: int,
+        out_channels: int,
+        num_res_blocks: int,
+        attention_resolutions: int,
+        dropout: float = 0.0,
+        channel_mult: Union[List, Tuple] = (1, 2, 4, 8),
+        conv_resample: bool = True,
+        dims: int = 2,
+        num_classes: Optional[Union[int, str]] = None,
+        use_checkpoint: bool = False,
+        num_heads: int = -1,
+        num_head_channels: int = -1,
+        num_heads_upsample: int = -1,
+        use_scale_shift_norm: bool = False,
+        resblock_updown: bool = False,
+        transformer_depth: int = 1,
+        context_dim: Optional[int] = None,
+        disable_self_attentions: Optional[List[bool]] = None,
+        num_attention_blocks: Optional[List[int]] = None,
+        disable_middle_self_attn: bool = False,
+        disable_middle_transformer: bool = False,
+        use_linear_in_transformer: bool = False,
+        spatial_transformer_attn_type: str = "softmax",
+        adm_in_channels: Optional[int] = None,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert (
+                num_head_channels != -1
+            ), "Either num_heads or num_head_channels has to be set"
+
+        if num_head_channels == -1:
+            assert (
+                num_heads != -1
+            ), "Either num_heads or num_head_channels has to be set"
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        if isinstance(transformer_depth, int):
+            transformer_depth = len(channel_mult) * [transformer_depth]
+        transformer_depth_middle = transformer_depth[-1]
+
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError(
+                    "provide num_res_blocks either as an int (globally constant) or "
+                    "as a list/tuple (per-level) with the same length as channel_mult"
+                )
+            self.num_res_blocks = num_res_blocks
+
+        if disable_self_attentions is not None:
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(
+                map(
+                    lambda i: self.num_res_blocks[i] >= num_attention_blocks[i],
+                    range(len(num_attention_blocks)),
+                )
+            )
+            logpy.info(
+                f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                f"attention will still not be set."
+            )
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+            elif self.num_classes == "continuous":
+                logpy.info("setting up linear c_adm embedding layer")
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            elif self.num_classes == "timestep":
+                self.label_emb = nn.Sequential(
+                    Timestep(model_channels),
+                    nn.Sequential(
+                        linear(model_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    ),
+                )
+            elif self.num_classes == "sequential":
+                assert adm_in_channels is not None
+                self.label_emb = nn.Sequential(
+                    nn.Sequential(
+                        linear(adm_in_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    )
+                )
+            else:
+                raise ValueError
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+
+                    if context_dim is not None and exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if (
+                        not exists(num_attention_blocks)
+                        or nr < num_attention_blocks[level]
+                    ):
+                        layers.append(
+                            SpatialTransformer(
+                                ch,
+                                num_heads,
+                                dim_head,
+                                depth=transformer_depth[level],
+                                context_dim=context_dim,
+                                disable_self_attn=disabled_sa,
+                                use_linear=use_linear_in_transformer,
+                                attn_type=spatial_transformer_attn_type,
+                                use_checkpoint=use_checkpoint,
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                out_channels=ch,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            SpatialTransformer(
+                ch,
+                num_heads,
+                dim_head,
+                depth=transformer_depth_middle,
+                context_dim=context_dim,
+                disable_self_attn=disable_middle_self_attn,
+                use_linear=use_linear_in_transformer,
+                attn_type=spatial_transformer_attn_type,
+                use_checkpoint=use_checkpoint,
+            )
+            if not disable_middle_transformer
+            else th.nn.Identity(),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(self.num_res_blocks[level] + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if (
+                        not exists(num_attention_blocks)
+                        or i < num_attention_blocks[level]
+                    ):
+                        layers.append(
+                            SpatialTransformer(
+                                ch,
+                                num_heads,
+                                dim_head,
+                                depth=transformer_depth[level],
+                                context_dim=context_dim,
+                                disable_self_attn=disabled_sa,
+                                use_linear=use_linear_in_transformer,
+                                attn_type=spatial_transformer_attn_type,
+                                use_checkpoint=use_checkpoint,
+                            )
+                        )
+                if level and i == self.num_res_blocks[level]:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+
+    def forward(
+        self,
+        x: th.Tensor,
+        timesteps: Optional[th.Tensor] = None,
+        context: Optional[th.Tensor] = None,
+        y: Optional[th.Tensor] = None,
+        **kwargs,
+    ) -> th.Tensor:
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.num_classes is not None:
+            assert y.shape[0] == x.shape[0]
+            emb = emb + self.label_emb(y)
+
+        h = x
+        for module in self.input_blocks:
+            h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+        h = h.type(x.dtype)
+
+        return self.out(h)
diff --git a/sgm/modules/diffusionmodules/sampling.py b/sgm/modules/diffusionmodules/sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..af07566d599fdd6f255f8b9fd4592a962b0d2ace
--- /dev/null
+++ b/sgm/modules/diffusionmodules/sampling.py
@@ -0,0 +1,362 @@
+"""
+    Partially ported from https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py
+"""
+
+
+from typing import Dict, Union
+
+import torch
+from omegaconf import ListConfig, OmegaConf
+from tqdm import tqdm
+
+from ...modules.diffusionmodules.sampling_utils import (get_ancestral_step,
+                                                        linear_multistep_coeff,
+                                                        to_d, to_neg_log_sigma,
+                                                        to_sigma)
+from ...util import append_dims, default, instantiate_from_config
+
+DEFAULT_GUIDER = {"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"}
+
+
+class BaseDiffusionSampler:
+    def __init__(
+        self,
+        discretization_config: Union[Dict, ListConfig, OmegaConf],
+        num_steps: Union[int, None] = None,
+        guider_config: Union[Dict, ListConfig, OmegaConf, None] = None,
+        verbose: bool = False,
+        device: str = "cuda",
+    ):
+        self.num_steps = num_steps
+        self.discretization = instantiate_from_config(discretization_config)
+        self.guider = instantiate_from_config(
+            default(
+                guider_config,
+                DEFAULT_GUIDER,
+            )
+        )
+        self.verbose = verbose
+        self.device = device
+
+    def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None):
+        sigmas = self.discretization(
+            self.num_steps if num_steps is None else num_steps, device=self.device
+        )
+        uc = default(uc, cond)
+
+        x *= torch.sqrt(1.0 + sigmas[0] ** 2.0)
+        num_sigmas = len(sigmas)
+
+        s_in = x.new_ones([x.shape[0]])
+
+        return x, s_in, sigmas, num_sigmas, cond, uc
+
+    def denoise(self, x, denoiser, sigma, cond, uc):
+        denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc))
+        denoised = self.guider(denoised, sigma)
+        return denoised
+
+    def get_sigma_gen(self, num_sigmas):
+        sigma_generator = range(num_sigmas - 1)
+        if self.verbose:
+            print("#" * 30, " Sampling setting ", "#" * 30)
+            print(f"Sampler: {self.__class__.__name__}")
+            print(f"Discretization: {self.discretization.__class__.__name__}")
+            print(f"Guider: {self.guider.__class__.__name__}")
+            sigma_generator = tqdm(
+                sigma_generator,
+                total=num_sigmas,
+                desc=f"Sampling with {self.__class__.__name__} for {num_sigmas} steps",
+            )
+        return sigma_generator
+
+
+class SingleStepDiffusionSampler(BaseDiffusionSampler):
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc, *args, **kwargs):
+        raise NotImplementedError
+
+    def euler_step(self, x, d, dt):
+        return x + dt * d
+
+
+class EDMSampler(SingleStepDiffusionSampler):
+    def __init__(
+        self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.s_churn = s_churn
+        self.s_tmin = s_tmin
+        self.s_tmax = s_tmax
+        self.s_noise = s_noise
+
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0):
+        sigma_hat = sigma * (gamma + 1.0)
+        if gamma > 0:
+            eps = torch.randn_like(x) * self.s_noise
+            x = x + eps * append_dims(sigma_hat**2 - sigma**2, x.ndim) ** 0.5
+
+        denoised = self.denoise(x, denoiser, sigma_hat, cond, uc)
+        d = to_d(x, sigma_hat, denoised)
+        dt = append_dims(next_sigma - sigma_hat, x.ndim)
+
+        euler_step = self.euler_step(x, d, dt)
+        x = self.possible_correction_step(
+            euler_step, x, d, dt, next_sigma, denoiser, cond, uc
+        )
+        return x
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        for i in self.get_sigma_gen(num_sigmas):
+            gamma = (
+                min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1)
+                if self.s_tmin <= sigmas[i] <= self.s_tmax
+                else 0.0
+            )
+            x = self.sampler_step(
+                s_in * sigmas[i],
+                s_in * sigmas[i + 1],
+                denoiser,
+                x,
+                cond,
+                uc,
+                gamma,
+            )
+
+        return x
+
+
+class AncestralSampler(SingleStepDiffusionSampler):
+    def __init__(self, eta=1.0, s_noise=1.0, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.eta = eta
+        self.s_noise = s_noise
+        self.noise_sampler = lambda x: torch.randn_like(x)
+
+    def ancestral_euler_step(self, x, denoised, sigma, sigma_down):
+        d = to_d(x, sigma, denoised)
+        dt = append_dims(sigma_down - sigma, x.ndim)
+
+        return self.euler_step(x, d, dt)
+
+    def ancestral_step(self, x, sigma, next_sigma, sigma_up):
+        x = torch.where(
+            append_dims(next_sigma, x.ndim) > 0.0,
+            x + self.noise_sampler(x) * self.s_noise * append_dims(sigma_up, x.ndim),
+            x,
+        )
+        return x
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        for i in self.get_sigma_gen(num_sigmas):
+            x = self.sampler_step(
+                s_in * sigmas[i],
+                s_in * sigmas[i + 1],
+                denoiser,
+                x,
+                cond,
+                uc,
+            )
+
+        return x
+
+
+class LinearMultistepSampler(BaseDiffusionSampler):
+    def __init__(
+        self,
+        order=4,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.order = order
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        ds = []
+        sigmas_cpu = sigmas.detach().cpu().numpy()
+        for i in self.get_sigma_gen(num_sigmas):
+            sigma = s_in * sigmas[i]
+            denoised = denoiser(
+                *self.guider.prepare_inputs(x, sigma, cond, uc), **kwargs
+            )
+            denoised = self.guider(denoised, sigma)
+            d = to_d(x, sigma, denoised)
+            ds.append(d)
+            if len(ds) > self.order:
+                ds.pop(0)
+            cur_order = min(i + 1, self.order)
+            coeffs = [
+                linear_multistep_coeff(cur_order, sigmas_cpu, i, j)
+                for j in range(cur_order)
+            ]
+            x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
+
+        return x
+
+
+class EulerEDMSampler(EDMSampler):
+    def possible_correction_step(
+        self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
+    ):
+        return euler_step
+
+
+class HeunEDMSampler(EDMSampler):
+    def possible_correction_step(
+        self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
+    ):
+        if torch.sum(next_sigma) < 1e-14:
+            # Save a network evaluation if all noise levels are 0
+            return euler_step
+        else:
+            denoised = self.denoise(euler_step, denoiser, next_sigma, cond, uc)
+            d_new = to_d(euler_step, next_sigma, denoised)
+            d_prime = (d + d_new) / 2.0
+
+            # apply correction if noise level is not 0
+            x = torch.where(
+                append_dims(next_sigma, x.ndim) > 0.0, x + d_prime * dt, euler_step
+            )
+            return x
+
+
+class EulerAncestralSampler(AncestralSampler):
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc):
+        sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
+        denoised = self.denoise(x, denoiser, sigma, cond, uc)
+        x = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
+        x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
+
+        return x
+
+
+class DPMPP2SAncestralSampler(AncestralSampler):
+    def get_variables(self, sigma, sigma_down):
+        t, t_next = [to_neg_log_sigma(s) for s in (sigma, sigma_down)]
+        h = t_next - t
+        s = t + 0.5 * h
+        return h, s, t, t_next
+
+    def get_mult(self, h, s, t, t_next):
+        mult1 = to_sigma(s) / to_sigma(t)
+        mult2 = (-0.5 * h).expm1()
+        mult3 = to_sigma(t_next) / to_sigma(t)
+        mult4 = (-h).expm1()
+
+        return mult1, mult2, mult3, mult4
+
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, **kwargs):
+        sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
+        denoised = self.denoise(x, denoiser, sigma, cond, uc)
+        x_euler = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
+
+        if torch.sum(sigma_down) < 1e-14:
+            # Save a network evaluation if all noise levels are 0
+            x = x_euler
+        else:
+            h, s, t, t_next = self.get_variables(sigma, sigma_down)
+            mult = [
+                append_dims(mult, x.ndim) for mult in self.get_mult(h, s, t, t_next)
+            ]
+
+            x2 = mult[0] * x - mult[1] * denoised
+            denoised2 = self.denoise(x2, denoiser, to_sigma(s), cond, uc)
+            x_dpmpp2s = mult[2] * x - mult[3] * denoised2
+
+            # apply correction if noise level is not 0
+            x = torch.where(append_dims(sigma_down, x.ndim) > 0.0, x_dpmpp2s, x_euler)
+
+        x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
+        return x
+
+
+class DPMPP2MSampler(BaseDiffusionSampler):
+    def get_variables(self, sigma, next_sigma, previous_sigma=None):
+        t, t_next = [to_neg_log_sigma(s) for s in (sigma, next_sigma)]
+        h = t_next - t
+
+        if previous_sigma is not None:
+            h_last = t - to_neg_log_sigma(previous_sigma)
+            r = h_last / h
+            return h, r, t, t_next
+        else:
+            return h, None, t, t_next
+
+    def get_mult(self, h, r, t, t_next, previous_sigma):
+        mult1 = to_sigma(t_next) / to_sigma(t)
+        mult2 = (-h).expm1()
+
+        if previous_sigma is not None:
+            mult3 = 1 + 1 / (2 * r)
+            mult4 = 1 / (2 * r)
+            return mult1, mult2, mult3, mult4
+        else:
+            return mult1, mult2
+
+    def sampler_step(
+        self,
+        old_denoised,
+        previous_sigma,
+        sigma,
+        next_sigma,
+        denoiser,
+        x,
+        cond,
+        uc=None,
+    ):
+        denoised = self.denoise(x, denoiser, sigma, cond, uc)
+
+        h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma)
+        mult = [
+            append_dims(mult, x.ndim)
+            for mult in self.get_mult(h, r, t, t_next, previous_sigma)
+        ]
+
+        x_standard = mult[0] * x - mult[1] * denoised
+        if old_denoised is None or torch.sum(next_sigma) < 1e-14:
+            # Save a network evaluation if all noise levels are 0 or on the first step
+            return x_standard, denoised
+        else:
+            denoised_d = mult[2] * denoised - mult[3] * old_denoised
+            x_advanced = mult[0] * x - mult[1] * denoised_d
+
+            # apply correction if noise level is not 0 and not first step
+            x = torch.where(
+                append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard
+            )
+
+        return x, denoised
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        old_denoised = None
+        for i in self.get_sigma_gen(num_sigmas):
+            x, old_denoised = self.sampler_step(
+                old_denoised,
+                None if i == 0 else s_in * sigmas[i - 1],
+                s_in * sigmas[i],
+                s_in * sigmas[i + 1],
+                denoiser,
+                x,
+                cond,
+                uc=uc,
+            )
+
+        return x
diff --git a/sgm/modules/diffusionmodules/sampling_utils.py b/sgm/modules/diffusionmodules/sampling_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce78527ea9052a8bfd0856ed2278901516fb9130
--- /dev/null
+++ b/sgm/modules/diffusionmodules/sampling_utils.py
@@ -0,0 +1,43 @@
+import torch
+from scipy import integrate
+
+from ...util import append_dims
+
+
+def linear_multistep_coeff(order, t, i, j, epsrel=1e-4):
+    if order - 1 > i:
+        raise ValueError(f"Order {order} too high for step {i}")
+
+    def fn(tau):
+        prod = 1.0
+        for k in range(order):
+            if j == k:
+                continue
+            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
+        return prod
+
+    return integrate.quad(fn, t[i], t[i + 1], epsrel=epsrel)[0]
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.0):
+    if not eta:
+        return sigma_to, 0.0
+    sigma_up = torch.minimum(
+        sigma_to,
+        eta
+        * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5,
+    )
+    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
+    return sigma_down, sigma_up
+
+
+def to_d(x, sigma, denoised):
+    return (x - denoised) / append_dims(sigma, x.ndim)
+
+
+def to_neg_log_sigma(sigma):
+    return sigma.log().neg()
+
+
+def to_sigma(neg_log_sigma):
+    return neg_log_sigma.neg().exp()
diff --git a/sgm/modules/diffusionmodules/sigma_sampling.py b/sgm/modules/diffusionmodules/sigma_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..d54724c6ef6a7b8067784a4192b0fe2f41123063
--- /dev/null
+++ b/sgm/modules/diffusionmodules/sigma_sampling.py
@@ -0,0 +1,31 @@
+import torch
+
+from ...util import default, instantiate_from_config
+
+
+class EDMSampling:
+    def __init__(self, p_mean=-1.2, p_std=1.2):
+        self.p_mean = p_mean
+        self.p_std = p_std
+
+    def __call__(self, n_samples, rand=None):
+        log_sigma = self.p_mean + self.p_std * default(rand, torch.randn((n_samples,)))
+        return log_sigma.exp()
+
+
+class DiscreteSampling:
+    def __init__(self, discretization_config, num_idx, do_append_zero=False, flip=True):
+        self.num_idx = num_idx
+        self.sigmas = instantiate_from_config(discretization_config)(
+            num_idx, do_append_zero=do_append_zero, flip=flip
+        )
+
+    def idx_to_sigma(self, idx):
+        return self.sigmas[idx]
+
+    def __call__(self, n_samples, rand=None):
+        idx = default(
+            rand,
+            torch.randint(0, self.num_idx, (n_samples,)),
+        )
+        return self.idx_to_sigma(idx)
diff --git a/sgm/modules/diffusionmodules/util.py b/sgm/modules/diffusionmodules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..389f0e449367b1b628d61dca105343d066dbefff
--- /dev/null
+++ b/sgm/modules/diffusionmodules/util.py
@@ -0,0 +1,369 @@
+"""
+partially adopted from
+https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+and
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+and
+https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+
+thanks!
+"""
+
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+
+def make_beta_schedule(
+    schedule,
+    n_timestep,
+    linear_start=1e-4,
+    linear_end=2e-2,
+):
+    if schedule == "linear":
+        betas = (
+            torch.linspace(
+                linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
+            )
+            ** 2
+        )
+    return betas.numpy()
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def mixed_checkpoint(func, inputs: dict, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass. This differs from the original checkpoint function
+    borrowed from https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py in that
+    it also works with non-tensor inputs
+    :param func: the function to evaluate.
+    :param inputs: the argument dictionary to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        tensor_keys = [key for key in inputs if isinstance(inputs[key], torch.Tensor)]
+        tensor_inputs = [
+            inputs[key] for key in inputs if isinstance(inputs[key], torch.Tensor)
+        ]
+        non_tensor_keys = [
+            key for key in inputs if not isinstance(inputs[key], torch.Tensor)
+        ]
+        non_tensor_inputs = [
+            inputs[key] for key in inputs if not isinstance(inputs[key], torch.Tensor)
+        ]
+        args = tuple(tensor_inputs) + tuple(non_tensor_inputs) + tuple(params)
+        return MixedCheckpointFunction.apply(
+            func,
+            len(tensor_inputs),
+            len(non_tensor_inputs),
+            tensor_keys,
+            non_tensor_keys,
+            *args,
+        )
+    else:
+        return func(**inputs)
+
+
+class MixedCheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        run_function,
+        length_tensors,
+        length_non_tensors,
+        tensor_keys,
+        non_tensor_keys,
+        *args,
+    ):
+        ctx.end_tensors = length_tensors
+        ctx.end_non_tensors = length_tensors + length_non_tensors
+        ctx.gpu_autocast_kwargs = {
+            "enabled": torch.is_autocast_enabled(),
+            "dtype": torch.get_autocast_gpu_dtype(),
+            "cache_enabled": torch.is_autocast_cache_enabled(),
+        }
+        assert (
+            len(tensor_keys) == length_tensors
+            and len(non_tensor_keys) == length_non_tensors
+        )
+
+        ctx.input_tensors = {
+            key: val for (key, val) in zip(tensor_keys, list(args[: ctx.end_tensors]))
+        }
+        ctx.input_non_tensors = {
+            key: val
+            for (key, val) in zip(
+                non_tensor_keys, list(args[ctx.end_tensors : ctx.end_non_tensors])
+            )
+        }
+        ctx.run_function = run_function
+        ctx.input_params = list(args[ctx.end_non_tensors :])
+
+        with torch.no_grad():
+            output_tensors = ctx.run_function(
+                **ctx.input_tensors, **ctx.input_non_tensors
+            )
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        # additional_args = {key: ctx.input_tensors[key] for key in ctx.input_tensors if not isinstance(ctx.input_tensors[key],torch.Tensor)}
+        ctx.input_tensors = {
+            key: ctx.input_tensors[key].detach().requires_grad_(True)
+            for key in ctx.input_tensors
+        }
+
+        with torch.enable_grad(), torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = {
+                key: ctx.input_tensors[key].view_as(ctx.input_tensors[key])
+                for key in ctx.input_tensors
+            }
+            # shallow_copies.update(additional_args)
+            output_tensors = ctx.run_function(**shallow_copies, **ctx.input_non_tensors)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            list(ctx.input_tensors.values()) + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (
+            (None, None, None, None, None)
+            + input_grads[: ctx.end_tensors]
+            + (None,) * (ctx.end_non_tensors - ctx.end_tensors)
+            + input_grads[ctx.end_tensors :]
+        )
+
+
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        args = tuple(inputs) + tuple(params)
+        return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, length, *args):
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+        ctx.gpu_autocast_kwargs = {
+            "enabled": torch.is_autocast_enabled(),
+            "dtype": torch.get_autocast_gpu_dtype(),
+            "cache_enabled": torch.is_autocast_cache_enabled(),
+        }
+        with torch.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with torch.enable_grad(), torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+    else:
+        embedding = repeat(timesteps, "b -> b d", d=dim)
+    return embedding
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class AlphaBlender(nn.Module):
+    strategies = ["learned", "fixed", "learned_with_images"]
+
+    def __init__(
+        self,
+        alpha: float,
+        merge_strategy: str = "learned_with_images",
+        rearrange_pattern: str = "b t -> (b t) 1 1",
+    ):
+        super().__init__()
+        self.merge_strategy = merge_strategy
+        self.rearrange_pattern = rearrange_pattern
+
+        assert (
+            merge_strategy in self.strategies
+        ), f"merge_strategy needs to be in {self.strategies}"
+
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif (
+            self.merge_strategy == "learned"
+            or self.merge_strategy == "learned_with_images"
+        ):
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor:
+        if self.merge_strategy == "fixed":
+            alpha = self.mix_factor
+        elif self.merge_strategy == "learned":
+            alpha = torch.sigmoid(self.mix_factor)
+        elif self.merge_strategy == "learned_with_images":
+            assert image_only_indicator is not None, "need image_only_indicator ..."
+            alpha = torch.where(
+                image_only_indicator.bool(),
+                torch.ones(1, 1, device=image_only_indicator.device),
+                rearrange(torch.sigmoid(self.mix_factor), "... -> ... 1"),
+            )
+            alpha = rearrange(alpha, self.rearrange_pattern)
+        else:
+            raise NotImplementedError
+        return alpha
+
+    def forward(
+        self,
+        x_spatial: torch.Tensor,
+        x_temporal: torch.Tensor,
+        image_only_indicator: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        alpha = self.get_alpha(image_only_indicator)
+        x = (
+            alpha.to(x_spatial.dtype) * x_spatial
+            + (1.0 - alpha).to(x_spatial.dtype) * x_temporal
+        )
+        return x
diff --git a/sgm/modules/diffusionmodules/video_model.py b/sgm/modules/diffusionmodules/video_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff2d077c7d0c7ed1c4a2c21f14105c266abc4926
--- /dev/null
+++ b/sgm/modules/diffusionmodules/video_model.py
@@ -0,0 +1,493 @@
+from functools import partial
+from typing import List, Optional, Union
+
+from einops import rearrange
+
+from ...modules.diffusionmodules.openaimodel import *
+from ...modules.video_attention import SpatialVideoTransformer
+from ...util import default
+from .util import AlphaBlender
+
+
+class VideoResBlock(ResBlock):
+    def __init__(
+        self,
+        channels: int,
+        emb_channels: int,
+        dropout: float,
+        video_kernel_size: Union[int, List[int]] = 3,
+        merge_strategy: str = "fixed",
+        merge_factor: float = 0.5,
+        out_channels: Optional[int] = None,
+        use_conv: bool = False,
+        use_scale_shift_norm: bool = False,
+        dims: int = 2,
+        use_checkpoint: bool = False,
+        up: bool = False,
+        down: bool = False,
+    ):
+        super().__init__(
+            channels,
+            emb_channels,
+            dropout,
+            out_channels=out_channels,
+            use_conv=use_conv,
+            use_scale_shift_norm=use_scale_shift_norm,
+            dims=dims,
+            use_checkpoint=use_checkpoint,
+            up=up,
+            down=down,
+        )
+
+        self.time_stack = ResBlock(
+            default(out_channels, channels),
+            emb_channels,
+            dropout=dropout,
+            dims=3,
+            out_channels=default(out_channels, channels),
+            use_scale_shift_norm=False,
+            use_conv=False,
+            up=False,
+            down=False,
+            kernel_size=video_kernel_size,
+            use_checkpoint=use_checkpoint,
+            exchange_temb_dims=True,
+        )
+        self.time_mixer = AlphaBlender(
+            alpha=merge_factor,
+            merge_strategy=merge_strategy,
+            rearrange_pattern="b t -> b 1 t 1 1",
+        )
+
+    def forward(
+        self,
+        x: th.Tensor,
+        emb: th.Tensor,
+        num_video_frames: int,
+        image_only_indicator: Optional[th.Tensor] = None,
+    ) -> th.Tensor:
+        x = super().forward(x, emb)
+
+        x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
+        x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
+
+        x = self.time_stack(
+            x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames)
+        )
+        x = self.time_mixer(
+            x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator
+        )
+        x = rearrange(x, "b c t h w -> (b t) c h w")
+        return x
+
+
+class VideoUNet(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        model_channels: int,
+        out_channels: int,
+        num_res_blocks: int,
+        attention_resolutions: int,
+        dropout: float = 0.0,
+        channel_mult: List[int] = (1, 2, 4, 8),
+        conv_resample: bool = True,
+        dims: int = 2,
+        num_classes: Optional[int] = None,
+        use_checkpoint: bool = False,
+        num_heads: int = -1,
+        num_head_channels: int = -1,
+        num_heads_upsample: int = -1,
+        use_scale_shift_norm: bool = False,
+        resblock_updown: bool = False,
+        transformer_depth: Union[List[int], int] = 1,
+        transformer_depth_middle: Optional[int] = None,
+        context_dim: Optional[int] = None,
+        time_downup: bool = False,
+        time_context_dim: Optional[int] = None,
+        extra_ff_mix_layer: bool = False,
+        use_spatial_context: bool = False,
+        merge_strategy: str = "fixed",
+        merge_factor: float = 0.5,
+        spatial_transformer_attn_type: str = "softmax",
+        video_kernel_size: Union[int, List[int]] = 3,
+        use_linear_in_transformer: bool = False,
+        adm_in_channels: Optional[int] = None,
+        disable_temporal_crossattention: bool = False,
+        max_ddpm_temb_period: int = 10000,
+    ):
+        super().__init__()
+        assert context_dim is not None
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1
+
+        if num_head_channels == -1:
+            assert num_heads != -1
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        if isinstance(transformer_depth, int):
+            transformer_depth = len(channel_mult) * [transformer_depth]
+        transformer_depth_middle = default(
+            transformer_depth_middle, transformer_depth[-1]
+        )
+
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+            elif self.num_classes == "continuous":
+                print("setting up linear c_adm embedding layer")
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            elif self.num_classes == "timestep":
+                self.label_emb = nn.Sequential(
+                    Timestep(model_channels),
+                    nn.Sequential(
+                        linear(model_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    ),
+                )
+
+            elif self.num_classes == "sequential":
+                assert adm_in_channels is not None
+                self.label_emb = nn.Sequential(
+                    nn.Sequential(
+                        linear(adm_in_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    )
+                )
+            else:
+                raise ValueError()
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+
+        def get_attention_layer(
+            ch,
+            num_heads,
+            dim_head,
+            depth=1,
+            context_dim=None,
+            use_checkpoint=False,
+            disabled_sa=False,
+        ):
+            return SpatialVideoTransformer(
+                ch,
+                num_heads,
+                dim_head,
+                depth=depth,
+                context_dim=context_dim,
+                time_context_dim=time_context_dim,
+                dropout=dropout,
+                ff_in=extra_ff_mix_layer,
+                use_spatial_context=use_spatial_context,
+                merge_strategy=merge_strategy,
+                merge_factor=merge_factor,
+                checkpoint=use_checkpoint,
+                use_linear=use_linear_in_transformer,
+                attn_mode=spatial_transformer_attn_type,
+                disable_self_attn=disabled_sa,
+                disable_temporal_crossattention=disable_temporal_crossattention,
+                max_time_embed_period=max_ddpm_temb_period,
+            )
+
+        def get_resblock(
+            merge_factor,
+            merge_strategy,
+            video_kernel_size,
+            ch,
+            time_embed_dim,
+            dropout,
+            out_ch,
+            dims,
+            use_checkpoint,
+            use_scale_shift_norm,
+            down=False,
+            up=False,
+        ):
+            return VideoResBlock(
+                merge_factor=merge_factor,
+                merge_strategy=merge_strategy,
+                video_kernel_size=video_kernel_size,
+                channels=ch,
+                emb_channels=time_embed_dim,
+                dropout=dropout,
+                out_channels=out_ch,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+                down=down,
+                up=up,
+            )
+
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    get_resblock(
+                        merge_factor=merge_factor,
+                        merge_strategy=merge_strategy,
+                        video_kernel_size=video_kernel_size,
+                        ch=ch,
+                        time_embed_dim=time_embed_dim,
+                        dropout=dropout,
+                        out_ch=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+
+                    layers.append(
+                        get_attention_layer(
+                            ch,
+                            num_heads,
+                            dim_head,
+                            depth=transformer_depth[level],
+                            context_dim=context_dim,
+                            use_checkpoint=use_checkpoint,
+                            disabled_sa=False,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                ds *= 2
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        get_resblock(
+                            merge_factor=merge_factor,
+                            merge_strategy=merge_strategy,
+                            video_kernel_size=video_kernel_size,
+                            ch=ch,
+                            time_embed_dim=time_embed_dim,
+                            dropout=dropout,
+                            out_ch=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch,
+                            conv_resample,
+                            dims=dims,
+                            out_channels=out_ch,
+                            third_down=time_downup,
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+
+        self.middle_block = TimestepEmbedSequential(
+            get_resblock(
+                merge_factor=merge_factor,
+                merge_strategy=merge_strategy,
+                video_kernel_size=video_kernel_size,
+                ch=ch,
+                time_embed_dim=time_embed_dim,
+                out_ch=None,
+                dropout=dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            get_attention_layer(
+                ch,
+                num_heads,
+                dim_head,
+                depth=transformer_depth_middle,
+                context_dim=context_dim,
+                use_checkpoint=use_checkpoint,
+            ),
+            get_resblock(
+                merge_factor=merge_factor,
+                merge_strategy=merge_strategy,
+                video_kernel_size=video_kernel_size,
+                ch=ch,
+                out_ch=None,
+                time_embed_dim=time_embed_dim,
+                dropout=dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    get_resblock(
+                        merge_factor=merge_factor,
+                        merge_strategy=merge_strategy,
+                        video_kernel_size=video_kernel_size,
+                        ch=ch + ich,
+                        time_embed_dim=time_embed_dim,
+                        dropout=dropout,
+                        out_ch=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+
+                    layers.append(
+                        get_attention_layer(
+                            ch,
+                            num_heads,
+                            dim_head,
+                            depth=transformer_depth[level],
+                            context_dim=context_dim,
+                            use_checkpoint=use_checkpoint,
+                            disabled_sa=False,
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    ds //= 2
+                    layers.append(
+                        get_resblock(
+                            merge_factor=merge_factor,
+                            merge_strategy=merge_strategy,
+                            video_kernel_size=video_kernel_size,
+                            ch=ch,
+                            time_embed_dim=time_embed_dim,
+                            dropout=dropout,
+                            out_ch=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(
+                            ch,
+                            conv_resample,
+                            dims=dims,
+                            out_channels=out_ch,
+                            third_up=time_downup,
+                        )
+                    )
+
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+
+    def forward(
+        self,
+        x: th.Tensor,
+        timesteps: th.Tensor,
+        context: Optional[th.Tensor] = None,
+        y: Optional[th.Tensor] = None,
+        time_context: Optional[th.Tensor] = None,
+        num_video_frames: Optional[int] = None,
+        image_only_indicator: Optional[th.Tensor] = None,
+    ):
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional -> no, relax this TODO"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.num_classes is not None:
+            assert y.shape[0] == x.shape[0]
+            emb = emb + self.label_emb(y)
+
+        h = x
+        for module in self.input_blocks:
+            h = module(
+                h,
+                emb,
+                context=context,
+                image_only_indicator=image_only_indicator,
+                time_context=time_context,
+                num_video_frames=num_video_frames,
+            )
+            hs.append(h)
+        h = self.middle_block(
+            h,
+            emb,
+            context=context,
+            image_only_indicator=image_only_indicator,
+            time_context=time_context,
+            num_video_frames=num_video_frames,
+        )
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(
+                h,
+                emb,
+                context=context,
+                image_only_indicator=image_only_indicator,
+                time_context=time_context,
+                num_video_frames=num_video_frames,
+            )
+        h = h.type(x.dtype)
+        return self.out(h)
diff --git a/sgm/modules/diffusionmodules/wrappers.py b/sgm/modules/diffusionmodules/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..37449ea63e992b9f89856f1f47c18ba68be8e334
--- /dev/null
+++ b/sgm/modules/diffusionmodules/wrappers.py
@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+from packaging import version
+
+OPENAIUNETWRAPPER = "sgm.modules.diffusionmodules.wrappers.OpenAIWrapper"
+
+
+class IdentityWrapper(nn.Module):
+    def __init__(self, diffusion_model, compile_model: bool = False):
+        super().__init__()
+        compile = (
+            torch.compile
+            if (version.parse(torch.__version__) >= version.parse("2.0.0"))
+            and compile_model
+            else lambda x: x
+        )
+        self.diffusion_model = compile(diffusion_model)
+
+    def forward(self, *args, **kwargs):
+        return self.diffusion_model(*args, **kwargs)
+
+
+class OpenAIWrapper(IdentityWrapper):
+    def forward(
+        self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
+    ) -> torch.Tensor:
+        x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=1)
+        return self.diffusion_model(
+            x,
+            timesteps=t,
+            context=c.get("crossattn", None),
+            y=c.get("vector", None),
+            **kwargs,
+        )
diff --git a/sgm/modules/distributions/__init__.py b/sgm/modules/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/modules/distributions/__pycache__/__init__.cpython-39.pyc b/sgm/modules/distributions/__pycache__/__init__.cpython-39.pyc
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diff --git a/sgm/modules/distributions/__pycache__/distributions.cpython-39.pyc b/sgm/modules/distributions/__pycache__/distributions.cpython-39.pyc
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diff --git a/sgm/modules/distributions/distributions.py b/sgm/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..016be35523187ea366db9ade391fe8ee276db60b
--- /dev/null
+++ b/sgm/modules/distributions/distributions.py
@@ -0,0 +1,102 @@
+import numpy as np
+import torch
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(
+                device=self.parameters.device
+            )
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(
+            device=self.parameters.device
+        )
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2, 3],
+                )
+
+    def nll(self, sample, dims=[1, 2, 3]):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )
diff --git a/sgm/modules/ema.py b/sgm/modules/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..97b5ae2b230f89b4dba57e44c4f851478ad86f68
--- /dev/null
+++ b/sgm/modules/ema.py
@@ -0,0 +1,86 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError("Decay must be between 0 and 1")
+
+        self.m_name2s_name = {}
+        self.register_buffer("decay", torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer(
+            "num_updates",
+            torch.tensor(0, dtype=torch.int)
+            if use_num_upates
+            else torch.tensor(-1, dtype=torch.int),
+        )
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                # remove as '.'-character is not allowed in buffers
+                s_name = name.replace(".", "")
+                self.m_name2s_name.update({name: s_name})
+                self.register_buffer(s_name, p.clone().detach().data)
+
+        self.collected_params = []
+
+    def reset_num_updates(self):
+        del self.num_updates
+        self.register_buffer("num_updates", torch.tensor(0, dtype=torch.int))
+
+    def forward(self, model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(
+                        one_minus_decay * (shadow_params[sname] - m_param[key])
+                    )
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)
diff --git a/sgm/modules/encoders/__init__.py b/sgm/modules/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/modules/encoders/__pycache__/__init__.cpython-39.pyc b/sgm/modules/encoders/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0d3fb8f72123d0c57b6ce7bdec76325d664ab315
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diff --git a/sgm/modules/encoders/__pycache__/modules.cpython-39.pyc b/sgm/modules/encoders/__pycache__/modules.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..172ce515f60549fa5e861bb0d29d6d979a306d66
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diff --git a/sgm/modules/encoders/modules.py b/sgm/modules/encoders/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cca880453697bc9a1f00090831b81d9baacd103
--- /dev/null
+++ b/sgm/modules/encoders/modules.py
@@ -0,0 +1,1787 @@
+import math
+import random
+import kiui
+from kiui.op import recenter
+import torchvision
+import torchvision.transforms.v2
+from contextlib import nullcontext
+from functools import partial
+from typing import Dict, List, Optional, Tuple, Union
+from pdb import set_trace as st
+
+import kornia
+import numpy as np
+import open_clip
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+from omegaconf import ListConfig
+from torch.utils.checkpoint import checkpoint
+from transformers import (ByT5Tokenizer, CLIPTextModel, CLIPTokenizer,
+                          T5EncoderModel, T5Tokenizer)
+
+from ...modules.autoencoding.regularizers import DiagonalGaussianRegularizer
+from ...modules.diffusionmodules.model import Encoder
+from ...modules.diffusionmodules.openaimodel import Timestep
+from ...modules.diffusionmodules.util import (extract_into_tensor,
+                                              make_beta_schedule)
+from ...modules.distributions.distributions import DiagonalGaussianDistribution
+from ...util import (append_dims, autocast, count_params, default,
+                     disabled_train, expand_dims_like, instantiate_from_config)
+
+from dit.dit_models_xformers import CaptionEmbedder, approx_gelu, t2i_modulate
+
+
+class AbstractEmbModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self._is_trainable = None
+        self._ucg_rate = None
+        self._input_key = None
+
+    @property
+    def is_trainable(self) -> bool:
+        return self._is_trainable
+
+    @property
+    def ucg_rate(self) -> Union[float, torch.Tensor]:
+        return self._ucg_rate
+
+    @property
+    def input_key(self) -> str:
+        return self._input_key
+
+    @is_trainable.setter
+    def is_trainable(self, value: bool):
+        self._is_trainable = value
+
+    @ucg_rate.setter
+    def ucg_rate(self, value: Union[float, torch.Tensor]):
+        self._ucg_rate = value
+
+    @input_key.setter
+    def input_key(self, value: str):
+        self._input_key = value
+
+    @is_trainable.deleter
+    def is_trainable(self):
+        del self._is_trainable
+
+    @ucg_rate.deleter
+    def ucg_rate(self):
+        del self._ucg_rate
+
+    @input_key.deleter
+    def input_key(self):
+        del self._input_key
+
+
+class GeneralConditioner(nn.Module):
+    OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"}
+    KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1}
+
+    def __init__(self, emb_models: Union[List, ListConfig]):
+        super().__init__()
+        embedders = []
+        for n, embconfig in enumerate(emb_models):
+            embedder = instantiate_from_config(embconfig)
+            assert isinstance(
+                embedder, AbstractEmbModel
+            ), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel"
+            embedder.is_trainable = embconfig.get("is_trainable", False)
+            embedder.ucg_rate = embconfig.get("ucg_rate", 0.0)
+            if not embedder.is_trainable:
+                embedder.train = disabled_train
+                for param in embedder.parameters():
+                    param.requires_grad = False
+                embedder.eval()
+            print(
+                f"Initialized embedder #{n}: {embedder.__class__.__name__} "
+                f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
+            )
+
+            if "input_key" in embconfig:
+                embedder.input_key = embconfig["input_key"]
+            elif "input_keys" in embconfig:
+                embedder.input_keys = embconfig["input_keys"]
+            else:
+                raise KeyError(
+                    f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}"
+                )
+
+            embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None)
+            if embedder.legacy_ucg_val is not None:
+                embedder.ucg_prng = np.random.RandomState()
+
+            embedders.append(embedder)
+        self.embedders = nn.ModuleList(embedders)
+
+    def possibly_get_ucg_val(self, embedder: AbstractEmbModel,
+                             batch: Dict) -> Dict:
+        assert embedder.legacy_ucg_val is not None
+        p = embedder.ucg_rate
+        val = embedder.legacy_ucg_val
+        for i in range(len(batch[embedder.input_key])):
+            if embedder.ucg_prng.choice(2, p=[1 - p, p]):
+                batch[embedder.input_key][i] = val
+        return batch
+
+    def forward(self,
+                batch: Dict,
+                force_zero_embeddings: Optional[List] = None) -> Dict:
+        output = dict()
+        if force_zero_embeddings is None:
+            force_zero_embeddings = []
+        for embedder in self.embedders:
+            embedding_context = nullcontext if embedder.is_trainable else torch.no_grad
+            with embedding_context():
+                if hasattr(embedder, "input_key") and (embedder.input_key
+                                                       is not None):
+                    if embedder.legacy_ucg_val is not None:
+                        batch = self.possibly_get_ucg_val(embedder, batch)
+                    emb_out = embedder(batch[embedder.input_key])
+                elif hasattr(embedder, "input_keys"):
+                    emb_out = embedder(
+                        *[batch[k] for k in embedder.input_keys])
+            assert isinstance(
+                emb_out, (torch.Tensor, list, tuple)
+            ), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}"
+            if not isinstance(emb_out, (list, tuple)):
+                emb_out = [emb_out]
+            for emb in emb_out:
+                if embedder.input_key in ('caption', 'img'):
+                    out_key = f'{embedder.input_key}_{self.OUTPUT_DIM2KEYS[emb.dim()]}'
+                elif emb.dim()==3 and emb.shape[-1] == 3:
+                    out_key = 'fps-xyz'
+                else:
+                    out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
+                if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
+                    emb = (expand_dims_like(
+                        torch.bernoulli(
+                            (1.0 - embedder.ucg_rate) *
+                            torch.ones(emb.shape[0], device=emb.device)),
+                        emb,
+                    ) * emb)
+                if (hasattr(embedder, "input_key")
+                        and embedder.input_key in force_zero_embeddings):
+                    emb = torch.zeros_like(emb)
+                if out_key in output:
+                    output[out_key] = torch.cat((output[out_key], emb),
+                                                self.KEY2CATDIM[out_key.split('_')[1]])
+                else:
+                    output[out_key] = emb
+        return output
+
+    def get_unconditional_conditioning(
+        self,
+        batch_c: Dict,
+        batch_uc: Optional[Dict] = None,
+        force_uc_zero_embeddings: Optional[List[str]] = None,
+        force_cond_zero_embeddings: Optional[List[str]] = None,
+    ):
+        if force_uc_zero_embeddings is None:
+            force_uc_zero_embeddings = []
+        ucg_rates = list()
+        for embedder in self.embedders:
+            ucg_rates.append(embedder.ucg_rate)
+            embedder.ucg_rate = 0.0 # ! force no drop during inference
+        c = self(batch_c, force_cond_zero_embeddings)
+        uc = self(batch_c if batch_uc is None else batch_uc,
+                  force_uc_zero_embeddings)
+
+        for embedder, rate in zip(self.embedders, ucg_rates):
+            embedder.ucg_rate = rate
+        return c, uc
+
+
+class InceptionV3(nn.Module):
+    """Wrapper around the https://github.com/mseitzer/pytorch-fid inception
+    port with an additional squeeze at the end"""
+
+    def __init__(self, normalize_input=False, **kwargs):
+        super().__init__()
+        from pytorch_fid import inception
+
+        kwargs["resize_input"] = True
+        self.model = inception.InceptionV3(normalize_input=normalize_input,
+                                           **kwargs)
+
+    def forward(self, inp):
+        outp = self.model(inp)
+
+        if len(outp) == 1:
+            return outp[0].squeeze()
+
+        return outp
+
+
+class IdentityEncoder(AbstractEmbModel):
+
+    def encode(self, x):
+        return x
+
+    def forward(self, x):
+        return x
+
+
+class ClassEmbedder(AbstractEmbModel):
+
+    def __init__(self, embed_dim, n_classes=1000, add_sequence_dim=False):
+        super().__init__()
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+        self.n_classes = n_classes
+        self.add_sequence_dim = add_sequence_dim
+
+    def forward(self, c):
+        c = self.embedding(c)
+        if self.add_sequence_dim:
+            c = c[:, None, :]
+        return c
+
+    def get_unconditional_conditioning(self, bs, device="cuda"):
+        uc_class = (
+            self.n_classes - 1
+        )  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+        uc = torch.ones((bs, ), device=device) * uc_class
+        uc = {self.key: uc.long()}
+        return uc
+
+
+class ClassEmbedderForMultiCond(ClassEmbedder):
+
+    def forward(self, batch, key=None, disable_dropout=False):
+        out = batch
+        key = default(key, self.key)
+        islist = isinstance(batch[key], list)
+        if islist:
+            batch[key] = batch[key][0]
+        c_out = super().forward(batch, key, disable_dropout)
+        out[key] = [c_out] if islist else c_out
+        return out
+
+
+class FrozenT5Embedder(AbstractEmbModel):
+    """Uses the T5 transformer encoder for text"""
+
+    def __init__(self,
+                 version="google/t5-v1_1-xxl",
+                 device="cuda",
+                 max_length=77,
+                 freeze=True
+                 ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = T5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        with torch.autocast("cuda", enabled=False):
+            outputs = self.transformer(input_ids=tokens)
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenByT5Embedder(AbstractEmbModel):
+    """
+    Uses the ByT5 transformer encoder for text. Is character-aware.
+    """
+
+    def __init__(self,
+                 version="google/byt5-base",
+                 device="cuda",
+                 max_length=77,
+                 freeze=True
+                 ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = ByT5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        with torch.autocast("cuda", enabled=False):
+            outputs = self.transformer(input_ids=tokens)
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEmbModel):
+    """Uses the CLIP transformer encoder for text (from huggingface)"""
+
+    LAYERS = ["last", "pooled", "hidden"]
+
+    def __init__(
+        self,
+        version="openai/clip-vit-large-patch14",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        layer="last",
+        layer_idx=None,
+        always_return_pooled=False,
+    ):  # clip-vit-base-patch32
+        super().__init__()
+        assert layer in self.LAYERS
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        self.layer_idx = layer_idx
+        self.return_pooled = always_return_pooled
+        if layer == "hidden":
+            assert layer_idx is not None
+            assert 0 <= abs(layer_idx) <= 12
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens,
+                                   output_hidden_states=self.layer == "hidden")
+        if self.layer == "last":
+            z = outputs.last_hidden_state
+        elif self.layer == "pooled":
+            z = outputs.pooler_output[:, None, :]
+        else:
+            z = outputs.hidden_states[self.layer_idx]
+        if self.return_pooled:
+            return z, outputs.pooler_output
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPEmbedder2(AbstractEmbModel):
+    """
+    Uses the OpenCLIP transformer encoder for text
+    """
+
+    LAYERS = ["pooled", "last", "penultimate"]
+
+    def __init__(
+        self,
+        arch="ViT-H-14",
+        version="laion2b_s32b_b79k",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        layer="last",
+        always_return_pooled=False,
+        legacy=True,
+    ):
+        super().__init__()
+        assert layer in self.LAYERS
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch,
+            device=torch.device("cpu"),
+            pretrained=version,
+        )
+        del model.visual
+        self.model = model
+
+        self.device = device
+        self.max_length = max_length
+        self.return_pooled = always_return_pooled
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "last":
+            self.layer_idx = 0
+        elif self.layer == "penultimate":
+            self.layer_idx = 1
+        else:
+            raise NotImplementedError()
+        self.legacy = legacy
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, text):
+        tokens = open_clip.tokenize(text)
+        z = self.encode_with_transformer(tokens.to(self.device))
+        if not self.return_pooled and self.legacy:
+            return z
+        if self.return_pooled:
+            assert not self.legacy
+            return z[self.layer], z["pooled"]
+        return z[self.layer]
+
+    def encode_with_transformer(self, text):
+        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
+        x = x + self.model.positional_embedding
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+        if self.legacy:
+            x = x[self.layer]
+            x = self.model.ln_final(x)
+            return x
+        else:
+            # x is a dict and will stay a dict
+            o = x["last"]
+            o = self.model.ln_final(o)
+            pooled = self.pool(o, text)
+            x["pooled"] = pooled
+            return x
+
+    def pool(self, x, text):
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x = (x[torch.arange(x.shape[0]),
+               text.argmax(dim=-1)] @ self.model.text_projection)
+        return x
+
+    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
+        outputs = {}
+        for i, r in enumerate(self.model.transformer.resblocks):
+            if i == len(self.model.transformer.resblocks) - 1:
+                outputs["penultimate"] = x.permute(1, 0, 2)  # LND -> NLD
+            if (self.model.transformer.grad_checkpointing
+                    and not torch.jit.is_scripting()):
+                x = checkpoint(r, x, attn_mask)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        outputs["last"] = x.permute(1, 0, 2)  # LND -> NLD
+        return outputs
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPEmbedder(AbstractEmbModel):
+    LAYERS = [
+        # "pooled",
+        "last",
+        "penultimate",
+    ]
+
+    def __init__(
+        self,
+        arch="ViT-H-14",
+        version="laion2b_s32b_b79k",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        layer="last",
+    ):
+        super().__init__()
+        assert layer in self.LAYERS
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch, device=torch.device("cpu"), pretrained=version)
+        del model.visual
+        self.model = model
+
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "last":
+            self.layer_idx = 0
+        elif self.layer == "penultimate":
+            self.layer_idx = 1
+        else:
+            raise NotImplementedError()
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = open_clip.tokenize(text)
+        z = self.encode_with_transformer(tokens.to(self.device))
+        return z
+
+    def encode_with_transformer(self, text):
+        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
+        x = x + self.model.positional_embedding
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.model.ln_final(x)
+        return x
+
+    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
+        for i, r in enumerate(self.model.transformer.resblocks):
+            if i == len(self.model.transformer.resblocks) - self.layer_idx:
+                break
+            if (self.model.transformer.grad_checkpointing
+                    and not torch.jit.is_scripting()):
+                x = checkpoint(r, x, attn_mask)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
+    """
+    Uses the OpenCLIP vision transformer encoder for images
+    """
+
+    def __init__(
+        self,
+        # arch="ViT-H-14",
+        # version="laion2b_s32b_b79k",
+        arch="ViT-L-14",
+        # version="laion2b_s32b_b82k",
+        version="openai",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        antialias=True,
+        ucg_rate=0.0,
+        unsqueeze_dim=False,
+        repeat_to_max_len=False,
+        num_image_crops=0,
+        output_tokens=False,
+        init_device=None,
+        inp_size=224,
+    ):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch,
+            device=torch.device(default(init_device, "cpu")),
+            pretrained=version,
+        )
+        del model.transformer
+        self.inp_size = inp_size
+        self.model = model
+        self.max_crops = num_image_crops
+        self.pad_to_max_len = self.max_crops > 0
+        self.repeat_to_max_len = repeat_to_max_len and (
+            not self.pad_to_max_len)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+        self.antialias = antialias
+
+        self.register_buffer("mean",
+                             torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
+                             persistent=False)
+        self.register_buffer("std",
+                             torch.Tensor([0.26862954, 0.26130258,
+                                           0.27577711]),
+                             persistent=False)
+        self.ucg_rate = ucg_rate
+        self.unsqueeze_dim = unsqueeze_dim
+        self.stored_batch = None
+        self.model.visual.output_tokens = output_tokens
+        self.output_tokens = output_tokens
+        self.interpolate_offset = 0.0
+        self.patch_size = 14
+        npatch = (self.inp_size // self.patch_size) ** 2
+        # https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/configs/eval/vitl14_reg4_pretrain.yaml#L5
+        self.interpolate_antialias = True
+
+        if self.inp_size != 224:
+            self.model.visual.positional_embedding = torch.nn.Parameter(self.interpolate_pos_encoding(npatch, self.inp_size, self.inp_size) )
+
+    # https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179
+    def interpolate_pos_encoding(self, npatch, w, h):
+        dim = self.model.visual.positional_embedding.shape[-1]
+
+        # previous_dtype = x.dtype
+        previous_dtype = torch.float32
+        # npatch = x.shape[1] - 1
+
+        pos_embed = self.model.visual.positional_embedding.float().unsqueeze(0)
+        N = pos_embed.shape[1] - 1
+
+        if npatch == N and w == h:
+            return self.model.visual.positional_embedding
+
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        # dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        M = int(math.sqrt(N))  # Recover the number of patches in each dimension
+        assert N == M * M
+        kwargs = {}
+        if self.interpolate_offset:
+            # Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8
+            # Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors
+            sx = float(w0 + self.interpolate_offset) / M
+            sy = float(h0 + self.interpolate_offset) / M
+            kwargs["scale_factor"] = (sx, sy)
+        else:
+            # Simply specify an output size instead of a scale factor
+            kwargs["size"] = (w0, h0)
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, M, M, dim).permute(0, 3, 1, 2),
+            mode="bicubic",
+            antialias=self.interpolate_antialias,
+            **kwargs,
+        )
+        assert (w0, h0) == patch_pos_embed.shape[-2:]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)[0]
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(
+            x,
+            (self.inp_size, self.inp_size),
+            interpolation="bicubic",
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, image, no_dropout=False):
+        z = self.encode_with_vision_transformer(image)
+        tokens = None
+        if self.output_tokens:
+            z, tokens = z[0], z[1]
+        z = z.to(image.dtype)
+        if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
+            z = (torch.bernoulli(
+                (1.0 - self.ucg_rate) *
+                torch.ones(z.shape[0], device=z.device))[:, None] * z)
+            if tokens is not None:
+                tokens = (expand_dims_like(
+                    torch.bernoulli(
+                        (1.0 - self.ucg_rate) *
+                        torch.ones(tokens.shape[0], device=tokens.device)),
+                    tokens,
+                ) * tokens)
+        if self.unsqueeze_dim:
+            z = z[:, None, :]
+        if self.output_tokens:
+            assert not self.repeat_to_max_len
+            assert not self.pad_to_max_len
+            return tokens, z
+        if self.repeat_to_max_len:
+            if z.dim() == 2:
+                z_ = z[:, None, :]
+            else:
+                z_ = z
+            return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
+        elif self.pad_to_max_len:
+            assert z.dim() == 3
+            z_pad = torch.cat(
+                (
+                    z,
+                    torch.zeros(
+                        z.shape[0],
+                        self.max_length - z.shape[1],
+                        z.shape[2],
+                        device=z.device,
+                    ),
+                ),
+                1,
+            )
+            return z_pad, z_pad[:, 0, ...]
+        return z
+
+    def encode_with_vision_transformer(self, img):
+        # if self.max_crops > 0:
+        #    img = self.preprocess_by_cropping(img)
+        if img.dim() == 5:
+            assert self.max_crops == img.shape[1]
+            img = rearrange(img, "b n c h w -> (b n) c h w")
+        img = self.preprocess(img)
+        if not self.output_tokens:
+            assert not self.model.visual.output_tokens
+            x = self.model.visual(img)
+            tokens = None
+        else:
+            assert self.model.visual.output_tokens
+            x, tokens = self.model.visual(img)
+        if self.max_crops > 0:
+            x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
+            # drop out between 0 and all along the sequence axis
+            x = (torch.bernoulli(
+                (1.0 - self.ucg_rate) *
+                torch.ones(x.shape[0], x.shape[1], 1, device=x.device)) * x)
+            if tokens is not None:
+                tokens = rearrange(tokens,
+                                   "(b n) t d -> b t (n d)",
+                                   n=self.max_crops)
+                print(
+                    f"You are running very experimental token-concat in {self.__class__.__name__}. "
+                    f"Check what you are doing, and then remove this message.")
+        if self.output_tokens:
+            return x, tokens
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+
+# dino-v2 embedder
+class FrozenDinov2ImageEmbedder(AbstractEmbModel):
+    """
+    Uses the Dino-v2 for low-level image embedding
+    """
+
+    def __init__(
+        self,
+        arch="vitl",
+        version="dinov2",  # by default
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        antialias=True,
+        ucg_rate=0.0,
+        unsqueeze_dim=False,
+        repeat_to_max_len=False,
+        num_image_crops=0,
+        output_tokens=False,
+        output_cls=False,
+        init_device=None,
+        inp_size=224,
+    ):
+        super().__init__()
+
+        self.model = torch.hub.load(
+            f'facebookresearch/{version}',
+            '{}_{}{}_reg'.format(
+                version, f'{arch}', '14'
+            ),  # with registers better performance. vitl and vitg similar. Since fixed, load the best one.
+            pretrained=True).to(torch.device(default(init_device, "cpu")))
+        
+        # print(self.model)
+
+        # ! frozen
+        # self.tokenizer.requires_grad_(False)
+        # self.tokenizer.eval()
+
+        # assert freeze # add adaLN here
+        self.inp_size = inp_size
+        if freeze:
+            self.freeze()
+
+        # self.model = model
+        self.max_crops = num_image_crops
+        self.pad_to_max_len = self.max_crops > 0
+        self.repeat_to_max_len = repeat_to_max_len and (
+            not self.pad_to_max_len)
+        self.device = device
+        self.max_length = max_length
+
+        self.antialias = antialias
+
+        # https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/data/transforms.py#L41
+        IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+        IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+        self.register_buffer("mean",
+                             torch.Tensor(IMAGENET_DEFAULT_MEAN),
+                             persistent=False)
+        self.register_buffer("std",
+                             torch.Tensor(IMAGENET_DEFAULT_STD),
+                             persistent=False)
+
+        self.ucg_rate = ucg_rate
+        self.unsqueeze_dim = unsqueeze_dim
+        self.stored_batch = None
+        # self.model.visual.output_tokens = output_tokens
+        self.output_tokens = output_tokens  # output
+        self.output_cls = output_cls
+        # self.output_tokens = False
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(
+            x,
+            # (224, 224),
+            (self.inp_size, self.inp_size),
+            interpolation="bicubic",
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def _model_forward(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
+
+    def encode_with_vision_transformer(self, img, **kwargs):
+        # if self.max_crops > 0:
+        #    img = self.preprocess_by_cropping(img)
+        if img.dim() == 5:
+            # assert self.max_crops == img.shape[1]
+            img = rearrange(img, "b n c h w -> (b n) c h w")
+        img = self.preprocess(img)
+
+        # https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L326
+        if not self.output_cls:
+            return self._model_forward(
+                img, is_training=True,
+                **kwargs)['x_norm_patchtokens']  # to return spatial tokens
+
+        else:
+            dino_ret_dict = self._model_forward(
+                img, is_training=True)  # to return spatial tokens
+            x_patchtokens, x_norm_clstoken = dino_ret_dict[
+                'x_norm_patchtokens'], dino_ret_dict['x_norm_clstoken']
+
+            return x_norm_clstoken, x_patchtokens
+
+    @autocast
+    def forward(self, image, no_dropout=False, **kwargs):
+        tokens = self.encode_with_vision_transformer(image, **kwargs)
+        z = None
+        if self.output_cls:
+            # z, tokens = z[0], z[1]
+            z, tokens = tokens[0], tokens[1]
+            z = z.to(image.dtype)
+        tokens = tokens.to(image.dtype)  # ! return spatial tokens only
+        if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
+            if z is not None:
+                z = (torch.bernoulli(
+                    (1.0 - self.ucg_rate) *
+                    torch.ones(z.shape[0], device=z.device))[:, None] * z)
+            tokens = (expand_dims_like(
+                torch.bernoulli(
+                    (1.0 - self.ucg_rate) *
+                    torch.ones(tokens.shape[0], device=tokens.device)),
+                tokens,
+            ) * tokens)
+        if self.output_cls:
+            return tokens, z
+        else:
+            return tokens
+
+
+class FrozenDinov2ImageEmbedderMVPlucker(FrozenDinov2ImageEmbedder):
+
+    def __init__(
+        self,
+        arch="vitl",
+        version="dinov2",  # by default
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        antialias=True,
+        ucg_rate=0.0,
+        unsqueeze_dim=False,
+        repeat_to_max_len=False,
+        num_image_crops=0,
+        output_tokens=False,
+        output_cls=False,
+        init_device=None,
+        # mv cond settings
+        n_cond_frames=4,  # numebr of condition views
+        enable_bf16=False,
+        modLN=False,
+        aug_c=False,
+        inp_size=224,
+    ):
+        super().__init__(
+            arch,
+            version,
+            device,
+            max_length,
+            freeze,
+            antialias,
+            ucg_rate,
+            unsqueeze_dim,
+            repeat_to_max_len,
+            num_image_crops,
+            output_tokens,
+            output_cls,
+            init_device,
+            inp_size=inp_size,
+        )
+        self.n_cond_frames = n_cond_frames
+        self.dtype = torch.bfloat16 if enable_bf16 else torch.float32
+        self.enable_bf16 = enable_bf16
+        self.aug_c = aug_c
+
+        # ! proj c_cond to features
+
+        self.reso_encoder = inp_size
+        orig_patch_embed_weight = self.model.patch_embed.state_dict()
+
+        # ! 9-d input
+        with torch.no_grad():
+            new_patch_embed = PatchEmbed(img_size=224,
+                                        patch_size=14,
+                                        in_chans=9,
+                                        embed_dim=self.model.embed_dim)
+            # zero init first
+            nn.init.constant_(new_patch_embed.proj.weight, 0)
+            nn.init.constant_(new_patch_embed.proj.bias, 0)
+            # load pre-trained first 3 layers weights, bias into the new patch_embed
+
+            new_patch_embed.proj.weight[:, :3].copy_(orig_patch_embed_weight['proj.weight'])
+            new_patch_embed.proj.bias[:].copy_(orig_patch_embed_weight['proj.bias'])
+
+        self.model.patch_embed = new_patch_embed  # xyz in the front
+        # self.scale_jitter_aug = torchvision.transforms.v2.ScaleJitter(target_size=(self.reso_encoder, self.reso_encoder), scale_range=(0.5, 1.5))
+
+    @autocast
+    def scale_jitter_aug(self, x):
+        inp_size = x.shape[2]
+        # aug_size = torch.randint(low=50, high=100, size=(1,)) / 100 * inp_size
+        aug_size = int(max(0.5, random.random()) * inp_size)
+        # st()
+        x = torch.nn.functional.interpolate(x,
+                                            size=aug_size,
+                                            mode='bilinear', 
+                                            antialias=True)
+        x = torch.nn.functional.interpolate(x,size=inp_size,
+                                            mode='bilinear', antialias=True)
+        return x
+
+    @autocast
+    def gen_rays(self, c):
+        # Generate rays
+        intrinsics, c2w = c[16:], c[:16].reshape(4, 4)
+        self.h = self.reso_encoder
+        self.w = self.reso_encoder
+        yy, xx = torch.meshgrid(
+            torch.arange(self.h, dtype=torch.float32, device=c.device) + 0.5,
+            torch.arange(self.w, dtype=torch.float32, device=c.device) + 0.5,
+            indexing='ij')
+
+        # normalize to 0-1 pixel range
+        yy = yy / self.h
+        xx = xx / self.w
+
+        # K = np.array([f_x, 0, w / 2, 0, f_y, h / 2, 0, 0, 1]).reshape(3, 3)
+        cx, cy, fx, fy = intrinsics[2], intrinsics[5], intrinsics[
+            0], intrinsics[4]
+        # cx *= self.w
+        # cy *= self.h
+
+        # f_x = f_y = fx * h / res_raw
+        # c2w = torch.from_numpy(c2w).float()
+        c2w = c2w.float()
+
+        xx = (xx - cx) / fx
+        yy = (yy - cy) / fy
+        zz = torch.ones_like(xx)
+        dirs = torch.stack((xx, yy, zz), dim=-1)  # OpenCV convention
+        dirs /= torch.norm(dirs, dim=-1, keepdim=True)
+        dirs = dirs.reshape(-1, 3, 1)
+        del xx, yy, zz
+        # st()
+        dirs = (c2w[None, :3, :3] @ dirs)[..., 0]
+
+        origins = c2w[None, :3, 3].expand(self.h * self.w, -1).contiguous()
+        origins = origins.view(self.h, self.w, 3)
+        dirs = dirs.view(self.h, self.w, 3)
+
+        return origins, dirs
+
+    @autocast
+    def get_plucker_ray(self, c):
+        rays_plucker = []
+        for idx in range(c.shape[0]):
+            rays_o, rays_d = self.gen_rays(c[idx])
+            rays_plucker.append(
+                torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d],
+                          dim=-1).permute(2, 0, 1))  # [h, w, 6] -> 6,h,w
+        rays_plucker = torch.stack(rays_plucker, 0)
+        return rays_plucker
+
+    @autocast
+    def _model_forward(self, x, plucker_c, *args, **kwargs):
+
+        with torch.cuda.amp.autocast(dtype=self.dtype, enabled=True):
+            x = torch.cat([x, plucker_c], dim=1).to(self.dtype)
+            return self.model(x, **kwargs)
+
+    def preprocess(self, x):
+        # add gaussian noise and rescale augmentation
+
+        if self.ucg_rate > 0.0:
+
+            # 1 means maintain the input x
+            enable_drop_flag = torch.bernoulli(
+                (1.0 - self.ucg_rate) *
+                torch.ones(x.shape[0], device=x.device))[:, None, None, None] # broadcast to B,1,1,1
+
+            # * add random downsample & upsample
+            # rescaled_x = self.downsample_upsample(x)
+            # torchvision.utils.save_image(x, 'tmp/x.png', normalize=True, value_range=(-1,1))
+            x_aug = self.scale_jitter_aug(x)
+            # torchvision.utils.save_image(x_aug, 'tmp/rescale-x.png', normalize=True, value_range=(-1,1))
+
+            # x_aug = x * enable_drop_flag + (1-enable_drop_flag) * x_aug
+
+            # * guassian noise jitter
+            # force linear_weight > 0.24
+            # linear_weight = torch.max(enable_drop_flag, torch.max(torch.rand_like(enable_drop_flag), 0.25 * torch.ones_like(enable_drop_flag), dim=0, keepdim=True), dim=0, keepdim=True)
+            gaussian_jitter_scale, jitter_lb = torch.rand_like(enable_drop_flag), 0.8 * torch.ones_like(enable_drop_flag)           
+            gaussian_jitter_scale = torch.where(gaussian_jitter_scale>jitter_lb, gaussian_jitter_scale, jitter_lb)
+
+            # torchvision.utils.save_image(x, 'tmp/aug-x.png', normalize=True, value_range=(-1,1))
+            x_aug = gaussian_jitter_scale * x_aug + (1 - gaussian_jitter_scale) * torch.randn_like(x).clamp(-1,1)
+
+            x_aug = x * enable_drop_flag + (1-enable_drop_flag) * x_aug
+            # torchvision.utils.save_image(x_aug, 'tmp/final-x.png', normalize=True, value_range=(-1,1))
+
+        # st()
+
+        return super().preprocess(x)
+
+    def random_rotate_c(self, c):
+
+        intrinsics, c2ws = c[16:], c[:16].reshape(4, 4)
+
+        # https://github.com/TencentARC/InstantMesh/blob/34c193cc96eebd46deb7c48a76613753ad777122/src/data/objaverse.py#L195
+
+        degree = np.random.uniform(-math.pi * 0.25, math.pi * 0.25)
+
+        # random rotation along z axis
+        if random.random() > 0.5:
+            rot = torch.tensor([
+                [np.cos(degree), -np.sin(degree), 0, 0],
+                [np.sin(degree), np.cos(degree), 0, 0],
+                [0, 0, 1, 0],
+                [0, 0, 0, 1],
+            ]).to(c2ws)
+        else:
+            # random rotation along y axis
+            rot = torch.tensor([
+                [np.cos(degree), 0, np.sin(degree),  0],
+                [0, 1, 0, 0],
+                [-np.sin(degree), 0, np.cos(degree), 0],
+                [0, 0, 0, 1],
+            ]).to(c2ws)
+
+        c2ws = torch.matmul(rot, c2ws)
+
+        return torch.cat([c2ws.reshape(-1), intrinsics])
+
+    @autocast
+    def forward(self, img_c, no_dropout=False):
+
+
+        mv_image, c = img_c['img'], img_c['c']
+
+        if self.aug_c:
+            for idx_b in range(c.shape[0]):
+                for idx_v in range(c.shape[1]):
+                    if random.random() > 0.8:
+                        c[idx_b, idx_v] = self.random_rotate_c(c[idx_b, idx_v])
+
+        # plucker_c = self.get_plucker_ray(
+        #     rearrange(c[:, 1:1 + self.n_cond_frames], "b t ... -> (b t) ..."))
+        plucker_c = self.get_plucker_ray(
+            rearrange(c[:, :self.n_cond_frames], "b t ... -> (b t) ..."))
+        
+        # plucker_c = torch.ones_like(plucker_c)
+        # plucker_c = torch.zeros_like(plucker_c)
+
+        # mv_image_tokens = super().forward(mv_image[:, 1:1 + self.n_cond_frames],
+        mv_image_tokens = super().forward(mv_image[:, :self.n_cond_frames],
+                                plucker_c=plucker_c, 
+                                no_dropout=no_dropout)
+
+        mv_image_tokens = rearrange(mv_image_tokens,
+                            "(b t) ... -> b t ...",
+                            t=self.n_cond_frames)
+
+        return mv_image_tokens
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_HW,
+                              stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (
+            self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
+
+
+class FrozenDinov2ImageEmbedderMV(FrozenDinov2ImageEmbedder):
+
+    def __init__(
+        self,
+        arch="vitl",
+        version="dinov2",  # by default
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        antialias=True,
+        ucg_rate=0.0,
+        unsqueeze_dim=False,
+        repeat_to_max_len=False,
+        num_image_crops=0,
+        output_tokens=False,
+        output_cls=False,
+        init_device=None,
+        # mv cond settings
+        n_cond_frames=4,  # numebr of condition views
+        enable_bf16=False,
+        modLN=False,
+        inp_size=224,
+    ):
+        super().__init__(
+            arch,
+            version,
+            device,
+            max_length,
+            freeze,
+            antialias,
+            ucg_rate,
+            unsqueeze_dim,
+            repeat_to_max_len,
+            num_image_crops,
+            output_tokens,
+            output_cls,
+            init_device,
+            inp_size=inp_size,
+        )
+        self.n_cond_frames = n_cond_frames
+        self.dtype = torch.bfloat16 if enable_bf16 else torch.float32
+        self.enable_bf16 = enable_bf16
+
+        # ! proj c_cond to features
+
+        hidden_size = self.model.embed_dim  # 768 for vit-b
+
+        # self.cam_proj = CaptionEmbedder(16, hidden_size,
+        self.cam_proj = CaptionEmbedder(25, hidden_size, act_layer=approx_gelu)
+
+        # ! single-modLN
+        self.model.modLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 4 * hidden_size, bias=True))
+
+        # zero-init modLN
+        nn.init.constant_(self.model.modLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.model.modLN_modulation[-1].bias, 0)
+
+        # inject modLN to dino block
+        for block in self.model.blocks:
+            block.scale_shift_table = nn.Parameter(torch.zeros(
+                4, hidden_size))  # zero init also
+
+            # torch.randn(4, hidden_size) / hidden_size**0.5)
+
+    def _model_forward(self, x, *args, **kwargs):
+        # re-define model forward, finetune dino-v2.
+        assert self.training
+
+        # ? how to send in camera
+        # c = 0 # placeholder
+        # ret = self.model.forward_features(*args, **kwargs)
+
+        with torch.cuda.amp.autocast(dtype=self.dtype, enabled=True):
+
+            x = self.model.prepare_tokens_with_masks(x, masks=None)
+
+            B, N, C = x.shape
+            # TODO how to send in c
+            # c = torch.ones(B, 25).to(x) # placeholder
+            c = kwargs.get('c')
+            c = self.cam_proj(c)
+            cond = self.model.modLN_modulation(c)
+
+            # https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/layers/block.py#L89
+            for blk in self.model.blocks:  # inject modLN
+
+                shift_msa, scale_msa, shift_mlp, scale_mlp = (
+                    blk.scale_shift_table[None] +
+                    cond.reshape(B, 4, -1)).chunk(4, dim=1)
+
+                def attn_residual_func(x: torch.Tensor) -> torch.Tensor:
+                    # return blk.ls1(blk.attn(blk.norm1(x), attn_bias=attn_bias))
+                    return blk.ls1(
+                        blk.attn(
+                            t2i_modulate(blk.norm1(x), shift_msa, scale_msa)))
+
+                def ffn_residual_func(x: torch.Tensor) -> torch.Tensor:
+                    # return blk.ls2(blk.mlp(blk.norm2(x)))
+                    return blk.ls2(
+                        t2i_modulate(blk.mlp(blk.norm2(x)), shift_mlp,
+                                     scale_mlp))
+
+                x = x + blk.drop_path1(
+                    attn_residual_func(x))  # all drop_path identity() here.
+                x = x + blk.drop_path2(ffn_residual_func(x))
+
+            x_norm = self.model.norm(x)
+
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            # "x_norm_regtokens": x_norm[:, 1 : self.model.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:,
+                                         self.model.num_register_tokens + 1:],
+            # "x_prenorm": x,
+            # "masks": masks,
+        }
+
+    @autocast
+    def forward(self, img_c, no_dropout=False):
+
+        # if self.enable_bf16:
+        #     with th.cuda.amp.autocast(dtype=self.dtype,
+        #                               enabled=True):
+        # mv_image = super().forward(mv_image[:, 1:1+self.n_cond_frames].to(torch.bf16))
+        # else:
+        mv_image, c = img_c['img'], img_c['c']
+
+        # ! use zero c here, ablation. current verison wrong.
+        # c = torch.zeros_like(c)
+
+        # ! frame-0 as canonical here.
+
+        mv_image = super().forward(mv_image[:, :self.n_cond_frames],
+                                   c=rearrange(c[:, :self.n_cond_frames],
+                                               "b t ... -> (b t) ...",
+                                               t=self.n_cond_frames), 
+                                               no_dropout=no_dropout)
+
+        mv_image = rearrange(mv_image,
+                             "(b t) ... -> b t ...",
+                             t=self.n_cond_frames)
+
+        return mv_image
+
+
+class FrozenCLIPT5Encoder(AbstractEmbModel):
+
+    def __init__(
+        self,
+        clip_version="openai/clip-vit-large-patch14",
+        t5_version="google/t5-v1_1-xl",
+        device="cuda",
+        clip_max_length=77,
+        t5_max_length=77,
+    ):
+        super().__init__()
+        self.clip_encoder = FrozenCLIPEmbedder(clip_version,
+                                               device,
+                                               max_length=clip_max_length)
+        self.t5_encoder = FrozenT5Embedder(t5_version,
+                                           device,
+                                           max_length=t5_max_length)
+        print(
+            f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
+            f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
+        )
+
+    def encode(self, text):
+        return self(text)
+
+    def forward(self, text):
+        clip_z = self.clip_encoder.encode(text)
+        t5_z = self.t5_encoder.encode(text)
+        return [clip_z, t5_z]
+
+
+class SpatialRescaler(nn.Module):
+
+    def __init__(
+        self,
+        n_stages=1,
+        method="bilinear",
+        multiplier=0.5,
+        in_channels=3,
+        out_channels=None,
+        bias=False,
+        wrap_video=False,
+        kernel_size=1,
+        remap_output=False,
+    ):
+        super().__init__()
+        self.n_stages = n_stages
+        assert self.n_stages >= 0
+        assert method in [
+            "nearest",
+            "linear",
+            "bilinear",
+            "trilinear",
+            "bicubic",
+            "area",
+        ]
+        self.multiplier = multiplier
+        self.interpolator = partial(torch.nn.functional.interpolate,
+                                    mode=method)
+        self.remap_output = out_channels is not None or remap_output
+        if self.remap_output:
+            print(
+                f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
+            )
+            self.channel_mapper = nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                bias=bias,
+                padding=kernel_size // 2,
+            )
+        self.wrap_video = wrap_video
+
+    def forward(self, x):
+        if self.wrap_video and x.ndim == 5:
+            B, C, T, H, W = x.shape
+            x = rearrange(x, "b c t h w -> b t c h w")
+            x = rearrange(x, "b t c h w -> (b t) c h w")
+
+        for stage in range(self.n_stages):
+            x = self.interpolator(x, scale_factor=self.multiplier)
+
+        if self.wrap_video:
+            x = rearrange(x, "(b t) c h w -> b t c h w", b=B, t=T, c=C)
+            x = rearrange(x, "b t c h w -> b c t h w")
+        if self.remap_output:
+            x = self.channel_mapper(x)
+        return x
+
+    def encode(self, x):
+        return self(x)
+
+
+class LowScaleEncoder(nn.Module):
+
+    def __init__(
+        self,
+        model_config,
+        linear_start,
+        linear_end,
+        timesteps=1000,
+        max_noise_level=250,
+        output_size=64,
+        scale_factor=1.0,
+    ):
+        super().__init__()
+        self.max_noise_level = max_noise_level
+        self.model = instantiate_from_config(model_config)
+        self.augmentation_schedule = self.register_schedule(
+            timesteps=timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end)
+        self.out_size = output_size
+        self.scale_factor = scale_factor
+
+    def register_schedule(
+        self,
+        beta_schedule="linear",
+        timesteps=1000,
+        linear_start=1e-4,
+        linear_end=2e-2,
+        cosine_s=8e-3,
+    ):
+        betas = make_beta_schedule(
+            beta_schedule,
+            timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end,
+            cosine_s=cosine_s,
+        )
+        alphas = 1.0 - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
+
+        (timesteps, ) = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert (alphas_cumprod.shape[0] == self.num_timesteps
+                ), "alphas have to be defined for each timestep"
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer("betas", to_torch(betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
+        self.register_buffer("alphas_cumprod_prev",
+                             to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer("sqrt_alphas_cumprod",
+                             to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer("sqrt_one_minus_alphas_cumprod",
+                             to_torch(np.sqrt(1.0 - alphas_cumprod)))
+        self.register_buffer("log_one_minus_alphas_cumprod",
+                             to_torch(np.log(1.0 - alphas_cumprod)))
+        self.register_buffer("sqrt_recip_alphas_cumprod",
+                             to_torch(np.sqrt(1.0 / alphas_cumprod)))
+        self.register_buffer("sqrt_recipm1_alphas_cumprod",
+                             to_torch(np.sqrt(1.0 / alphas_cumprod - 1)))
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (
+            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) *
+            x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod,
+                                          t, x_start.shape) * noise)
+
+    def forward(self, x):
+        z = self.model.encode(x)
+        if isinstance(z, DiagonalGaussianDistribution):
+            z = z.sample()
+        z = z * self.scale_factor
+        noise_level = torch.randint(0,
+                                    self.max_noise_level, (x.shape[0], ),
+                                    device=x.device).long()
+        z = self.q_sample(z, noise_level)
+        if self.out_size is not None:
+            z = torch.nn.functional.interpolate(z,
+                                                size=self.out_size,
+                                                mode="nearest")
+        return z, noise_level
+
+    def decode(self, z):
+        z = z / self.scale_factor
+        return self.model.decode(z)
+
+
+class ConcatTimestepEmbedderND(AbstractEmbModel):
+    """embeds each dimension independently and concatenates them"""
+
+    def __init__(self, outdim):
+        super().__init__()
+        self.timestep = Timestep(outdim)
+        self.outdim = outdim
+
+    def forward(self, x):
+        if x.ndim == 1:
+            x = x[:, None]
+        assert len(x.shape) == 2
+        b, dims = x.shape[0], x.shape[1]
+        x = rearrange(x, "b d -> (b d)")
+        emb = self.timestep(x)
+        emb = rearrange(emb,
+                        "(b d) d2 -> b (d d2)",
+                        b=b,
+                        d=dims,
+                        d2=self.outdim)
+        return emb
+
+
+class GaussianEncoder(Encoder, AbstractEmbModel):
+
+    def __init__(self,
+                 weight: float = 1.0,
+                 flatten_output: bool = True,
+                 *args,
+                 **kwargs):
+        super().__init__(*args, **kwargs)
+        self.posterior = DiagonalGaussianRegularizer()
+        self.weight = weight
+        self.flatten_output = flatten_output
+
+    def forward(self, x) -> Tuple[Dict, torch.Tensor]:
+        z = super().forward(x)
+        z, log = self.posterior(z)
+        log["loss"] = log["kl_loss"]
+        log["weight"] = self.weight
+        if self.flatten_output:
+            z = rearrange(z, "b c h w -> b (h w ) c")
+        return log, z
+
+
+class VideoPredictionEmbedderWithEncoder(AbstractEmbModel):
+
+    def __init__(
+        self,
+        n_cond_frames: int,
+        n_copies: int,
+        encoder_config: dict,
+        sigma_sampler_config: Optional[dict] = None,
+        sigma_cond_config: Optional[dict] = None,
+        is_ae: bool = False,
+        scale_factor: float = 1.0,
+        disable_encoder_autocast: bool = False,
+        en_and_decode_n_samples_a_time: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.n_cond_frames = n_cond_frames
+        self.n_copies = n_copies
+        self.encoder = instantiate_from_config(encoder_config)
+        self.sigma_sampler = (instantiate_from_config(sigma_sampler_config)
+                              if sigma_sampler_config is not None else None)
+        self.sigma_cond = (instantiate_from_config(sigma_cond_config)
+                           if sigma_cond_config is not None else None)
+        self.is_ae = is_ae
+        self.scale_factor = scale_factor
+        self.disable_encoder_autocast = disable_encoder_autocast
+        self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time
+
+    def forward(
+        self, vid: torch.Tensor
+    ) -> Union[
+            torch.Tensor,
+            Tuple[torch.Tensor, torch.Tensor],
+            Tuple[torch.Tensor, dict],
+            Tuple[Tuple[torch.Tensor, torch.Tensor], dict],
+    ]:
+        if self.sigma_sampler is not None:
+            b = vid.shape[0] // self.n_cond_frames
+            sigmas = self.sigma_sampler(b).to(vid.device)
+            if self.sigma_cond is not None:
+                sigma_cond = self.sigma_cond(sigmas)
+                sigma_cond = repeat(sigma_cond,
+                                    "b d -> (b t) d",
+                                    t=self.n_copies)
+            sigmas = repeat(sigmas, "b -> (b t)", t=self.n_cond_frames)
+            noise = torch.randn_like(vid)
+            vid = vid + noise * append_dims(sigmas, vid.ndim)
+
+        with torch.autocast("cuda", enabled=not self.disable_encoder_autocast):
+            n_samples = (self.en_and_decode_n_samples_a_time
+                         if self.en_and_decode_n_samples_a_time is not None
+                         else vid.shape[0])
+            n_rounds = math.ceil(vid.shape[0] / n_samples)
+            all_out = []
+            for n in range(n_rounds):
+                if self.is_ae:
+                    out = self.encoder.encode(vid[n * n_samples:(n + 1) *
+                                                  n_samples])
+                else:
+                    out = self.encoder(vid[n * n_samples:(n + 1) * n_samples])
+                all_out.append(out)
+
+        vid = torch.cat(all_out, dim=0)
+        vid *= self.scale_factor
+
+        vid = rearrange(vid,
+                        "(b t) c h w -> b () (t c) h w",
+                        t=self.n_cond_frames)
+        vid = repeat(vid, "b 1 c h w -> (b t) c h w", t=self.n_copies)
+
+        return_val = (vid, sigma_cond) if self.sigma_cond is not None else vid
+
+        return return_val
+
+
+class FrozenOpenCLIPImagePredictionEmbedder(AbstractEmbModel):
+
+    def __init__(
+        self,
+        open_clip_embedding_config: Dict,
+        n_cond_frames: int,
+        n_copies: int,
+    ):
+        super().__init__()
+
+        self.n_cond_frames = n_cond_frames
+        self.n_copies = n_copies
+        self.open_clip = instantiate_from_config(open_clip_embedding_config)
+
+    def forward(self, vid):
+        vid = self.open_clip(vid)
+        vid = rearrange(vid, "(b t) d -> b t d", t=self.n_cond_frames)
+        vid = repeat(vid, "b t d -> (b s) t d", s=self.n_copies)
+
+        return vid
+
+
+class FrozenOpenCLIPImageMVEmbedder(AbstractEmbModel):
+    # for multi-view 3D diffusion condition. Only extract the first frame
+    def __init__(
+        self,
+        open_clip_embedding_config: Dict,
+        # n_cond_frames: int,
+        # n_copies: int,
+    ):
+        super().__init__()
+
+        # self.n_cond_frames = n_cond_frames
+        # self.n_copies = n_copies
+        self.open_clip = instantiate_from_config(open_clip_embedding_config)
+
+    def forward(self, vid, no_dropout=False):
+        # st()
+        vid = self.open_clip(vid[:, 0, ...], no_dropout=no_dropout)
+        # vid = rearrange(vid, "(b t) d -> b t d", t=self.n_cond_frames)
+        # vid = repeat(vid, "b t d -> (b s) t d", s=self.n_copies)
+
+        return vid
+
+# process PCD
+
+# raw scaling
+class PCD_Scaler(AbstractEmbModel):
+    """
+    just scale the input pcd
+    TODO, add some rand noise?
+    """
+
+    def __init__(
+        self,
+        scaling_factor=0.45,
+        perturb_pcd_scale=0.0,
+    ):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        self.perturb_pcd_scale = perturb_pcd_scale
+
+    @autocast
+    def forward(self, pcd, **kwargs):
+        if self.perturb_pcd_scale > 0:
+            t = torch.rand(pcd.shape[0], 1, 1).to(pcd) * self.perturb_pcd_scale
+            pcd = pcd + t * torch.randn_like(pcd)
+            pcd = pcd.clip(-0.45, 0.45) # avoid scaling xyz too large.
+        pcd = pcd / self.scaling_factor
+        return pcd
+
+
+
+# raw scaling
+class PCD_Scaler_perChannel(AbstractEmbModel):
+    """
+    scale the input pcd to unit std
+    """
+
+    def __init__(
+        self,
+        scaling_factor=[0.14593576, 0.15753542, 0.18873914],
+    ):
+        super().__init__()
+        self.scaling_factor = np.array(scaling_factor)
+
+    @autocast
+    def forward(self, pcd, **kwargs):
+        return pcd / self.scaling_factor
\ No newline at end of file
diff --git a/sgm/modules/video_attention.py b/sgm/modules/video_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d7bd095e3e1c197a3baff21b010bea553c4fa1c
--- /dev/null
+++ b/sgm/modules/video_attention.py
@@ -0,0 +1,302 @@
+import torch
+
+from ..modules.attention import *
+from ..modules.diffusionmodules.util import (AlphaBlender, linear,
+                                             timestep_embedding)
+
+
+class TimeMixSequential(nn.Sequential):
+    def forward(self, x, context=None, timesteps=None):
+        for layer in self:
+            x = layer(x, context, timesteps)
+
+        return x
+
+
+class VideoTransformerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,
+        "softmax-xformers": MemoryEfficientCrossAttention,
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        timesteps=None,
+        ff_in=False,
+        inner_dim=None,
+        attn_mode="softmax",
+        disable_self_attn=False,
+        disable_temporal_crossattention=False,
+        switch_temporal_ca_to_sa=False,
+    ):
+        super().__init__()
+
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+
+        self.ff_in = ff_in or inner_dim is not None
+        if inner_dim is None:
+            inner_dim = dim
+
+        assert int(n_heads * d_head) == inner_dim
+
+        self.is_res = inner_dim == dim
+
+        if self.ff_in:
+            self.norm_in = nn.LayerNorm(dim)
+            self.ff_in = FeedForward(
+                dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff
+            )
+
+        self.timesteps = timesteps
+        self.disable_self_attn = disable_self_attn
+        if self.disable_self_attn:
+            self.attn1 = attn_cls(
+                query_dim=inner_dim,
+                heads=n_heads,
+                dim_head=d_head,
+                context_dim=context_dim,
+                dropout=dropout,
+            )  # is a cross-attention
+        else:
+            self.attn1 = attn_cls(
+                query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout
+            )  # is a self-attention
+
+        self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff)
+
+        if disable_temporal_crossattention:
+            if switch_temporal_ca_to_sa:
+                raise ValueError
+            else:
+                self.attn2 = None
+        else:
+            self.norm2 = nn.LayerNorm(inner_dim)
+            if switch_temporal_ca_to_sa:
+                self.attn2 = attn_cls(
+                    query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout
+                )  # is a self-attention
+            else:
+                self.attn2 = attn_cls(
+                    query_dim=inner_dim,
+                    context_dim=context_dim,
+                    heads=n_heads,
+                    dim_head=d_head,
+                    dropout=dropout,
+                )  # is self-attn if context is none
+
+        self.norm1 = nn.LayerNorm(inner_dim)
+        self.norm3 = nn.LayerNorm(inner_dim)
+        self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
+
+        self.checkpoint = checkpoint
+        if self.checkpoint:
+            print(f"{self.__class__.__name__} is using checkpointing")
+
+    def forward(
+        self, x: torch.Tensor, context: torch.Tensor = None, timesteps: int = None
+    ) -> torch.Tensor:
+        if self.checkpoint:
+            return checkpoint(self._forward, x, context, timesteps)
+        else:
+            return self._forward(x, context, timesteps=timesteps)
+
+    def _forward(self, x, context=None, timesteps=None):
+        assert self.timesteps or timesteps
+        assert not (self.timesteps and timesteps) or self.timesteps == timesteps
+        timesteps = self.timesteps or timesteps
+        B, S, C = x.shape
+        x = rearrange(x, "(b t) s c -> (b s) t c", t=timesteps)
+
+        if self.ff_in:
+            x_skip = x
+            x = self.ff_in(self.norm_in(x))
+            if self.is_res:
+                x += x_skip
+
+        if self.disable_self_attn:
+            x = self.attn1(self.norm1(x), context=context) + x
+        else:
+            x = self.attn1(self.norm1(x)) + x
+
+        if self.attn2 is not None:
+            if self.switch_temporal_ca_to_sa:
+                x = self.attn2(self.norm2(x)) + x
+            else:
+                x = self.attn2(self.norm2(x), context=context) + x
+        x_skip = x
+        x = self.ff(self.norm3(x))
+        if self.is_res:
+            x += x_skip
+
+        x = rearrange(
+            x, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
+        )
+        return x
+
+    def get_last_layer(self):
+        return self.ff.net[-1].weight
+
+
+class SpatialVideoTransformer(SpatialTransformer):
+    def __init__(
+        self,
+        in_channels,
+        n_heads,
+        d_head,
+        depth=1,
+        dropout=0.0,
+        use_linear=False,
+        context_dim=None,
+        use_spatial_context=False,
+        timesteps=None,
+        merge_strategy: str = "fixed",
+        merge_factor: float = 0.5,
+        time_context_dim=None,
+        ff_in=False,
+        checkpoint=False,
+        time_depth=1,
+        attn_mode="softmax",
+        disable_self_attn=False,
+        disable_temporal_crossattention=False,
+        max_time_embed_period: int = 10000,
+    ):
+        super().__init__(
+            in_channels,
+            n_heads,
+            d_head,
+            depth=depth,
+            dropout=dropout,
+            attn_type=attn_mode,
+            use_checkpoint=checkpoint,
+            context_dim=context_dim,
+            use_linear=use_linear,
+            disable_self_attn=disable_self_attn,
+        )
+        self.time_depth = time_depth
+        self.depth = depth
+        self.max_time_embed_period = max_time_embed_period
+
+        time_mix_d_head = d_head
+        n_time_mix_heads = n_heads
+
+        time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
+
+        inner_dim = n_heads * d_head
+        if use_spatial_context:
+            time_context_dim = context_dim
+
+        self.time_stack = nn.ModuleList(
+            [
+                VideoTransformerBlock(
+                    inner_dim,
+                    n_time_mix_heads,
+                    time_mix_d_head,
+                    dropout=dropout,
+                    context_dim=time_context_dim,
+                    timesteps=timesteps,
+                    checkpoint=checkpoint,
+                    ff_in=ff_in,
+                    inner_dim=time_mix_inner_dim,
+                    attn_mode=attn_mode,
+                    disable_self_attn=disable_self_attn,
+                    disable_temporal_crossattention=disable_temporal_crossattention,
+                )
+                for _ in range(self.depth)
+            ]
+        )
+
+        assert len(self.time_stack) == len(self.transformer_blocks)
+
+        self.use_spatial_context = use_spatial_context
+        self.in_channels = in_channels
+
+        time_embed_dim = self.in_channels * 4
+        self.time_pos_embed = nn.Sequential(
+            linear(self.in_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, self.in_channels),
+        )
+
+        self.time_mixer = AlphaBlender(
+            alpha=merge_factor, merge_strategy=merge_strategy
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        time_context: Optional[torch.Tensor] = None,
+        timesteps: Optional[int] = None,
+        image_only_indicator: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        _, _, h, w = x.shape
+        x_in = x
+        spatial_context = None
+        if exists(context):
+            spatial_context = context
+
+        if self.use_spatial_context:
+            assert (
+                context.ndim == 3
+            ), f"n dims of spatial context should be 3 but are {context.ndim}"
+
+            time_context = context
+            time_context_first_timestep = time_context[::timesteps]
+            time_context = repeat(
+                time_context_first_timestep, "b ... -> (b n) ...", n=h * w
+            )
+        elif time_context is not None and not self.use_spatial_context:
+            time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
+            if time_context.ndim == 2:
+                time_context = rearrange(time_context, "b c -> b 1 c")
+
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, "b c h w -> b (h w) c")
+        if self.use_linear:
+            x = self.proj_in(x)
+
+        num_frames = torch.arange(timesteps, device=x.device)
+        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+        num_frames = rearrange(num_frames, "b t -> (b t)")
+        t_emb = timestep_embedding(
+            num_frames,
+            self.in_channels,
+            repeat_only=False,
+            max_period=self.max_time_embed_period,
+        )
+        emb = self.time_pos_embed(t_emb)
+        emb = emb[:, None, :]
+
+        for it_, (block, mix_block) in enumerate(
+            zip(self.transformer_blocks, self.time_stack)
+        ):
+            x = block(
+                x,
+                context=spatial_context,
+            )
+
+            x_mix = x
+            x_mix = x_mix + emb
+
+            x_mix = mix_block(x_mix, context=time_context, timesteps=timesteps)
+            x = self.time_mixer(
+                x_spatial=x,
+                x_temporal=x_mix,
+                image_only_indicator=image_only_indicator,
+            )
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        if not self.use_linear:
+            x = self.proj_out(x)
+        out = x + x_in
+        return out
diff --git a/sgm/sampling_utils/__init__.py b/sgm/sampling_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..71e6591b99a458a239dda8fc553708ad8da57263
--- /dev/null
+++ b/sgm/sampling_utils/__init__.py
@@ -0,0 +1 @@
+# originally generative-models/scripts, for sampling stuffs
\ No newline at end of file
diff --git a/sgm/sampling_utils/demo/__init__.py b/sgm/sampling_utils/demo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/sgm/sampling_utils/demo/detect.py b/sgm/sampling_utils/demo/detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e9f212b868e60602b0b1880a0f3d7c66e16703
--- /dev/null
+++ b/sgm/sampling_utils/demo/detect.py
@@ -0,0 +1,156 @@
+import argparse
+
+import cv2
+import numpy as np
+
+try:
+    from imwatermark import WatermarkDecoder
+except ImportError as e:
+    try:
+        # Assume some of the other dependencies such as torch are not fulfilled
+        # import file without loading unnecessary libraries.
+        import importlib.util
+        import sys
+
+        spec = importlib.util.find_spec("imwatermark.maxDct")
+        assert spec is not None
+        maxDct = importlib.util.module_from_spec(spec)
+        sys.modules["maxDct"] = maxDct
+        spec.loader.exec_module(maxDct)
+
+        class WatermarkDecoder(object):
+            """A minimal version of
+            https://github.com/ShieldMnt/invisible-watermark/blob/main/imwatermark/watermark.py
+            to only reconstruct bits using dwtDct"""
+
+            def __init__(self, wm_type="bytes", length=0):
+                assert wm_type == "bits", "Only bits defined in minimal import"
+                self._wmType = wm_type
+                self._wmLen = length
+
+            def reconstruct(self, bits):
+                if len(bits) != self._wmLen:
+                    raise RuntimeError("bits are not matched with watermark length")
+
+                return bits
+
+            def decode(self, cv2Image, method="dwtDct", **configs):
+                (r, c, channels) = cv2Image.shape
+                if r * c < 256 * 256:
+                    raise RuntimeError("image too small, should be larger than 256x256")
+
+                bits = []
+                assert method == "dwtDct"
+                embed = maxDct.EmbedMaxDct(watermarks=[], wmLen=self._wmLen, **configs)
+                bits = embed.decode(cv2Image)
+                return self.reconstruct(bits)
+
+    except:
+        raise e
+
+
+# A fixed 48-bit message that was choosen at random
+# WATERMARK_MESSAGE = 0xB3EC907BB19E
+WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
+# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
+WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
+MATCH_VALUES = [
+    [27, "No watermark detected"],
+    [33, "Partial watermark match. Cannot determine with certainty."],
+    [
+        35,
+        (
+            "Likely watermarked. In our test 0.02% of real images were "
+            'falsely detected as "Likely watermarked"'
+        ),
+    ],
+    [
+        49,
+        (
+            "Very likely watermarked. In our test no real images were "
+            'falsely detected as "Very likely watermarked"'
+        ),
+    ],
+]
+
+
+class GetWatermarkMatch:
+    def __init__(self, watermark):
+        self.watermark = watermark
+        self.num_bits = len(self.watermark)
+        self.decoder = WatermarkDecoder("bits", self.num_bits)
+
+    def __call__(self, x: np.ndarray) -> np.ndarray:
+        """
+        Detects the number of matching bits the predefined watermark with one
+        or multiple images. Images should be in cv2 format, e.g. h x w x c BGR.
+
+        Args:
+            x: ([B], h w, c) in range [0, 255]
+
+        Returns:
+           number of matched bits ([B],)
+        """
+        squeeze = len(x.shape) == 3
+        if squeeze:
+            x = x[None, ...]
+
+        bs = x.shape[0]
+        detected = np.empty((bs, self.num_bits), dtype=bool)
+        for k in range(bs):
+            detected[k] = self.decoder.decode(x[k], "dwtDct")
+        result = np.sum(detected == self.watermark, axis=-1)
+        if squeeze:
+            return result[0]
+        else:
+            return result
+
+
+get_watermark_match = GetWatermarkMatch(WATERMARK_BITS)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "filename",
+        nargs="+",
+        type=str,
+        help="Image files to check for watermarks",
+    )
+    opts = parser.parse_args()
+
+    print(
+        """
+        This script tries to detect watermarked images. Please be aware of
+        the following:
+        - As the watermark is supposed to be invisible, there is the risk that
+          watermarked images may not be detected.
+        - To maximize the chance of detection make sure that the image has the same
+          dimensions as when the watermark was applied (most likely 1024x1024
+          or 512x512).
+        - Specific image manipulation may drastically decrease the chance that
+          watermarks can be detected.
+        - There is also the chance that an image has the characteristics of the
+          watermark by chance.
+        - The watermark script is public, anybody may watermark any images, and
+          could therefore claim it to be generated.
+        - All numbers below are based on a test using 10,000 images without any
+          modifications after applying the watermark.
+        """
+    )
+
+    for fn in opts.filename:
+        image = cv2.imread(fn)
+        if image is None:
+            print(f"Couldn't read {fn}. Skipping")
+            continue
+
+        num_bits = get_watermark_match(image)
+        k = 0
+        while num_bits > MATCH_VALUES[k][0]:
+            k += 1
+        print(
+            f"{fn}: {MATCH_VALUES[k][1]}",
+            f"Bits that matched the watermark {num_bits} from {len(WATERMARK_BITS)}\n",
+            sep="\n\t",
+        )
diff --git a/sgm/sampling_utils/demo/discretization.py b/sgm/sampling_utils/demo/discretization.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7030a229692e25ddf8d2f516aef2aba0e6149b9
--- /dev/null
+++ b/sgm/sampling_utils/demo/discretization.py
@@ -0,0 +1,59 @@
+import torch
+
+from sgm.modules.diffusionmodules.discretizer import Discretization
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization: Discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+class Txt2NoisyDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(
+        self, discretization: Discretization, strength: float = 0.0, original_steps=None
+    ):
+        self.discretization = discretization
+        self.strength = strength
+        self.original_steps = original_steps
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        if self.original_steps is None:
+            steps = len(sigmas)
+        else:
+            steps = self.original_steps + 1
+        prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
+        sigmas = sigmas[prune_index:]
+        print("prune index:", prune_index)
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
diff --git a/sgm/sampling_utils/demo/gradio_app.py b/sgm/sampling_utils/demo/gradio_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab7c9d302d8ac5d18acda713a1814b3bcbc457d9
--- /dev/null
+++ b/sgm/sampling_utils/demo/gradio_app.py
@@ -0,0 +1,308 @@
+# Adding this at the very top of app.py to make 'generative-models' directory discoverable
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "generative-models"))
+
+import math
+import random
+import uuid
+from glob import glob
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import gradio as gr
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from fire import Fire
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision.transforms import ToTensor
+
+from scripts.sampling.simple_video_sample import (
+    get_batch, get_unique_embedder_keys_from_conditioner, load_model)
+from scripts.util.detection.nsfw_and_watermark_dectection import \
+    DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.util import default, instantiate_from_config
+
+# To download all svd models
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints")
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid", filename="svd.safetensors", local_dir="checkpoints")
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt-1-1", filename="svd_xt_1_1.safetensors", local_dir="checkpoints")
+
+
+# Define the repo, local directory and filename
+repo_id = "stabilityai/stable-video-diffusion-img2vid-xt-1-1"  # replace with "stabilityai/stable-video-diffusion-img2vid-xt" or "stabilityai/stable-video-diffusion-img2vid" for other models
+filename = "svd_xt_1_1.safetensors"  # replace with "svd_xt.safetensors" or "svd.safetensors" for other models
+local_dir = "checkpoints"
+local_file_path = os.path.join(local_dir, filename)
+
+# Check if the file already exists
+if not os.path.exists(local_file_path):
+    # If the file doesn't exist, download it
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+    print("File downloaded.")
+else:
+    print("File already exists. No need to download.")
+
+
+version = "svd_xt_1_1"  # replace with 'svd_xt' or 'svd' for other models
+device = "cuda"
+max_64_bit_int = 2**63 - 1
+
+if version == "svd_xt_1_1":
+    num_frames = 25
+    num_steps = 30
+    model_config = "scripts/sampling/configs/svd_xt_1_1.yaml"
+else:
+    raise ValueError(f"Version {version} does not exist.")
+
+model, filter = load_model(
+    model_config,
+    device,
+    num_frames,
+    num_steps,
+)
+
+
+def sample(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    seed: Optional[int] = None,
+    randomize_seed: bool = True,
+    motion_bucket_id: int = 127,
+    fps_id: int = 6,
+    version: str = "svd_xt_1_1",
+    cond_aug: float = 0.02,
+    decoding_t: int = 7,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    device: str = "cuda",
+    output_folder: str = "outputs",
+    progress=gr.Progress(track_tqdm=True),
+):
+    """
+    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+    fps_id = int(fps_id)  # casting float slider values to int)
+    if randomize_seed:
+        seed = random.randint(0, max_64_bit_int)
+
+    torch.manual_seed(seed)
+
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+        with Image.open(input_img_path) as image:
+            if image.mode == "RGBA":
+                image = image.convert("RGB")
+            w, h = image.size
+
+            if h % 64 != 0 or w % 64 != 0:
+                width, height = map(lambda x: x - x % 64, (w, h))
+                image = image.resize((width, height))
+                print(
+                    f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
+                )
+
+            image = ToTensor()(image)
+            image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 1024):
+            print(
+                "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
+            )
+        if motion_bucket_id > 255:
+            print(
+                "WARNING: High motion bucket! This may lead to suboptimal performance."
+            )
+
+        if fps_id < 5:
+            print("WARNING: Small fps value! This may lead to suboptimal performance.")
+
+        if fps_id > 30:
+            print("WARNING: Large fps value! This may lead to suboptimal performance.")
+
+        value_dict = {}
+        value_dict["motion_bucket_id"] = motion_bucket_id
+        value_dict["fps_id"] = fps_id
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        value_dict["cond_aug"] = cond_aug
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                writer = cv2.VideoWriter(
+                    video_path,
+                    cv2.VideoWriter_fourcc(*"mp4v"),
+                    fps_id + 1,
+                    (samples.shape[-1], samples.shape[-2]),
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                for frame in vid:
+                    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+                    writer.write(frame)
+                writer.release()
+
+        return video_path, seed
+
+
+def resize_image(image_path, output_size=(1024, 576)):
+    image = Image.open(image_path)
+    # Calculate aspect ratios
+    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
+    image_aspect = image.width / image.height  # Aspect ratio of the original image
+
+    # Resize then crop if the original image is larger
+    if image_aspect > target_aspect:
+        # Resize the image to match the target height, maintaining aspect ratio
+        new_height = output_size[1]
+        new_width = int(new_height * image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = (new_width - output_size[0]) / 2
+        top = 0
+        right = (new_width + output_size[0]) / 2
+        bottom = output_size[1]
+    else:
+        # Resize the image to match the target width, maintaining aspect ratio
+        new_width = output_size[0]
+        new_height = int(new_width / image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = 0
+        top = (new_height - output_size[1]) / 2
+        right = output_size[0]
+        bottom = (new_height + output_size[1]) / 2
+
+    # Crop the image
+    cropped_image = resized_image.crop((left, top, right, bottom))
+
+    return cropped_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets))
+#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). Generation takes ~60s in an A100. [Join the waitlist for Stability's upcoming web experience](https://stability.ai/contact).
+  """
+    )
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="filepath")
+            generate_btn = gr.Button("Generate")
+        video = gr.Video()
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=42,
+            randomize=True,
+            minimum=0,
+            maximum=max_64_bit_int,
+            step=1,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        motion_bucket_id = gr.Slider(
+            label="Motion bucket id",
+            info="Controls how much motion to add/remove from the image",
+            value=127,
+            minimum=1,
+            maximum=255,
+        )
+        fps_id = gr.Slider(
+            label="Frames per second",
+            info="The length of your video in seconds will be 25/fps",
+            value=6,
+            minimum=5,
+            maximum=30,
+        )
+
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+    generate_btn.click(
+        fn=sample,
+        inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id],
+        outputs=[video, seed],
+        api_name="video",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20)
+    demo.launch(share=True)
diff --git a/sgm/sampling_utils/demo/sampling.py b/sgm/sampling_utils/demo/sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..46c20048176aeb0db96114f95d760091a46d49cd
--- /dev/null
+++ b/sgm/sampling_utils/demo/sampling.py
@@ -0,0 +1,364 @@
+from pytorch_lightning import seed_everything
+
+from scripts.demo.streamlit_helpers import *
+
+SAVE_PATH = "outputs/demo/txt2img/"
+
+SD_XL_BASE_RATIOS = {
+    "0.5": (704, 1408),
+    "0.52": (704, 1344),
+    "0.57": (768, 1344),
+    "0.6": (768, 1280),
+    "0.68": (832, 1216),
+    "0.72": (832, 1152),
+    "0.78": (896, 1152),
+    "0.82": (896, 1088),
+    "0.88": (960, 1088),
+    "0.94": (960, 1024),
+    "1.0": (1024, 1024),
+    "1.07": (1024, 960),
+    "1.13": (1088, 960),
+    "1.21": (1088, 896),
+    "1.29": (1152, 896),
+    "1.38": (1152, 832),
+    "1.46": (1216, 832),
+    "1.67": (1280, 768),
+    "1.75": (1344, 768),
+    "1.91": (1344, 704),
+    "2.0": (1408, 704),
+    "2.09": (1472, 704),
+    "2.4": (1536, 640),
+    "2.5": (1600, 640),
+    "2.89": (1664, 576),
+    "3.0": (1728, 576),
+}
+
+VERSION2SPECS = {
+    "SDXL-base-1.0": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_base_1.0.safetensors",
+    },
+    "SDXL-base-0.9": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_base_0.9.safetensors",
+    },
+    "SD-2.1": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_2_1.yaml",
+        "ckpt": "checkpoints/v2-1_512-ema-pruned.safetensors",
+    },
+    "SD-2.1-768": {
+        "H": 768,
+        "W": 768,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_2_1_768.yaml",
+        "ckpt": "checkpoints/v2-1_768-ema-pruned.safetensors",
+    },
+    "SDXL-refiner-0.9": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_xl_refiner.yaml",
+        "ckpt": "checkpoints/sd_xl_refiner_0.9.safetensors",
+    },
+    "SDXL-refiner-1.0": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_xl_refiner.yaml",
+        "ckpt": "checkpoints/sd_xl_refiner_1.0.safetensors",
+    },
+}
+
+
+def load_img(display=True, key=None, device="cuda"):
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+    width, height = map(
+        lambda x: x - x % 64, (w, h)
+    )  # resize to integer multiple of 64
+    image = image.resize((width, height))
+    image = np.array(image.convert("RGB"))
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+    return image.to(device)
+
+
+def run_txt2img(
+    state,
+    version,
+    version_dict,
+    is_legacy=False,
+    return_latents=False,
+    filter=None,
+    stage2strength=None,
+):
+    if version.startswith("SDXL-base"):
+        W, H = st.selectbox("Resolution:", list(SD_XL_BASE_RATIOS.values()), 10)
+    else:
+        H = st.number_input("H", value=version_dict["H"], min_value=64, max_value=2048)
+        W = st.number_input("W", value=version_dict["W"], min_value=64, max_value=2048)
+    C = version_dict["C"]
+    F = version_dict["f"]
+
+    init_dict = {
+        "orig_width": W,
+        "orig_height": H,
+        "target_width": W,
+        "target_height": H,
+    }
+    value_dict = init_embedder_options(
+        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
+        init_dict,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+    )
+    sampler, num_rows, num_cols = init_sampling(stage2strength=stage2strength)
+    num_samples = num_rows * num_cols
+
+    if st.button("Sample"):
+        st.write(f"**Model I:** {version}")
+        out = do_sample(
+            state["model"],
+            sampler,
+            value_dict,
+            num_samples,
+            H,
+            W,
+            C,
+            F,
+            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
+            return_latents=return_latents,
+            filter=filter,
+        )
+        return out
+
+
+def run_img2img(
+    state,
+    version_dict,
+    is_legacy=False,
+    return_latents=False,
+    filter=None,
+    stage2strength=None,
+):
+    img = load_img()
+    if img is None:
+        return None
+    H, W = img.shape[2], img.shape[3]
+
+    init_dict = {
+        "orig_width": W,
+        "orig_height": H,
+        "target_width": W,
+        "target_height": H,
+    }
+    value_dict = init_embedder_options(
+        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
+        init_dict,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+    )
+    strength = st.number_input(
+        "**Img2Img Strength**", value=0.75, min_value=0.0, max_value=1.0
+    )
+    sampler, num_rows, num_cols = init_sampling(
+        img2img_strength=strength,
+        stage2strength=stage2strength,
+    )
+    num_samples = num_rows * num_cols
+
+    if st.button("Sample"):
+        out = do_img2img(
+            repeat(img, "1 ... -> n ...", n=num_samples),
+            state["model"],
+            sampler,
+            value_dict,
+            num_samples,
+            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
+            return_latents=return_latents,
+            filter=filter,
+        )
+        return out
+
+
+def apply_refiner(
+    input,
+    state,
+    sampler,
+    num_samples,
+    prompt,
+    negative_prompt,
+    filter=None,
+    finish_denoising=False,
+):
+    init_dict = {
+        "orig_width": input.shape[3] * 8,
+        "orig_height": input.shape[2] * 8,
+        "target_width": input.shape[3] * 8,
+        "target_height": input.shape[2] * 8,
+    }
+
+    value_dict = init_dict
+    value_dict["prompt"] = prompt
+    value_dict["negative_prompt"] = negative_prompt
+
+    value_dict["crop_coords_top"] = 0
+    value_dict["crop_coords_left"] = 0
+
+    value_dict["aesthetic_score"] = 6.0
+    value_dict["negative_aesthetic_score"] = 2.5
+
+    st.warning(f"refiner input shape: {input.shape}")
+    samples = do_img2img(
+        input,
+        state["model"],
+        sampler,
+        value_dict,
+        num_samples,
+        skip_encode=True,
+        filter=filter,
+        add_noise=not finish_denoising,
+    )
+
+    return samples
+
+
+if __name__ == "__main__":
+    st.title("Stable Diffusion")
+    version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
+    version_dict = VERSION2SPECS[version]
+    if st.checkbox("Load Model"):
+        mode = st.radio("Mode", ("txt2img", "img2img"), 0)
+    else:
+        mode = "skip"
+    st.write("__________________________")
+
+    set_lowvram_mode(st.checkbox("Low vram mode", True))
+
+    if version.startswith("SDXL-base"):
+        add_pipeline = st.checkbox("Load SDXL-refiner?", False)
+        st.write("__________________________")
+    else:
+        add_pipeline = False
+
+    seed = st.sidebar.number_input("seed", value=42, min_value=0, max_value=int(1e9))
+    seed_everything(seed)
+
+    save_locally, save_path = init_save_locally(os.path.join(SAVE_PATH, version))
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+
+    is_legacy = version_dict["is_legacy"]
+
+    prompt = st.text_input(
+        "prompt",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    )
+    if is_legacy:
+        negative_prompt = st.text_input("negative prompt", "")
+    else:
+        negative_prompt = ""  # which is unused
+
+    stage2strength = None
+    finish_denoising = False
+
+    if add_pipeline:
+        st.write("__________________________")
+        version2 = st.selectbox("Refiner:", ["SDXL-refiner-1.0", "SDXL-refiner-0.9"])
+        st.warning(
+            f"Running with {version2} as the second stage model. Make sure to provide (V)RAM :) "
+        )
+        st.write("**Refiner Options:**")
+
+        version_dict2 = VERSION2SPECS[version2]
+        state2 = init_st(version_dict2, load_filter=False)
+        st.info(state2["msg"])
+
+        stage2strength = st.number_input(
+            "**Refinement strength**", value=0.15, min_value=0.0, max_value=1.0
+        )
+
+        sampler2, *_ = init_sampling(
+            key=2,
+            img2img_strength=stage2strength,
+            specify_num_samples=False,
+        )
+        st.write("__________________________")
+        finish_denoising = st.checkbox("Finish denoising with refiner.", True)
+        if not finish_denoising:
+            stage2strength = None
+
+    if mode == "txt2img":
+        out = run_txt2img(
+            state,
+            version,
+            version_dict,
+            is_legacy=is_legacy,
+            return_latents=add_pipeline,
+            filter=state.get("filter"),
+            stage2strength=stage2strength,
+        )
+    elif mode == "img2img":
+        out = run_img2img(
+            state,
+            version_dict,
+            is_legacy=is_legacy,
+            return_latents=add_pipeline,
+            filter=state.get("filter"),
+            stage2strength=stage2strength,
+        )
+    elif mode == "skip":
+        out = None
+    else:
+        raise ValueError(f"unknown mode {mode}")
+    if isinstance(out, (tuple, list)):
+        samples, samples_z = out
+    else:
+        samples = out
+        samples_z = None
+
+    if add_pipeline and samples_z is not None:
+        st.write("**Running Refinement Stage**")
+        samples = apply_refiner(
+            samples_z,
+            state2,
+            sampler2,
+            samples_z.shape[0],
+            prompt=prompt,
+            negative_prompt=negative_prompt if is_legacy else "",
+            filter=state.get("filter"),
+            finish_denoising=finish_denoising,
+        )
+
+    if save_locally and samples is not None:
+        perform_save_locally(save_path, samples)
diff --git a/sgm/sampling_utils/demo/streamlit_helpers.py b/sgm/sampling_utils/demo/streamlit_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c5760e26fcffefc95ddc5435870b2a804257e51
--- /dev/null
+++ b/sgm/sampling_utils/demo/streamlit_helpers.py
@@ -0,0 +1,887 @@
+import copy
+import math
+import os
+from glob import glob
+from typing import Dict, List, Optional, Tuple, Union
+
+import cv2
+import numpy as np
+import streamlit as st
+import torch
+import torch.nn as nn
+import torchvision.transforms as TT
+from einops import rearrange, repeat
+from imwatermark import WatermarkEncoder
+from omegaconf import ListConfig, OmegaConf
+from PIL import Image
+from safetensors.torch import load_file as load_safetensors
+from torch import autocast
+from torchvision import transforms
+from torchvision.utils import make_grid, save_image
+
+from scripts.demo.discretization import (Img2ImgDiscretizationWrapper,
+                                         Txt2NoisyDiscretizationWrapper)
+from scripts.util.detection.nsfw_and_watermark_dectection import \
+    DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.modules.diffusionmodules.guiders import (LinearPredictionGuider,
+                                                  VanillaCFG)
+from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
+                                                   DPMPP2SAncestralSampler,
+                                                   EulerAncestralSampler,
+                                                   EulerEDMSampler,
+                                                   HeunEDMSampler,
+                                                   LinearMultistepSampler)
+from sgm.util import append_dims, default, instantiate_from_config
+
+
+@st.cache_resource()
+def init_st(version_dict, load_ckpt=True, load_filter=True):
+    state = dict()
+    if not "model" in state:
+        config = version_dict["config"]
+        ckpt = version_dict["ckpt"]
+
+        config = OmegaConf.load(config)
+        model, msg = load_model_from_config(config, ckpt if load_ckpt else None)
+
+        state["msg"] = msg
+        state["model"] = model
+        state["ckpt"] = ckpt if load_ckpt else None
+        state["config"] = config
+        if load_filter:
+            state["filter"] = DeepFloydDataFiltering(verbose=False)
+    return state
+
+
+def load_model(model):
+    model.cuda()
+
+
+lowvram_mode = False
+
+
+def set_lowvram_mode(mode):
+    global lowvram_mode
+    lowvram_mode = mode
+
+
+def initial_model_load(model):
+    global lowvram_mode
+    if lowvram_mode:
+        model.model.half()
+    else:
+        model.cuda()
+    return model
+
+
+def unload_model(model):
+    global lowvram_mode
+    if lowvram_mode:
+        model.cpu()
+        torch.cuda.empty_cache()
+
+
+def load_model_from_config(config, ckpt=None, verbose=True):
+    model = instantiate_from_config(config.model)
+
+    if ckpt is not None:
+        print(f"Loading model from {ckpt}")
+        if ckpt.endswith("ckpt"):
+            pl_sd = torch.load(ckpt, map_location="cpu")
+            if "global_step" in pl_sd:
+                global_step = pl_sd["global_step"]
+                st.info(f"loaded ckpt from global step {global_step}")
+                print(f"Global Step: {pl_sd['global_step']}")
+            sd = pl_sd["state_dict"]
+        elif ckpt.endswith("safetensors"):
+            sd = load_safetensors(ckpt)
+        else:
+            raise NotImplementedError
+
+        msg = None
+
+        m, u = model.load_state_dict(sd, strict=False)
+
+        if len(m) > 0 and verbose:
+            print("missing keys:")
+            print(m)
+        if len(u) > 0 and verbose:
+            print("unexpected keys:")
+            print(u)
+    else:
+        msg = None
+
+    model = initial_model_load(model)
+    model.eval()
+    return model, msg
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list(set([x.input_key for x in conditioner.embedders]))
+
+
+def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
+    # Hardcoded demo settings; might undergo some changes in the future
+
+    value_dict = {}
+    for key in keys:
+        if key == "txt":
+            if prompt is None:
+                prompt = "A professional photograph of an astronaut riding a pig"
+            if negative_prompt is None:
+                negative_prompt = ""
+
+            prompt = st.text_input("Prompt", prompt)
+            negative_prompt = st.text_input("Negative prompt", negative_prompt)
+
+            value_dict["prompt"] = prompt
+            value_dict["negative_prompt"] = negative_prompt
+
+        if key == "original_size_as_tuple":
+            orig_width = st.number_input(
+                "orig_width",
+                value=init_dict["orig_width"],
+                min_value=16,
+            )
+            orig_height = st.number_input(
+                "orig_height",
+                value=init_dict["orig_height"],
+                min_value=16,
+            )
+
+            value_dict["orig_width"] = orig_width
+            value_dict["orig_height"] = orig_height
+
+        if key == "crop_coords_top_left":
+            crop_coord_top = st.number_input("crop_coords_top", value=0, min_value=0)
+            crop_coord_left = st.number_input("crop_coords_left", value=0, min_value=0)
+
+            value_dict["crop_coords_top"] = crop_coord_top
+            value_dict["crop_coords_left"] = crop_coord_left
+
+        if key == "aesthetic_score":
+            value_dict["aesthetic_score"] = 6.0
+            value_dict["negative_aesthetic_score"] = 2.5
+
+        if key == "target_size_as_tuple":
+            value_dict["target_width"] = init_dict["target_width"]
+            value_dict["target_height"] = init_dict["target_height"]
+
+        if key in ["fps_id", "fps"]:
+            fps = st.number_input("fps", value=6, min_value=1)
+
+            value_dict["fps"] = fps
+            value_dict["fps_id"] = fps - 1
+
+        if key == "motion_bucket_id":
+            mb_id = st.number_input("motion bucket id", 0, 511, value=127)
+            value_dict["motion_bucket_id"] = mb_id
+
+        if key == "pool_image":
+            st.text("Image for pool conditioning")
+            image = load_img(
+                key="pool_image_input",
+                size=224,
+                center_crop=True,
+            )
+            if image is None:
+                st.info("Need an image here")
+                image = torch.zeros(1, 3, 224, 224)
+            value_dict["pool_image"] = image
+
+    return value_dict
+
+
+def perform_save_locally(save_path, samples):
+    os.makedirs(os.path.join(save_path), exist_ok=True)
+    base_count = len(os.listdir(os.path.join(save_path)))
+    samples = embed_watermark(samples)
+    for sample in samples:
+        sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
+        Image.fromarray(sample.astype(np.uint8)).save(
+            os.path.join(save_path, f"{base_count:09}.png")
+        )
+        base_count += 1
+
+
+def init_save_locally(_dir, init_value: bool = False):
+    save_locally = st.sidebar.checkbox("Save images locally", value=init_value)
+    if save_locally:
+        save_path = st.text_input("Save path", value=os.path.join(_dir, "samples"))
+    else:
+        save_path = None
+
+    return save_locally, save_path
+
+
+def get_guider(options, key):
+    guider = st.sidebar.selectbox(
+        f"Discretization #{key}",
+        [
+            "VanillaCFG",
+            "IdentityGuider",
+            "LinearPredictionGuider",
+        ],
+        options.get("guider", 0),
+    )
+
+    additional_guider_kwargs = options.pop("additional_guider_kwargs", {})
+
+    if guider == "IdentityGuider":
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
+        }
+    elif guider == "VanillaCFG":
+        scale = st.number_input(
+            f"cfg-scale #{key}",
+            value=options.get("cfg", 5.0),
+            min_value=0.0,
+        )
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
+            "params": {
+                "scale": scale,
+                **additional_guider_kwargs,
+            },
+        }
+    elif guider == "LinearPredictionGuider":
+        max_scale = st.number_input(
+            f"max-cfg-scale #{key}",
+            value=options.get("cfg", 1.5),
+            min_value=1.0,
+        )
+        min_scale = st.number_input(
+            f"min guidance scale",
+            value=options.get("min_cfg", 1.0),
+            min_value=1.0,
+            max_value=10.0,
+        )
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.LinearPredictionGuider",
+            "params": {
+                "max_scale": max_scale,
+                "min_scale": min_scale,
+                "num_frames": options["num_frames"],
+                **additional_guider_kwargs,
+            },
+        }
+    else:
+        raise NotImplementedError
+    return guider_config
+
+
+def init_sampling(
+    key=1,
+    img2img_strength: Optional[float] = None,
+    specify_num_samples: bool = True,
+    stage2strength: Optional[float] = None,
+    options: Optional[Dict[str, int]] = None,
+):
+    options = {} if options is None else options
+
+    num_rows, num_cols = 1, 1
+    if specify_num_samples:
+        num_cols = st.number_input(
+            f"num cols #{key}", value=num_cols, min_value=1, max_value=10
+        )
+
+    steps = st.sidebar.number_input(
+        f"steps #{key}", value=options.get("num_steps", 40), min_value=1, max_value=1000
+    )
+    sampler = st.sidebar.selectbox(
+        f"Sampler #{key}",
+        [
+            "EulerEDMSampler",
+            "HeunEDMSampler",
+            "EulerAncestralSampler",
+            "DPMPP2SAncestralSampler",
+            "DPMPP2MSampler",
+            "LinearMultistepSampler",
+        ],
+        options.get("sampler", 0),
+    )
+    discretization = st.sidebar.selectbox(
+        f"Discretization #{key}",
+        [
+            "LegacyDDPMDiscretization",
+            "EDMDiscretization",
+        ],
+        options.get("discretization", 0),
+    )
+
+    discretization_config = get_discretization(discretization, options=options, key=key)
+
+    guider_config = get_guider(options=options, key=key)
+
+    sampler = get_sampler(sampler, steps, discretization_config, guider_config, key=key)
+    if img2img_strength is not None:
+        st.warning(
+            f"Wrapping {sampler.__class__.__name__} with Img2ImgDiscretizationWrapper"
+        )
+        sampler.discretization = Img2ImgDiscretizationWrapper(
+            sampler.discretization, strength=img2img_strength
+        )
+    if stage2strength is not None:
+        sampler.discretization = Txt2NoisyDiscretizationWrapper(
+            sampler.discretization, strength=stage2strength, original_steps=steps
+        )
+    return sampler, num_rows, num_cols
+
+
+def get_discretization(discretization, options, key=1):
+    if discretization == "LegacyDDPMDiscretization":
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
+        }
+    elif discretization == "EDMDiscretization":
+        sigma_min = st.number_input(
+            f"sigma_min #{key}", value=options.get("sigma_min", 0.03)
+        )  # 0.0292
+        sigma_max = st.number_input(
+            f"sigma_max #{key}", value=options.get("sigma_max", 14.61)
+        )  # 14.6146
+        rho = st.number_input(f"rho #{key}", value=options.get("rho", 3.0))
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
+            "params": {
+                "sigma_min": sigma_min,
+                "sigma_max": sigma_max,
+                "rho": rho,
+            },
+        }
+
+    return discretization_config
+
+
+def get_sampler(sampler_name, steps, discretization_config, guider_config, key=1):
+    if sampler_name == "EulerEDMSampler" or sampler_name == "HeunEDMSampler":
+        s_churn = st.sidebar.number_input(f"s_churn #{key}", value=0.0, min_value=0.0)
+        s_tmin = st.sidebar.number_input(f"s_tmin #{key}", value=0.0, min_value=0.0)
+        s_tmax = st.sidebar.number_input(f"s_tmax #{key}", value=999.0, min_value=0.0)
+        s_noise = st.sidebar.number_input(f"s_noise #{key}", value=1.0, min_value=0.0)
+
+        if sampler_name == "EulerEDMSampler":
+            sampler = EulerEDMSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                s_churn=s_churn,
+                s_tmin=s_tmin,
+                s_tmax=s_tmax,
+                s_noise=s_noise,
+                verbose=True,
+            )
+        elif sampler_name == "HeunEDMSampler":
+            sampler = HeunEDMSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                s_churn=s_churn,
+                s_tmin=s_tmin,
+                s_tmax=s_tmax,
+                s_noise=s_noise,
+                verbose=True,
+            )
+    elif (
+        sampler_name == "EulerAncestralSampler"
+        or sampler_name == "DPMPP2SAncestralSampler"
+    ):
+        s_noise = st.sidebar.number_input("s_noise", value=1.0, min_value=0.0)
+        eta = st.sidebar.number_input("eta", value=1.0, min_value=0.0)
+
+        if sampler_name == "EulerAncestralSampler":
+            sampler = EulerAncestralSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                eta=eta,
+                s_noise=s_noise,
+                verbose=True,
+            )
+        elif sampler_name == "DPMPP2SAncestralSampler":
+            sampler = DPMPP2SAncestralSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                eta=eta,
+                s_noise=s_noise,
+                verbose=True,
+            )
+    elif sampler_name == "DPMPP2MSampler":
+        sampler = DPMPP2MSampler(
+            num_steps=steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            verbose=True,
+        )
+    elif sampler_name == "LinearMultistepSampler":
+        order = st.sidebar.number_input("order", value=4, min_value=1)
+        sampler = LinearMultistepSampler(
+            num_steps=steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            order=order,
+            verbose=True,
+        )
+    else:
+        raise ValueError(f"unknown sampler {sampler_name}!")
+
+    return sampler
+
+
+def get_interactive_image() -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
+    if image is not None:
+        image = Image.open(image)
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        return image
+
+
+def load_img(
+    display: bool = True,
+    size: Union[None, int, Tuple[int, int]] = None,
+    center_crop: bool = False,
+):
+    image = get_interactive_image()
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+
+    transform = []
+    if size is not None:
+        transform.append(transforms.Resize(size))
+    if center_crop:
+        transform.append(transforms.CenterCrop(size))
+    transform.append(transforms.ToTensor())
+    transform.append(transforms.Lambda(lambda x: 2.0 * x - 1.0))
+
+    transform = transforms.Compose(transform)
+    img = transform(image)[None, ...]
+    st.text(f"input min/max/mean: {img.min():.3f}/{img.max():.3f}/{img.mean():.3f}")
+    return img
+
+
+def get_init_img(batch_size=1, key=None):
+    init_image = load_img(key=key).cuda()
+    init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
+    return init_image
+
+
+def do_sample(
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    H,
+    W,
+    C,
+    F,
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
+    batch2model_input: List = None,
+    return_latents=False,
+    filter=None,
+    T=None,
+    additional_batch_uc_fields=None,
+    decoding_t=None,
+):
+    force_uc_zero_embeddings = default(force_uc_zero_embeddings, [])
+    batch2model_input = default(batch2model_input, [])
+    additional_batch_uc_fields = default(additional_batch_uc_fields, [])
+
+    st.text("Sampling")
+
+    outputs = st.empty()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            with model.ema_scope():
+                if T is not None:
+                    num_samples = [num_samples, T]
+                else:
+                    num_samples = [num_samples]
+
+                load_model(model.conditioner)
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    num_samples,
+                    T=T,
+                    additional_batch_uc_fields=additional_batch_uc_fields,
+                )
+
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
+                )
+                unload_model(model.conditioner)
+
+                for k in c:
+                    if not k == "crossattn":
+                        c[k], uc[k] = map(
+                            lambda y: y[k][: math.prod(num_samples)].to("cuda"), (c, uc)
+                        )
+                    if k in ["crossattn", "concat"] and T is not None:
+                        uc[k] = repeat(uc[k], "b ... -> b t ...", t=T)
+                        uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=T)
+                        c[k] = repeat(c[k], "b ... -> b t ...", t=T)
+                        c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=T)
+
+                additional_model_inputs = {}
+                for k in batch2model_input:
+                    if k == "image_only_indicator":
+                        assert T is not None
+
+                        if isinstance(
+                            sampler.guider, (VanillaCFG, LinearPredictionGuider)
+                        ):
+                            additional_model_inputs[k] = torch.zeros(
+                                num_samples[0] * 2, num_samples[1]
+                            ).to("cuda")
+                        else:
+                            additional_model_inputs[k] = torch.zeros(num_samples).to(
+                                "cuda"
+                            )
+                    else:
+                        additional_model_inputs[k] = batch[k]
+
+                shape = (math.prod(num_samples), C, H // F, W // F)
+                randn = torch.randn(shape).to("cuda")
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                load_model(model.denoiser)
+                load_model(model.model)
+                samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                unload_model(model.model)
+                unload_model(model.denoiser)
+
+                load_model(model.first_stage_model)
+                model.en_and_decode_n_samples_a_time = (
+                    decoding_t  # Decode n frames at a time
+                )
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+                unload_model(model.first_stage_model)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if T is None:
+                    grid = torch.stack([samples])
+                    grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
+                    outputs.image(grid.cpu().numpy())
+                else:
+                    as_vids = rearrange(samples, "(b t) c h w -> b t c h w", t=T)
+                    for i, vid in enumerate(as_vids):
+                        grid = rearrange(make_grid(vid, nrow=4), "c h w -> h w c")
+                        st.image(
+                            grid.cpu().numpy(),
+                            f"Sample #{i} as image",
+                        )
+
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_batch(
+    keys,
+    value_dict: dict,
+    N: Union[List, ListConfig],
+    device: str = "cuda",
+    T: int = None,
+    additional_batch_uc_fields: List[str] = [],
+):
+    # Hardcoded demo setups; might undergo some changes in the future
+
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "txt":
+            batch["txt"] = [value_dict["prompt"]] * math.prod(N)
+
+            batch_uc["txt"] = [value_dict["negative_prompt"]] * math.prod(N)
+
+        elif key == "original_size_as_tuple":
+            batch["original_size_as_tuple"] = (
+                torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "crop_coords_top_left":
+            batch["crop_coords_top_left"] = (
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "aesthetic_score":
+            batch["aesthetic_score"] = (
+                torch.tensor([value_dict["aesthetic_score"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+            batch_uc["aesthetic_score"] = (
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+
+        elif key == "target_size_as_tuple":
+            batch["target_size_as_tuple"] = (
+                torch.tensor([value_dict["target_height"], value_dict["target_width"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "fps":
+            batch[key] = (
+                torch.tensor([value_dict["fps"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "fps_id":
+            batch[key] = (
+                torch.tensor([value_dict["fps_id"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "motion_bucket_id":
+            batch[key] = (
+                torch.tensor([value_dict["motion_bucket_id"]])
+                .to(device)
+                .repeat(math.prod(N))
+            )
+        elif key == "pool_image":
+            batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=math.prod(N)).to(
+                device, dtype=torch.half
+            )
+        elif key == "cond_aug":
+            batch[key] = repeat(
+                torch.tensor([value_dict["cond_aug"]]).to("cuda"),
+                "1 -> b",
+                b=math.prod(N),
+            )
+        elif key == "cond_frames":
+            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
+        elif key == "cond_frames_without_noise":
+            batch[key] = repeat(
+                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    if T is not None:
+        batch["num_video_frames"] = T
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+        elif key in additional_batch_uc_fields and key not in batch_uc:
+            batch_uc[key] = copy.copy(batch[key])
+    return batch, batch_uc
+
+
+@torch.no_grad()
+def do_img2img(
+    img,
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
+    additional_kwargs={},
+    offset_noise_level: int = 0.0,
+    return_latents=False,
+    skip_encode=False,
+    filter=None,
+    add_noise=True,
+):
+    st.text("Sampling")
+
+    outputs = st.empty()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            with model.ema_scope():
+                load_model(model.conditioner)
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [num_samples],
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
+                )
+                unload_model(model.conditioner)
+                for k in c:
+                    c[k], uc[k] = map(lambda y: y[k][:num_samples].to("cuda"), (c, uc))
+
+                for k in additional_kwargs:
+                    c[k] = uc[k] = additional_kwargs[k]
+                if skip_encode:
+                    z = img
+                else:
+                    load_model(model.first_stage_model)
+                    z = model.encode_first_stage(img)
+                    unload_model(model.first_stage_model)
+
+                noise = torch.randn_like(z)
+
+                sigmas = sampler.discretization(sampler.num_steps).cuda()
+                sigma = sigmas[0]
+
+                st.info(f"all sigmas: {sigmas}")
+                st.info(f"noising sigma: {sigma}")
+                if offset_noise_level > 0.0:
+                    noise = noise + offset_noise_level * append_dims(
+                        torch.randn(z.shape[0], device=z.device), z.ndim
+                    )
+                if add_noise:
+                    noised_z = z + noise * append_dims(sigma, z.ndim).cuda()
+                    noised_z = noised_z / torch.sqrt(
+                        1.0 + sigmas[0] ** 2.0
+                    )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+                else:
+                    noised_z = z / torch.sqrt(1.0 + sigmas[0] ** 2.0)
+
+                def denoiser(x, sigma, c):
+                    return model.denoiser(model.model, x, sigma, c)
+
+                load_model(model.denoiser)
+                load_model(model.model)
+                samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
+                unload_model(model.model)
+                unload_model(model.denoiser)
+
+                load_model(model.first_stage_model)
+                samples_x = model.decode_first_stage(samples_z)
+                unload_model(model.first_stage_model)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
+                outputs.image(grid.cpu().numpy())
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_resizing_factor(
+    desired_shape: Tuple[int, int], current_shape: Tuple[int, int]
+) -> float:
+    r_bound = desired_shape[1] / desired_shape[0]
+    aspect_r = current_shape[1] / current_shape[0]
+    if r_bound >= 1.0:
+        if aspect_r >= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r < 1.0:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+    else:
+        if aspect_r <= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r > 1:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+
+    return factor
+
+
+def get_interactive_image(key=None) -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
+    if image is not None:
+        image = Image.open(image)
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        return image
+
+
+def load_img_for_prediction(
+    W: int, H: int, display=True, key=None, device="cuda"
+) -> torch.Tensor:
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+
+    image = np.array(image).transpose(2, 0, 1)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 255.0
+    image = image.unsqueeze(0)
+
+    rfs = get_resizing_factor((H, W), (h, w))
+    resize_size = [int(np.ceil(rfs * s)) for s in (h, w)]
+    top = (resize_size[0] - H) // 2
+    left = (resize_size[1] - W) // 2
+
+    image = torch.nn.functional.interpolate(
+        image, resize_size, mode="area", antialias=False
+    )
+    image = TT.functional.crop(image, top=top, left=left, height=H, width=W)
+
+    if display:
+        numpy_img = np.transpose(image[0].numpy(), (1, 2, 0))
+        pil_image = Image.fromarray((numpy_img * 255).astype(np.uint8))
+        st.image(pil_image)
+    return image.to(device) * 2.0 - 1.0
+
+
+def save_video_as_grid_and_mp4(
+    video_batch: torch.Tensor, save_path: str, T: int, fps: int = 5
+):
+    os.makedirs(save_path, exist_ok=True)
+    base_count = len(glob(os.path.join(save_path, "*.mp4")))
+
+    video_batch = rearrange(video_batch, "(b t) c h w -> b t c h w", t=T)
+    video_batch = embed_watermark(video_batch)
+    for vid in video_batch:
+        save_image(vid, fp=os.path.join(save_path, f"{base_count:06d}.png"), nrow=4)
+
+        video_path = os.path.join(save_path, f"{base_count:06d}.mp4")
+
+        writer = cv2.VideoWriter(
+            video_path,
+            cv2.VideoWriter_fourcc(*"MP4V"),
+            fps,
+            (vid.shape[-1], vid.shape[-2]),
+        )
+
+        vid = (
+            (rearrange(vid, "t c h w -> t h w c") * 255).cpu().numpy().astype(np.uint8)
+        )
+        for frame in vid:
+            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+            writer.write(frame)
+
+        writer.release()
+
+        video_path_h264 = video_path[:-4] + "_h264.mp4"
+        os.system(f"ffmpeg -i {video_path} -c:v libx264 {video_path_h264}")
+
+        with open(video_path_h264, "rb") as f:
+            video_bytes = f.read()
+        st.video(video_bytes)
+
+        base_count += 1
diff --git a/sgm/sampling_utils/demo/turbo.py b/sgm/sampling_utils/demo/turbo.py
new file mode 100644
index 0000000000000000000000000000000000000000..91a973aec70eae280ed21044e29141b678e6f693
--- /dev/null
+++ b/sgm/sampling_utils/demo/turbo.py
@@ -0,0 +1,234 @@
+from st_keyup import st_keyup
+from streamlit_helpers import *
+
+from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
+
+VERSION2SPECS = {
+    "SDXL-Turbo": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_turbo_1.0.safetensors",
+    },
+    "SD-Turbo": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_2_1.yaml",
+        "ckpt": "checkpoints/sd_turbo.safetensors",
+    },
+}
+
+
+class SubstepSampler(EulerAncestralSampler):
+    def __init__(self, n_sample_steps=1, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.n_sample_steps = n_sample_steps
+        self.steps_subset = [0, 100, 200, 300, 1000]
+
+    def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None):
+        sigmas = self.discretization(
+            self.num_steps if num_steps is None else num_steps, device=self.device
+        )
+        sigmas = sigmas[
+            self.steps_subset[: self.n_sample_steps] + self.steps_subset[-1:]
+        ]
+        uc = cond
+        x *= torch.sqrt(1.0 + sigmas[0] ** 2.0)
+        num_sigmas = len(sigmas)
+        s_in = x.new_ones([x.shape[0]])
+        return x, s_in, sigmas, num_sigmas, cond, uc
+
+
+def seeded_randn(shape, seed):
+    randn = np.random.RandomState(seed).randn(*shape)
+    randn = torch.from_numpy(randn).to(device="cuda", dtype=torch.float32)
+    return randn
+
+
+class SeededNoise:
+    def __init__(self, seed):
+        self.seed = seed
+
+    def __call__(self, x):
+        self.seed = self.seed + 1
+        return seeded_randn(x.shape, self.seed)
+
+
+def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
+    value_dict = {}
+    for key in keys:
+        if key == "txt":
+            value_dict["prompt"] = prompt
+            value_dict["negative_prompt"] = ""
+
+        if key == "original_size_as_tuple":
+            orig_width = init_dict["orig_width"]
+            orig_height = init_dict["orig_height"]
+
+            value_dict["orig_width"] = orig_width
+            value_dict["orig_height"] = orig_height
+
+        if key == "crop_coords_top_left":
+            crop_coord_top = 0
+            crop_coord_left = 0
+
+            value_dict["crop_coords_top"] = crop_coord_top
+            value_dict["crop_coords_left"] = crop_coord_left
+
+        if key == "aesthetic_score":
+            value_dict["aesthetic_score"] = 6.0
+            value_dict["negative_aesthetic_score"] = 2.5
+
+        if key == "target_size_as_tuple":
+            value_dict["target_width"] = init_dict["target_width"]
+            value_dict["target_height"] = init_dict["target_height"]
+
+    return value_dict
+
+
+def sample(
+    model,
+    sampler,
+    prompt="A lush garden with oversized flowers and vibrant colors, inhabited by miniature animals.",
+    H=1024,
+    W=1024,
+    seed=0,
+    filter=None,
+):
+    F = 8
+    C = 4
+    shape = (1, C, H // F, W // F)
+
+    value_dict = init_embedder_options(
+        keys=get_unique_embedder_keys_from_conditioner(model.conditioner),
+        init_dict={
+            "orig_width": W,
+            "orig_height": H,
+            "target_width": W,
+            "target_height": H,
+        },
+        prompt=prompt,
+    )
+
+    if seed is None:
+        seed = torch.seed()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            batch, batch_uc = get_batch(
+                get_unique_embedder_keys_from_conditioner(model.conditioner),
+                value_dict,
+                [1],
+            )
+            c = model.conditioner(batch)
+            uc = None
+            randn = seeded_randn(shape, seed)
+
+            def denoiser(input, sigma, c):
+                return model.denoiser(
+                    model.model,
+                    input,
+                    sigma,
+                    c,
+                )
+
+            samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+            samples_x = model.decode_first_stage(samples_z)
+            samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+            if filter is not None:
+                samples = filter(samples)
+            samples = (
+                (255 * samples)
+                .to(dtype=torch.uint8)
+                .permute(0, 2, 3, 1)
+                .detach()
+                .cpu()
+                .numpy()
+            )
+    return samples
+
+
+def v_spacer(height) -> None:
+    for _ in range(height):
+        st.write("\n")
+
+
+if __name__ == "__main__":
+    st.title("Turbo")
+
+    head_cols = st.columns([1, 1, 1])
+    with head_cols[0]:
+        version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
+        version_dict = VERSION2SPECS[version]
+
+    with head_cols[1]:
+        v_spacer(2)
+        if st.checkbox("Load Model"):
+            mode = "txt2img"
+        else:
+            mode = "skip"
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+        load_model(model)
+
+    # seed
+    if "seed" not in st.session_state:
+        st.session_state.seed = 0
+
+    def increment_counter():
+        st.session_state.seed += 1
+
+    def decrement_counter():
+        if st.session_state.seed > 0:
+            st.session_state.seed -= 1
+
+    with head_cols[2]:
+        n_steps = st.number_input(label="number of steps", min_value=1, max_value=4)
+
+    sampler = SubstepSampler(
+        n_sample_steps=1,
+        num_steps=1000,
+        eta=1.0,
+        discretization_config=dict(
+            target="sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization"
+        ),
+    )
+    sampler.n_sample_steps = n_steps
+    default_prompt = (
+        "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
+    )
+    prompt = st_keyup(
+        "Enter a value", value=default_prompt, debounce=300, key="interactive_text"
+    )
+
+    cols = st.columns([1, 5, 1])
+    if mode != "skip":
+        with cols[0]:
+            v_spacer(14)
+            st.button("↩", on_click=decrement_counter)
+        with cols[2]:
+            v_spacer(14)
+            st.button("↪", on_click=increment_counter)
+
+        sampler.noise_sampler = SeededNoise(seed=st.session_state.seed)
+        out = sample(
+            model,
+            sampler,
+            H=512,
+            W=512,
+            seed=st.session_state.seed,
+            prompt=prompt,
+            filter=state.get("filter"),
+        )
+        with cols[1]:
+            st.image(out[0])
diff --git a/sgm/sampling_utils/demo/video_sampling.py b/sgm/sampling_utils/demo/video_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..95789020110f3abaf6e5a7755a7e5910b864c6df
--- /dev/null
+++ b/sgm/sampling_utils/demo/video_sampling.py
@@ -0,0 +1,200 @@
+import os
+
+from pytorch_lightning import seed_everything
+
+from scripts.demo.streamlit_helpers import *
+
+SAVE_PATH = "outputs/demo/vid/"
+
+VERSION2SPECS = {
+    "svd": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_image_decoder": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_xt": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd_xt.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+    "svd_xt_image_decoder": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_xt_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+}
+
+
+if __name__ == "__main__":
+    st.title("Stable Video Diffusion")
+    version = st.selectbox(
+        "Model Version",
+        [k for k in VERSION2SPECS.keys()],
+        0,
+    )
+    version_dict = VERSION2SPECS[version]
+    if st.checkbox("Load Model"):
+        mode = "img2vid"
+    else:
+        mode = "skip"
+
+    H = st.sidebar.number_input(
+        "H", value=version_dict["H"], min_value=64, max_value=2048
+    )
+    W = st.sidebar.number_input(
+        "W", value=version_dict["W"], min_value=64, max_value=2048
+    )
+    T = st.sidebar.number_input(
+        "T", value=version_dict["T"], min_value=0, max_value=128
+    )
+    C = version_dict["C"]
+    F = version_dict["f"]
+    options = version_dict["options"]
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+
+        ukeys = set(
+            get_unique_embedder_keys_from_conditioner(state["model"].conditioner)
+        )
+
+        value_dict = init_embedder_options(
+            ukeys,
+            {},
+        )
+
+        value_dict["image_only_indicator"] = 0
+
+        if mode == "img2vid":
+            img = load_img_for_prediction(W, H)
+            cond_aug = st.number_input(
+                "Conditioning augmentation:", value=0.02, min_value=0.0
+            )
+            value_dict["cond_frames_without_noise"] = img
+            value_dict["cond_frames"] = img + cond_aug * torch.randn_like(img)
+            value_dict["cond_aug"] = cond_aug
+
+        seed = st.sidebar.number_input(
+            "seed", value=23, min_value=0, max_value=int(1e9)
+        )
+        seed_everything(seed)
+
+        save_locally, save_path = init_save_locally(
+            os.path.join(SAVE_PATH, version), init_value=True
+        )
+
+        options["num_frames"] = T
+
+        sampler, num_rows, num_cols = init_sampling(options=options)
+        num_samples = num_rows * num_cols
+
+        decoding_t = st.number_input(
+            "Decode t frames at a time (set small if you are low on VRAM)",
+            value=options.get("decoding_t", T),
+            min_value=1,
+            max_value=int(1e9),
+        )
+
+        if st.checkbox("Overwrite fps in mp4 generator", False):
+            saving_fps = st.number_input(
+                f"saving video at fps:", value=value_dict["fps"], min_value=1
+            )
+        else:
+            saving_fps = value_dict["fps"]
+
+        if st.button("Sample"):
+            out = do_sample(
+                model,
+                sampler,
+                value_dict,
+                num_samples,
+                H,
+                W,
+                C,
+                F,
+                T=T,
+                batch2model_input=["num_video_frames", "image_only_indicator"],
+                force_uc_zero_embeddings=options.get("force_uc_zero_embeddings", None),
+                force_cond_zero_embeddings=options.get(
+                    "force_cond_zero_embeddings", None
+                ),
+                return_latents=False,
+                decoding_t=decoding_t,
+            )
+
+            if isinstance(out, (tuple, list)):
+                samples, samples_z = out
+            else:
+                samples = out
+                samples_z = None
+
+            if save_locally:
+                save_video_as_grid_and_mp4(samples, save_path, T, fps=saving_fps)
diff --git a/sgm/util.py b/sgm/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..66d9b2a69db2898323cbf2ad26a09ac8b2facd11
--- /dev/null
+++ b/sgm/util.py
@@ -0,0 +1,275 @@
+import functools
+import importlib
+import os
+from functools import partial
+from inspect import isfunction
+
+import fsspec
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from safetensors.torch import load_file as load_safetensors
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def get_string_from_tuple(s):
+    try:
+        # Check if the string starts and ends with parentheses
+        if s[0] == "(" and s[-1] == ")":
+            # Convert the string to a tuple
+            t = eval(s)
+            # Check if the type of t is tuple
+            if type(t) == tuple:
+                return t[0]
+            else:
+                pass
+    except:
+        pass
+    return s
+
+
+def is_power_of_two(n):
+    """
+    chat.openai.com/chat
+    Return True if n is a power of 2, otherwise return False.
+
+    The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
+    The function works by first checking if n is less than or equal to 0. If n is less than or equal to 0, it can't be a power of 2, so the function returns False.
+    If n is greater than 0, the function checks whether n is a power of 2 by using a bitwise AND operation between n and n-1. If n is a power of 2, then it will have only one bit set to 1 in its binary representation. When we subtract 1 from a power of 2, all the bits to the right of that bit become 1, and the bit itself becomes 0. So, when we perform a bitwise AND between n and n-1, we get 0 if n is a power of 2, and a non-zero value otherwise.
+    Thus, if the result of the bitwise AND operation is 0, then n is a power of 2 and the function returns True. Otherwise, the function returns False.
+
+    """
+    if n <= 0:
+        return False
+    return (n & (n - 1)) == 0
+
+
+def autocast(f, enabled=True):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(
+            enabled=enabled,
+            dtype=torch.get_autocast_gpu_dtype(),
+            cache_enabled=torch.is_autocast_cache_enabled(),
+        ):
+            return f(*args, **kwargs)
+
+    return do_autocast
+
+
+def load_partial_from_config(config):
+    return partial(get_obj_from_str(config["target"]), **config.get("params", dict()))
+
+
+def log_txt_as_img(wh, xc, size=10):
+    # wh a tuple of (width, height)
+    # xc a list of captions to plot
+    b = len(xc)
+    txts = list()
+    for bi in range(b):
+        txt = Image.new("RGB", wh, color="white")
+        draw = ImageDraw.Draw(txt)
+        font = ImageFont.truetype("data/DejaVuSans.ttf", size=size)
+        nc = int(40 * (wh[0] / 256))
+        if isinstance(xc[bi], list):
+            text_seq = xc[bi][0]
+        else:
+            text_seq = xc[bi]
+        lines = "\n".join(
+            text_seq[start : start + nc] for start in range(0, len(text_seq), nc)
+        )
+
+        try:
+            draw.text((0, 0), lines, fill="black", font=font)
+        except UnicodeEncodeError:
+            print("Cant encode string for logging. Skipping.")
+
+        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+        txts.append(txt)
+    txts = np.stack(txts)
+    txts = torch.tensor(txts)
+    return txts
+
+
+def partialclass(cls, *args, **kwargs):
+    class NewCls(cls):
+        __init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
+
+    return NewCls
+
+
+def make_path_absolute(path):
+    fs, p = fsspec.core.url_to_fs(path)
+    if fs.protocol == "file":
+        return os.path.abspath(p)
+    return path
+
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def isheatmap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+
+    return x.ndim == 2
+
+
+def isneighbors(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return x.ndim == 5 and (x.shape[2] == 3 or x.shape[2] == 1)
+
+
+def exists(x):
+    return x is not None
+
+
+def expand_dims_like(x, y):
+    while x.dim() != y.dim():
+        x = x.unsqueeze(-1)
+    return x
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+    """
+    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+    return total_params
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == "__is_first_stage__":
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False, invalidate_cache=True):
+    module, cls = string.rsplit(".", 1)
+    if invalidate_cache:
+        importlib.invalidate_caches()
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+        )
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def load_model_from_config(config, ckpt, verbose=True, freeze=True):
+    print(f"Loading model from {ckpt}")
+    if ckpt.endswith("ckpt"):
+        pl_sd = torch.load(ckpt, map_location="cpu")
+        if "global_step" in pl_sd:
+            print(f"Global Step: {pl_sd['global_step']}")
+        sd = pl_sd["state_dict"]
+    elif ckpt.endswith("safetensors"):
+        sd = load_safetensors(ckpt)
+    else:
+        raise NotImplementedError
+
+    model = instantiate_from_config(config.model)
+
+    m, u = model.load_state_dict(sd, strict=False)
+
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    if freeze:
+        for param in model.parameters():
+            param.requires_grad = False
+
+    model.eval()
+    return model
+
+
+def get_configs_path() -> str:
+    """
+    Get the `configs` directory.
+    For a working copy, this is the one in the root of the repository,
+    but for an installed copy, it's in the `sgm` package (see pyproject.toml).
+    """
+    this_dir = os.path.dirname(__file__)
+    candidates = (
+        os.path.join(this_dir, "configs"),
+        os.path.join(this_dir, "..", "configs"),
+    )
+    for candidate in candidates:
+        candidate = os.path.abspath(candidate)
+        if os.path.isdir(candidate):
+            return candidate
+    raise FileNotFoundError(f"Could not find SGM configs in {candidates}")
+
+
+def get_nested_attribute(obj, attribute_path, depth=None, return_key=False):
+    """
+    Will return the result of a recursive get attribute call.
+    E.g.:
+        a.b.c
+        = getattr(getattr(a, "b"), "c")
+        = get_nested_attribute(a, "b.c")
+    If any part of the attribute call is an integer x with current obj a, will
+    try to call a[x] instead of a.x first.
+    """
+    attributes = attribute_path.split(".")
+    if depth is not None and depth > 0:
+        attributes = attributes[:depth]
+    assert len(attributes) > 0, "At least one attribute should be selected"
+    current_attribute = obj
+    current_key = None
+    for level, attribute in enumerate(attributes):
+        current_key = ".".join(attributes[: level + 1])
+        try:
+            id_ = int(attribute)
+            current_attribute = current_attribute[id_]
+        except ValueError:
+            current_attribute = getattr(current_attribute, attribute)
+
+    return (current_attribute, current_key) if return_key else current_attribute
diff --git a/shell_scripts/raw_img_list/Animals.txt b/shell_scripts/raw_img_list/Animals.txt
new file mode 100644
index 0000000000000000000000000000000000000000..06e0a62ea52609b956d23c0424651dd9b2097db1
--- /dev/null
+++ b/shell_scripts/raw_img_list/Animals.txt
@@ -0,0 +1,12487 @@
+0/10017/campos_512_v4
+0/10031/campos_512_v4
+0/10050/campos_512_v4
+0/10075/campos_512_v4
+0/10118/campos_512_v4
+0/10120/campos_512_v4
+0/10142/campos_512_v4
+0/10155/campos_512_v4
+0/10174/campos_512_v4
+0/10178/campos_512_v4
+0/10193/campos_512_v4
+0/10196/campos_512_v4
+0/10218/campos_512_v4
+0/10230/campos_512_v4
+0/10253/campos_512_v4
+0/10271/campos_512_v4
+0/10314/campos_512_v4
+0/10342/campos_512_v4
+0/10422/campos_512_v4
+0/10425/campos_512_v4
+0/10457/campos_512_v4
+0/10462/campos_512_v4
+0/10507/campos_512_v4
+0/10517/campos_512_v4
+0/10529/campos_512_v4
+0/10554/campos_512_v4
+0/10585/campos_512_v4
+0/10619/campos_512_v4
+0/10636/campos_512_v4
+0/10639/campos_512_v4
+0/10664/campos_512_v4
+0/10679/campos_512_v4
+0/10770/campos_512_v4
+0/10772/campos_512_v4
+0/10867/campos_512_v4
+0/10891/campos_512_v4
+0/10955/campos_512_v4
+0/10971/campos_512_v4
+0/10998/campos_512_v4
+0/11068/campos_512_v4
+0/11075/campos_512_v4
+0/11133/campos_512_v4
+0/11300/campos_512_v4
+0/11453/campos_512_v4
+0/11546/campos_512_v4
+0/11649/campos_512_v4
+0/11726/campos_512_v4
+0/11869/campos_512_v4
+0/11959/campos_512_v4
+0/12023/campos_512_v4
+0/12259/campos_512_v4
+0/12495/campos_512_v4
+0/12535/campos_512_v4
+0/12539/campos_512_v4
+0/12600/campos_512_v4
+0/12603/campos_512_v4
+0/12691/campos_512_v4
+0/12706/campos_512_v4
+0/12859/campos_512_v4
+0/12926/campos_512_v4
+0/12962/campos_512_v4
+0/13087/campos_512_v4
+0/13240/campos_512_v4
+0/13277/campos_512_v4
+0/13312/campos_512_v4
+0/13547/campos_512_v4
+0/13677/campos_512_v4
+0/13838/campos_512_v4
+0/13872/campos_512_v4
+0/13887/campos_512_v4
+0/14050/campos_512_v4
+0/14307/campos_512_v4
+0/14359/campos_512_v4
+0/14544/campos_512_v4
+0/14686/campos_512_v4
+0/14781/campos_512_v4
+0/14816/campos_512_v4
+0/14903/campos_512_v4
+0/14911/campos_512_v4
+1/15366/campos_512_v4
+1/15480/campos_512_v4
+1/15563/campos_512_v4
+1/15812/campos_512_v4
+1/15836/campos_512_v4
+1/15943/campos_512_v4
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diff --git a/shell_scripts/raw_img_list/BuildingsOutdoor.txt b/shell_scripts/raw_img_list/BuildingsOutdoor.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb010df1f96095a9794f8c9f7e697690b62fc9ff
--- /dev/null
+++ b/shell_scripts/raw_img_list/BuildingsOutdoor.txt
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diff --git a/shell_scripts/raw_img_list/Electronics.txt b/shell_scripts/raw_img_list/Electronics.txt
new file mode 100644
index 0000000000000000000000000000000000000000..711b8f07d9aa02f2379513c6021254313ba97f31
--- /dev/null
+++ b/shell_scripts/raw_img_list/Electronics.txt
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diff --git a/shell_scripts/raw_img_list/Food.txt b/shell_scripts/raw_img_list/Food.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a6a0938c529463743277a25561782ada8e58a72d
--- /dev/null
+++ b/shell_scripts/raw_img_list/Food.txt
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diff --git a/shell_scripts/raw_img_list/Furnitures.txt b/shell_scripts/raw_img_list/Furnitures.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b0a77e7122af7f41939297785896ec99dd123e41
--- /dev/null
+++ b/shell_scripts/raw_img_list/Furnitures.txt
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diff --git a/shell_scripts/raw_img_list/Human-Shape.txt b/shell_scripts/raw_img_list/Human-Shape.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b860e75a3bfb34c5e1ea07d64a7129a62c4999e6
--- /dev/null
+++ b/shell_scripts/raw_img_list/Human-Shape.txt
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diff --git a/shell_scripts/raw_img_list/Plants.txt b/shell_scripts/raw_img_list/Plants.txt
new file mode 100644
index 0000000000000000000000000000000000000000..027c84e9ffc235eb01fc4cc2ad9e6bc860ebaf4e
--- /dev/null
+++ b/shell_scripts/raw_img_list/Plants.txt
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diff --git a/shell_scripts/raw_img_list/Transportations_tar.txt b/shell_scripts/raw_img_list/Transportations_tar.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cc5c8b484367af63676572fda2990b162ac4fab1
--- /dev/null
+++ b/shell_scripts/raw_img_list/Transportations_tar.txt
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diff --git a/shell_scripts/raw_img_list/daily-used.txt b/shell_scripts/raw_img_list/daily-used.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb7b6e551887c95f5dfd4bb094f0e4f84515d0d1
--- /dev/null
+++ b/shell_scripts/raw_img_list/daily-used.txt
@@ -0,0 +1,98382 @@
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+99/507696/campos_512_v4
+99/507702/campos_512_v4
+99/507704/campos_512_v4
+99/507705/campos_512_v4
+99/507714/campos_512_v4
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+99/507738/campos_512_v4
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+99/507787/campos_512_v4
+99/507789/campos_512_v4
+99/507805/campos_512_v4
+99/507807/campos_512_v4
+99/507867/campos_512_v4
+99/507870/campos_512_v4
+99/507872/campos_512_v4
+99/507878/campos_512_v4
+99/507882/campos_512_v4
+99/507890/campos_512_v4
+99/507938/campos_512_v4
+99/507941/campos_512_v4
+99/507946/campos_512_v4
+99/507952/campos_512_v4
+99/507955/campos_512_v4
+99/507965/campos_512_v4
+99/507971/campos_512_v4
+99/507977/campos_512_v4
+99/507986/campos_512_v4
+99/507991/campos_512_v4
+99/507993/campos_512_v4
+99/507994/campos_512_v4
+99/507997/campos_512_v4
+99/508001/campos_512_v4
+99/508012/campos_512_v4
+99/508017/campos_512_v4
+99/508034/campos_512_v4
+99/508041/campos_512_v4
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+99/508123/campos_512_v4
+99/508124/campos_512_v4
+99/508141/campos_512_v4
+99/508177/campos_512_v4
+99/508188/campos_512_v4
+99/508191/campos_512_v4
+99/508198/campos_512_v4
+99/508205/campos_512_v4
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+99/508218/campos_512_v4
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+99/508222/campos_512_v4
+99/508229/campos_512_v4
+99/508233/campos_512_v4
+99/508236/campos_512_v4
+99/508240/campos_512_v4
+99/508255/campos_512_v4
+99/508257/campos_512_v4
+99/508265/campos_512_v4
+99/508268/campos_512_v4
+99/508280/campos_512_v4
+99/508290/campos_512_v4
+99/508291/campos_512_v4
+99/508300/campos_512_v4
+99/508316/campos_512_v4
+99/508322/campos_512_v4
+99/508328/campos_512_v4
+99/508331/campos_512_v4
+99/508335/campos_512_v4
+99/508342/campos_512_v4
+99/508358/campos_512_v4
+99/508362/campos_512_v4
+99/508369/campos_512_v4
+99/508372/campos_512_v4
+99/508379/campos_512_v4
+99/508387/campos_512_v4
+99/508416/campos_512_v4
+99/508442/campos_512_v4
+99/508451/campos_512_v4
+99/508455/campos_512_v4
+99/508461/campos_512_v4
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+99/508484/campos_512_v4
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+99/508515/campos_512_v4
+99/508533/campos_512_v4
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+99/508535/campos_512_v4
+99/508544/campos_512_v4
+99/508551/campos_512_v4
+99/508552/campos_512_v4
+99/508556/campos_512_v4
+99/508565/campos_512_v4
+99/508575/campos_512_v4
+99/508580/campos_512_v4
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+99/508583/campos_512_v4
+99/508585/campos_512_v4
+99/508588/campos_512_v4
+99/508601/campos_512_v4
+99/508606/campos_512_v4
+99/508609/campos_512_v4
+99/508649/campos_512_v4
+99/508655/campos_512_v4
+99/508658/campos_512_v4
+99/508662/campos_512_v4
+99/508668/campos_512_v4
+99/508669/campos_512_v4
+99/508673/campos_512_v4
+99/508677/campos_512_v4
+99/508678/campos_512_v4
+99/508681/campos_512_v4
+99/508692/campos_512_v4
+99/508705/campos_512_v4
+99/508707/campos_512_v4
+99/508710/campos_512_v4
+99/508713/campos_512_v4
+99/508718/campos_512_v4
+99/508729/campos_512_v4
+99/508740/campos_512_v4
+99/508750/campos_512_v4
+99/508757/campos_512_v4
+99/508782/campos_512_v4
+99/508788/campos_512_v4
+99/508789/campos_512_v4
+99/508821/campos_512_v4
+99/508828/campos_512_v4
+99/508829/campos_512_v4
+99/508833/campos_512_v4
+99/508834/campos_512_v4
+99/508838/campos_512_v4
+99/508840/campos_512_v4
+99/508850/campos_512_v4
+99/508863/campos_512_v4
+99/508865/campos_512_v4
+99/508898/campos_512_v4
+99/508906/campos_512_v4
+99/508916/campos_512_v4
+99/508918/campos_512_v4
+99/508921/campos_512_v4
+99/508930/campos_512_v4
+99/508946/campos_512_v4
+99/508973/campos_512_v4
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+99/508975/campos_512_v4
+99/508979/campos_512_v4
+99/508984/campos_512_v4
+99/508990/campos_512_v4
+99/508991/campos_512_v4
+99/509004/campos_512_v4
+99/509021/campos_512_v4
+99/509028/campos_512_v4
+99/509030/campos_512_v4
+99/509031/campos_512_v4
+99/509038/campos_512_v4
+99/509044/campos_512_v4
+99/509047/campos_512_v4
+99/509049/campos_512_v4
+99/509055/campos_512_v4
+99/509060/campos_512_v4
+99/509064/campos_512_v4
+99/509075/campos_512_v4
+99/509081/campos_512_v4
+99/509088/campos_512_v4
+99/509093/campos_512_v4
+99/509103/campos_512_v4
+99/509109/campos_512_v4
+99/509111/campos_512_v4
+99/509120/campos_512_v4
+99/509121/campos_512_v4
+99/509130/campos_512_v4
+99/509149/campos_512_v4
+99/509150/campos_512_v4
+99/509167/campos_512_v4
+99/509169/campos_512_v4
+99/509188/campos_512_v4
+99/509203/campos_512_v4
+99/509204/campos_512_v4
+99/509217/campos_512_v4
+99/509235/campos_512_v4
+99/509236/campos_512_v4
+99/509238/campos_512_v4
+99/509242/campos_512_v4
+99/509244/campos_512_v4
+99/509262/campos_512_v4
+99/509266/campos_512_v4
+99/509270/campos_512_v4
+99/509273/campos_512_v4
+99/509277/campos_512_v4
+99/509281/campos_512_v4
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+99/509302/campos_512_v4
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+99/509346/campos_512_v4
+99/509353/campos_512_v4
+99/509356/campos_512_v4
+99/509357/campos_512_v4
+99/509359/campos_512_v4
+99/509360/campos_512_v4
+99/509365/campos_512_v4
+99/509366/campos_512_v4
+99/509374/campos_512_v4
+99/509382/campos_512_v4
+99/509383/campos_512_v4
+99/509387/campos_512_v4
+99/509389/campos_512_v4
+99/509406/campos_512_v4
+99/509413/campos_512_v4
+99/509417/campos_512_v4
+99/509432/campos_512_v4
+99/509443/campos_512_v4
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+99/509446/campos_512_v4
+99/509457/campos_512_v4
+99/509464/campos_512_v4
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+99/509481/campos_512_v4
+99/509482/campos_512_v4
+99/509486/campos_512_v4
+99/509488/campos_512_v4
+99/509499/campos_512_v4
+99/509500/campos_512_v4
+99/509501/campos_512_v4
+99/509509/campos_512_v4
+99/509511/campos_512_v4
+99/509517/campos_512_v4
+99/509520/campos_512_v4
+99/509523/campos_512_v4
+99/509524/campos_512_v4
+99/509527/campos_512_v4
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+99/509552/campos_512_v4
+99/509553/campos_512_v4
+99/509554/campos_512_v4
+99/509566/campos_512_v4
+99/509571/campos_512_v4
+99/509573/campos_512_v4
+99/509588/campos_512_v4
+99/509592/campos_512_v4
+99/509599/campos_512_v4
+99/509605/campos_512_v4
+99/509636/campos_512_v4
+99/509639/campos_512_v4
+99/509656/campos_512_v4
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+99/509698/campos_512_v4
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+99/509715/campos_512_v4
+99/509720/campos_512_v4
+99/509729/campos_512_v4
+99/509736/campos_512_v4
+99/509743/campos_512_v4
+99/509746/campos_512_v4
+99/509750/campos_512_v4
+99/509758/campos_512_v4
+99/509789/campos_512_v4
+99/509790/campos_512_v4
+99/509818/campos_512_v4
+99/509819/campos_512_v4
+99/509829/campos_512_v4
+99/509837/campos_512_v4
+99/509839/campos_512_v4
+99/509845/campos_512_v4
+99/509846/campos_512_v4
+99/509847/campos_512_v4
+99/509865/campos_512_v4
+99/509880/campos_512_v4
+99/509881/campos_512_v4
+99/509888/campos_512_v4
+99/509892/campos_512_v4
+99/509899/campos_512_v4
+99/509932/campos_512_v4
+99/509935/campos_512_v4
+99/509940/campos_512_v4
+99/509957/campos_512_v4
+99/509969/campos_512_v4
+99/509990/campos_512_v4
+99/509992/campos_512_v4
+99/509999/campos_512_v4
diff --git a/shell_scripts/release/evaluation/.gitkeep b/shell_scripts/release/evaluation/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/shell_scripts/release/inference/i23d/i23d-stage1.sh b/shell_scripts/release/inference/i23d/i23d-stage1.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a47b9f1497a98593e42b3eb913dfb940ab19d64d
--- /dev/null
+++ b/shell_scripts/release/inference/i23d/i23d-stage1.sh
@@ -0,0 +1,213 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+
+num_workers=1
+NUM_GPUS=1
+batch_size=1
+num_samples=2
+
+microbatch=${batch_size}
+
+data_dir="./assets/demo-image-for-i23d/instantmesh" # gso also available
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=0
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=1.0 # for xyz diffusion
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 10000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 1024 \
+"
+
+
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 3 \
+--denoise_out_channels 3 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d True \
+--dit_model_arch DiT-PixArt-PCD-CLAY-L \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=./logs/i23d/stage-1/dino_img/
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 3 \
+--resume_checkpoint yslan/GaussianAnything/ckpts/checkpoints/i23d/stage-1/model_joint_denoise_rec_model2335000.pt \
+"
+
+# --resume_checkpoint /mnt/sfs-common/yslan/open-source/checkpoints/i23d/stage-1/model_joint_denoise_rec_model2335000.pt \
+
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+
+export CUDA_VISIBLE_DEVICES=0
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:12780 \
+ scripts/vit_triplane_sit_sample.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key img \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk False \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type img-uniform-gvp-dino \
+ --load_pcd True \
+ --num_frames 8 \
+ --split_chunk_size 16 \
+ --load_caption_dataset False \
+ --num_samples ${num_samples} \
+ --plane_n 1 \
+ --pooling_ctx_dim 768 \
+ --export_mesh False \
+ --load_real True \
+ --load_gso True \
+ --pcd_path "" \
+ --mv_latent_dir ""
diff --git a/shell_scripts/release/inference/i23d/i23d-stage2.sh b/shell_scripts/release/inference/i23d/i23d-stage2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d1491e6c9250482dedae99f28e883a36423a21fb
--- /dev/null
+++ b/shell_scripts/release/inference/i23d/i23d-stage2.sh
@@ -0,0 +1,217 @@
+set -x 
+
+lpips_lambda=0.8
+
+# image_size=364 # final rendered resolution
+# image_size=224 # final rendered resolution
+# image_size=512 # final rendered resolution
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+num_frames=8
+
+num_workers=2 # ! for debug
+NUM_GPUS=1
+batch_size=1
+num_samples=1
+
+microbatch=$(( n_cond_frames*batch_size*2 ))
+
+data_dir="./assets/demo-image-for-i23d/instantmesh" # gso also available
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=1e-4
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=1.0 # for xyz diffusion
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 10000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 1024 \
+"
+
+
+# ! diffusion steps and noise schedule not used, since the continuous diffusion is adopted.
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 10 \
+--denoise_out_channels 10 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d True \
+--dit_model_arch DiT-PixArt-PCD-CLAY-stage2-L \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+logdir=./logs/i23d/stage-2/dino_img-debug/
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 3 \
+--resume_checkpoint yslan/GaussianAnything/ckpts/checkpoints/i23d/stage-2/model_joint_denoise_rec_model2505000.pt \
+"
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+
+export CUDA_VISIBLE_DEVICES=0
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:21382 \
+ scripts/vit_triplane_sit_sample.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key img-xyz \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk False \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type img-uniform-gvp-dino-stage2 \
+ --load_pcd True \
+ --num_frames ${num_frames} \
+ --split_chunk_size 16 \
+ --load_caption_dataset False \
+ --plane_n 1 \
+ --num_samples ${num_samples} \
+ --pooling_ctx_dim 768 \
+ --load_real True \
+ --load_gso True \
+ --save_img False \
+ --export_mesh True \
+ --pcd_path /cpfs01/user/lanyushi.p/data/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd/ \
+ --stage_1_output_dir ./logs/i23d/stage-1/dino_img/ \
+ --mv_latent_dir "" \
+
+# pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade_x8x4x4_512
\ No newline at end of file
diff --git a/shell_scripts/release/inference/t23d/stage1-t23d.sh b/shell_scripts/release/inference/t23d/stage1-t23d.sh
new file mode 100644
index 0000000000000000000000000000000000000000..dc2a8352ed80dc3c21d646c21ee6594bf1c9c73a
--- /dev/null
+++ b/shell_scripts/release/inference/t23d/stage1-t23d.sh
@@ -0,0 +1,206 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+num_samples=2
+
+patch_size=14
+
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+
+num_workers=0
+NUM_GPUS=1
+batch_size=4
+
+microbatch=${batch_size}
+
+data_dir="placeholder"
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=0
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=1.0 #
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 25000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 768 \
+"
+
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 3 \
+--denoise_out_channels 3 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d False \
+--dit_model_arch DiT-PCD-L \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=./logs/t23d/stage-1
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 10 \
+--resume_checkpoint yslan/GaussianAnything/ckpts/checkpoints/t23d/stage-1/model_joint_denoise_rec_model1950000.pt \
+"
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+export CUDA_VISIBLE_DEVICES=0
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:22377 \
+ scripts/vit_triplane_sit_sample.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key caption \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk True \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type stage1-t23d \
+ --load_pcd True \
+ --num_frames 8 \
+ --split_chunk_size 16 \
+ --load_caption_dataset True \
+ --plane_n 1 \
+ --i23d False \
+ --pooling_ctx_dim 768 \
+ --num_samples ${num_samples} \
+ --pcd_path "" \
+ --mv_latent_dir "" \
diff --git a/shell_scripts/release/inference/t23d/stage2-t23d.sh b/shell_scripts/release/inference/t23d/stage2-t23d.sh
new file mode 100644
index 0000000000000000000000000000000000000000..14e70877df2e24876751c7e78b6ad90272e26e6a
--- /dev/null
+++ b/shell_scripts/release/inference/t23d/stage2-t23d.sh
@@ -0,0 +1,208 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+num_samples=2
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+
+num_workers=0
+NUM_GPUS=1
+batch_size=4
+
+microbatch=${batch_size}
+
+data_dir="placeholder"
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=0
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=0.25 #
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 25000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 768 \
+"
+
+# ! diffusion steps and noise schedule not used, since the continuous diffusion is adopted.
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 10 \
+--denoise_out_channels 10 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--dit_model_arch DiT-PCD-L-stage2-xyz2feat \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=./logs/t23d/stage-2
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 10 \
+--resume_checkpoint yslan/GaussianAnything/ckpts/checkpoints/t23d/stage-2/model_joint_denoise_rec_model2725000.pt \
+"
+
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+
+export CUDA_VISIBLE_DEVICES=0
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:22258 \
+ scripts/vit_triplane_sit_sample.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key caption \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk True \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type stage2-t23d \
+ --load_pcd True \
+ --num_frames 8 \
+ --split_chunk_size 16 \
+ --load_caption_dataset True \
+ --plane_n 1 \
+ --i23d False \
+ --pooling_ctx_dim 768 \
+ --num_samples ${num_samples} \
+ --pcd_path /cpfs01/user/lanyushi.p/data/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd/ \
+ --stage_1_output_dir ./logs/t23d/stage-1 \
+ --mv_latent_dir /cpfs01/user/lanyushi.p/data/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange/latent_dir
diff --git a/shell_scripts/release/inference/vae-3d-pc2.sh b/shell_scripts/release/inference/vae-3d-pc2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1a036686369e1e5cdf40b6313777fffb7c892bd4
--- /dev/null
+++ b/shell_scripts/release/inference/vae-3d-pc2.sh
@@ -0,0 +1,190 @@
+set -x 
+
+lpips_lambda=2.0
+ssim_lambda=0.
+l1_lambda=0. # following gaussian splatting
+l2_lambda=1 # ! use_conf_map
+
+NUM_GPUS=1
+
+image_size=512
+image_size_encoder=512
+
+num_workers=0 # for debug
+
+patch_size=14
+kl_lambda=1.0e-06
+
+perturb_pcd_scale=0
+
+num_frames=8
+batch_size=1 # ! actuall BS will double
+
+microbatch=$(( num_frames*batch_size*2 ))
+
+data_dir=./assets/demo-image-for-i23d/for-vae-reconstruction/
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+# raw inference
+conv_lr=0
+lr=0
+
+vit_decoder_lr=$lr
+encoder_lr=${conv_lr} # scaling version , could be larger when multi-nodes
+triplane_decoder_lr=$conv_lr
+super_resolution_lr=$conv_lr
+
+# * above the best lr config
+
+LR_FLAGS="--encoder_lr $encoder_lr \
+--vit_decoder_lr $vit_decoder_lr \
+--triplane_decoder_lr $triplane_decoder_lr \
+--super_resolution_lr $super_resolution_lr \
+--lr $lr"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 10000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --dino_version mv-sd-dit-srt-pcd-structured-pc2-nopcd \
+ --sr_training False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --image_size $image_size \
+ --kl_lambda ${kl_lambda} \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder False \
+ --fg_mse True \
+ --bg_lamdba 1.0 \
+ --lpips_delay_iter 100 \
+ --sr_delay_iter 25000 \
+ --kl_anneal True \
+ --symmetry_loss False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+#  --resume_checkpoint ./checkpoint/model_rec1965000.pt \
+
+
+# logdir=./logs/latent_dir/768-512-perturb${perturb_pcd_scale}
+logdir=./logs/latent_dir/pc2-debug
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--alpha_lambda 1.0 \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--decoder_output_dim 3 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded \
+"
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+# localedef -c -f UTF-8 -i en_US en_US.UTF-8
+# export LC_ALL=en_US.UTF-8
+
+export OPENCV_IO_ENABLE_OPENEXR=1
+export OMP_NUM_THREADS=12
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export TORCH_NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+
+for wds_split in 0
+
+do
+
+export CUDA_VISIBLE_DEVICES=$(( 0 + $wds_split ))
+port=$(( 14000 + $wds_split ))
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes=1 \
+  --rdzv-endpoint=localhost:${port} \
+  --rdzv_backend=c10d \
+ scripts/vit_triplane_train.py \
+ --trainer_name nv_rec_patch_mvE_gs \
+ --num_workers ${num_workers} \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ --lpips_lambda $lpips_lambda \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --save_interval 10000 \
+ --eval_interval 250000000 \
+ --decomposed True \
+ --logdir $logdir \
+ --decoder_load_pretrained False \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --use_amp False \
+ --eval_batch_size ${batch_size} \
+ ${LR_FLAGS} \
+ --l1_lambda ${l1_lambda} \
+ --l2_lambda ${l2_lambda} \
+ --ssim_lambda ${ssim_lambda} \
+ --depth_smoothness_lambda 0 \
+ --use_conf_map False \
+ --objv_dataset True \
+ --depth_lambda 0.5 \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --mv_input True \
+ --inference True \
+ --split_chunk_input True \
+ --four_view_for_latent False \
+ --append_depth False \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --return_all_dit_layers False \
+ --ldm_embed_dim 10 \
+ --xyz_lambda 0.0 \
+ --emd_lambda 0.0 \
+ --cd_lambda 0.0 \
+ --fps_sampling True \
+ --subset_fps_sampling False \
+ --subset_half_fps_sampling False \
+ --num_frames ${num_frames} \
+ --frame_0_as_canonical False \
+ --split_chunk_size $((num_frames + num_frames)) \
+ --read_normal True \
+ --in_plane_attention False \
+ --load_pcd True \
+ --rand_aug_bg True \
+ --use_wds False \
+ --append_xyz True \
+ --use_chunk True \
+ --pcd_path /mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --plane_n 1 \
+ --latent_num 768 \
+ --perturb_pcd_scale ${perturb_pcd_scale} \
+ --wds_split ${wds_split} \
+
+#  --pcd_path /nas/shared/V2V/yslan/logs/nips23/Reconstruction/pcd-V=10_4096_polish/fps-pcd \
+
+done
+
diff --git a/shell_scripts/release/inference/vae-3d.sh b/shell_scripts/release/inference/vae-3d.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0697eebaa569d554826d9adb9291bbb45f3574db
--- /dev/null
+++ b/shell_scripts/release/inference/vae-3d.sh
@@ -0,0 +1,188 @@
+set -x 
+
+lpips_lambda=2.0
+ssim_lambda=0.
+l1_lambda=0. # following gaussian splatting
+l2_lambda=1 # ! use_conf_map
+
+NUM_GPUS=1
+
+image_size=512
+image_size_encoder=512
+
+num_workers=2 # for debug
+
+patch_size=14
+kl_lambda=1.0e-06
+
+perturb_pcd_scale=0
+
+num_frames=8
+batch_size=1 # ! actuall BS will double
+
+microbatch=$(( num_frames*batch_size*2 ))
+
+data_dir=./assets/demo-image-for-i23d/for-vae-reconstruction/
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+# raw inference
+conv_lr=0
+lr=0
+
+vit_decoder_lr=$lr
+encoder_lr=${conv_lr} # scaling version , could be larger when multi-nodes
+triplane_decoder_lr=$conv_lr
+super_resolution_lr=$conv_lr
+
+# * above the best lr config
+
+LR_FLAGS="--encoder_lr $encoder_lr \
+--vit_decoder_lr $vit_decoder_lr \
+--triplane_decoder_lr $triplane_decoder_lr \
+--super_resolution_lr $super_resolution_lr \
+--lr $lr"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 10000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --image_size $image_size \
+ --kl_lambda ${kl_lambda} \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder False \
+ --fg_mse True \
+ --bg_lamdba 1.0 \
+ --lpips_delay_iter 100 \
+ --sr_delay_iter 25000 \
+ --kl_anneal True \
+ --symmetry_loss False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ --resume_checkpoint ./checkpoint/model_rec1965000.pt \
+ "
+
+
+logdir=./logs/latent_dir/768-512-perturb${perturb_pcd_scale}
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--alpha_lambda 1.0 \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--decoder_output_dim 3 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_vae_decoder_cascaded \
+"
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+# localedef -c -f UTF-8 -i en_US en_US.UTF-8
+# export LC_ALL=en_US.UTF-8
+
+export OPENCV_IO_ENABLE_OPENEXR=1
+export OMP_NUM_THREADS=12
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export TORCH_NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+
+for wds_split in 0
+
+do
+
+export CUDA_VISIBLE_DEVICES=$(( 0 + $wds_split ))
+port=$(( 14000 + $wds_split ))
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes=1 \
+  --rdzv-endpoint=localhost:${port} \
+  --rdzv_backend=c10d \
+ scripts/vit_triplane_train.py \
+ --trainer_name nv_rec_patch_mvE_gs \
+ --num_workers ${num_workers} \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ --lpips_lambda $lpips_lambda \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --save_interval 10000 \
+ --eval_interval 250000000 \
+ --decomposed True \
+ --logdir $logdir \
+ --decoder_load_pretrained False \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --use_amp True \
+ --eval_batch_size ${batch_size} \
+ ${LR_FLAGS} \
+ --l1_lambda ${l1_lambda} \
+ --l2_lambda ${l2_lambda} \
+ --ssim_lambda ${ssim_lambda} \
+ --depth_smoothness_lambda 0 \
+ --use_conf_map False \
+ --objv_dataset True \
+ --depth_lambda 0.5 \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --mv_input True \
+ --inference True \
+ --split_chunk_input True \
+ --four_view_for_latent False \
+ --append_depth False \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --return_all_dit_layers False \
+ --ldm_embed_dim 10 \
+ --xyz_lambda 0.0 \
+ --emd_lambda 0.0 \
+ --cd_lambda 0.0 \
+ --fps_sampling True \
+ --subset_fps_sampling False \
+ --subset_half_fps_sampling False \
+ --num_frames ${num_frames} \
+ --frame_0_as_canonical False \
+ --split_chunk_size $((num_frames + num_frames)) \
+ --read_normal True \
+ --in_plane_attention False \
+ --load_pcd True \
+ --rand_aug_bg True \
+ --use_wds False \
+ --append_xyz True \
+ --use_chunk True \
+ --pcd_path /mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --plane_n 1 \
+ --latent_num 768 \
+ --perturb_pcd_scale ${perturb_pcd_scale} \
+ --wds_split ${wds_split} \
+
+#  --pcd_path /nas/shared/V2V/yslan/logs/nips23/Reconstruction/pcd-V=10_4096_polish/fps-pcd \
+
+done
+
diff --git a/shell_scripts/release/train/.gitkeep b/shell_scripts/release/train/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/shell_scripts/release/train/stage-1-vae3d/vae3d-adv-512.sh b/shell_scripts/release/train/stage-1-vae3d/vae3d-adv-512.sh
new file mode 100644
index 0000000000000000000000000000000000000000..403e55eff6963d73320a22b0d8434b5f4d85b221
--- /dev/null
+++ b/shell_scripts/release/train/stage-1-vae3d/vae3d-adv-512.sh
@@ -0,0 +1,212 @@
+set -x 
+# vit_decoder_lr=1.001
+
+lpips_lambda=0.8
+l1_lambda=1.0 # following gaussian splatting
+
+l2_lambda=0.0
+ssim_lambda=0.0
+lambda_normal=0.025
+
+lambda_dist=1000
+overfitting=False
+
+
+NUM_GPUS=8
+num_workers=8 # 
+batch_size=1
+
+
+patchgan_disc_factor=0.025
+patchgan_disc_g_weight=0.025
+perturb_pcd_scale=0.01
+
+
+image_size=512
+image_size_encoder=512
+
+patch_size=14
+kl_lambda=2.5e-06
+patch_rendering_resolution=${image_size}
+
+num_frames=8
+
+microbatch=$(( num_frames*batch_size*2 ))
+
+dataset_name=75K
+data_dir=/cpfs01/user/lanyushi.p/data/chunk-jpeg-normal/bs_16_fixsave3/170K/512/
+
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+# half LR since BS halved, during high-res finetuning
+conv_lr=1e-4
+lr=5e-5 
+
+vit_decoder_lr=$lr
+encoder_lr=${conv_lr} # scaling version , could be larger when multi-nodes
+triplane_decoder_lr=$conv_lr
+super_resolution_lr=$conv_lr
+
+# * above the best lr config
+
+LR_FLAGS="--encoder_lr $encoder_lr \
+--vit_decoder_lr $vit_decoder_lr \
+--triplane_decoder_lr $triplane_decoder_lr \
+--super_resolution_lr $super_resolution_lr \
+--lr $lr"
+
+TRAIN_FLAGS="--iterations 5000 --anneal_lr False \
+ --batch_size $batch_size \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --image_size $image_size \
+ --kl_lambda ${kl_lambda} \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder False \
+ --fg_mse True \
+ --bg_lamdba 1.0 \
+ --lpips_delay_iter 25000 \
+ --sr_delay_iter 25000 \
+ --kl_anneal True \
+ --symmetry_loss False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ --resume_checkpoint /nas/shared/public/yslan/logs/vae/f=8-cascade/latent=768-8x3x3-fullset-surfacePCD-adv/bs1-gpu8-0.025-0.01-advFinestOnly_512_perturb/model_rec1665000.pt \
+ "
+
+logdir=/nas/shared/public/yslan/logs/vae/f=8-cascade/latent=768-8x3x3-fullset-surfacePCD-adv/bs${batch_size}-gpu${NUM_GPUS}-${patchgan_disc_factor}-${patchgan_disc_g_weight}-advFinestOnly_512_perturb-largeradv
+
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--alpha_lambda 1.0 \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--decoder_output_dim 3 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade_x8x4x4_512 \
+"
+
+# pcd_structured_latent_space_lion_learnoffset_surfel_sr_noptVAE
+# pcd_structured_latent_space_lion_learnoffset_surfel_sr_noptVAE_debugscale_f1
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+# localedef -c -f UTF-8 -i en_US en_US.UTF-8
+# export LC_ALL=en_US.UTF-8
+
+export OPENCV_IO_ENABLE_OPENEXR=1
+export OMP_NUM_THREADS=12
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+export CUDA_LAUNCH_BLOCKING=1
+export TORCH_USE_CUDA_DSA=1
+
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+# export CUDA_VISIBLE_DEVICES=0,1,2,3
+# export CUDA_VISIBLE_DEVICES=4,5,6,7
+# export CUDA_VISIBLE_DEVICES=6,7
+# export CUDA_VISIBLE_DEVICES=2,3
+# export CUDA_VISIBLE_DEVICES=4,5
+# export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+
+  # --rdzv-endpoint=localhost:19381 \
+
+torchrun --rdzv-endpoint=localhost:19408 \
+  --nproc_per_node=$NUM_GPUS \
+  --nnodes=1 \
+  --rdzv_backend=c10d \
+ scripts/vit_triplane_train.py \
+ --trainer_name nv_rec_patch_mvE_gs_disc \
+ --num_workers ${num_workers} \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ --lpips_lambda $lpips_lambda \
+ --overfitting ${overfitting} \
+ --load_pretrain_encoder False \
+ --iterations 300000 \
+ --save_interval 10000 \
+ --eval_interval 250000000 \
+ --decomposed True \
+ --logdir $logdir \
+ --decoder_load_pretrained False \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --use_amp True \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --l1_lambda ${l1_lambda} \
+ --l2_lambda ${l2_lambda} \
+ --ssim_lambda ${ssim_lambda} \
+ --lambda_normal ${lambda_normal} \
+ --lambda_dist ${lambda_dist} \
+ --depth_smoothness_lambda 0 \
+ --use_conf_map False \
+ --objv_dataset True \
+ --depth_lambda 0.0 \
+ --patch_rendering_resolution ${patch_rendering_resolution} \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --mv_input True \
+ --split_chunk_input True \
+ --four_view_for_latent False \
+ --append_depth False \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --return_all_dit_layers False \
+ --ldm_embed_dim 10 \
+ --xyz_lambda 0.0 \
+ --emd_lambda 0.0 \
+ --cd_lambda 0.0 \
+ --fps_sampling True \
+ --subset_fps_sampling False \
+ --subset_half_fps_sampling False \
+ --num_frames ${num_frames} \
+ --frame_0_as_canonical False \
+ --split_chunk_size $((num_frames + num_frames)) \
+ --read_normal True \
+ --in_plane_attention False \
+ --load_pcd True \
+ --rand_aug_bg True \
+ --use_wds False \
+ --append_xyz True \
+ --use_chunk True \
+ --pcd_path /cpfs01/user/lanyushi.p/data/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd/ \
+ --pt_ft_kl False \
+ --ft_kl True \
+ --lambda_scale_reg 0.0 \
+ --latent_num 768 \
+ --lambda_opa_reg 0.01 \
+ --surfel_rendering True \
+ --patchgan_disc_factor ${patchgan_disc_factor} \
+ --patchgan_disc_g_weight ${patchgan_disc_g_weight} \
+ --perturb_pcd_scale ${perturb_pcd_scale} \
+ --plane_n 1 \
diff --git a/shell_scripts/release/train/stage2-i23d/.gitkeep b/shell_scripts/release/train/stage2-i23d/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/shell_scripts/release/train/stage2-i23d/i23d-klfeat-gen.sh b/shell_scripts/release/train/stage2-i23d/i23d-klfeat-gen.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d21be101a636941a6d03b1f97af9182b10cc710b
--- /dev/null
+++ b/shell_scripts/release/train/stage2-i23d/i23d-klfeat-gen.sh
@@ -0,0 +1,228 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+num_frames=8
+
+data_dir=/cpfs01/user/lanyushi.p/data/unzip4_img
+pcd_path=/mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd
+mv_latent_dir=/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange/latent_dir
+
+num_workers=1
+NUM_GPUS=1
+n_cond_frames=5
+batch_size=1
+
+# microbatch=${batch_size}
+microbatch=$(( n_cond_frames*batch_size*2 ))
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=1e-4
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=1.0 # for xyz diffusion
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 10000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 1024 \
+"
+
+
+# ! diffusion steps and noise schedule not used, since the continuous diffusion is adopted.
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 10 \
+--denoise_out_channels 10 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d True \
+--dit_model_arch DiT-PixArt-PCD-CLAY-stage2-L \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=/nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/${dataset_name}/gs-disentangle/cascade_check/clay/stage2/dino_img/noperturb/196w_latent-fullset_${NUM_GPUS}-${batch_size}-ctd
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade_x8x4x4_512 \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 3 \
+--resume_checkpoint /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage2/dino_img/noperturb/196w_latent-fullset_8-4/model_joint_denoise_rec_model2045000.pt \
+"
+
+# /nas/shared/public/yslan/logs/vae/f=8-cascade/latent=768-8x3x3-fullset-surfacePCD-adv/bs1-gpu8-0.025-0.025-advFinestOnly_512_perturb-largeradv/model_rec1965000.pt \
+
+# --resume_checkpoint /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage2/dino_img/noperturb/fullset_8-40/model_joint_denoise_rec_model1925000.pt \
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage2/dino_img/fullset_4-32-ctd2/model_joint_denoise_rec_model1885000.pt \
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage2/dino_img/fullset_8-32-ctd/model_joint_denoise_rec_model1825000.pt \
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage2/dino_img/fullset_8-32/model_joint_denoise_rec_model1785000.pt \
+# /nas/shared/public/yslan/logs/vae/f=8-cascade/latent=768-8x3x3-fullset-surfacePCD-adv/bs2-gpu8-0.025-0.01-advFinestOnly-perturbxyz/model_rec1635000.pt \
+
+# /nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/CA_dinoonly_ditb_overfit_CAFirst_dino518/model_joint_denoise_rec_model1750000.pt \
+
+
+# --resume_checkpoint /nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output/model_joint_denoise_rec_model1600000.pt \
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+# export CUDA_VISIBLE_DEVICES=0,1,2
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+# export CUDA_VISIBLE_DEVICES=4,5,6,7
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:21378 \
+ scripts/vit_triplane_sit_train.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key img-xyz \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk True \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type img-uniform-gvp-dino-stage2 \
+ --load_pcd True \
+ --num_frames ${num_frames} \
+ --split_chunk_size 16 \
+ --load_caption_dataset False \
+ --plane_n 1 \
+ --pooling_ctx_dim 768 \
+ --pcd_path ${pcd_path} \
+ --mv_latent_dir ${mv_latent_dir}
diff --git a/shell_scripts/release/train/stage2-i23d/i23d-pcd-gen.sh b/shell_scripts/release/train/stage2-i23d/i23d-pcd-gen.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f8f109a241d4223583ec747f23308162b896b30f
--- /dev/null
+++ b/shell_scripts/release/train/stage2-i23d/i23d-pcd-gen.sh
@@ -0,0 +1,242 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+
+latent_dir=/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange/latent_dir
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+
+num_workers=6
+NUM_GPUS=8
+n_cond_frames=5
+batch_size=4
+
+# microbatch=${batch_size}
+microbatch=$(( n_cond_frames*batch_size*2 ))
+
+data_dir=/cpfs01/user/lanyushi.p/data/unzip4_img
+pcd_path=/mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd
+mv_latent_dir=/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange/latent_dir
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=1e-4
+
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=1.0 # for xyz diffusion
+# * above the best lr config
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 10000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 1024 \
+"
+# --pred_type x0 \
+# --iw_sample_p drop_all_uniform \
+# --loss_type x0 \
+
+# ! diffusion steps and noise schedule not used, since the continuous diffusion is adopted.
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 3 \
+--denoise_out_channels 3 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d True \
+--dit_model_arch DiT-PixArt-PCD-CLAY-L \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=./logs/LSGM/t23d/FM/${dataset_name}/gs-disentangle/cascade_check/clay/stage1/dino_img/fullset_${NUM_GPUS}-${batch_size}
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 3 \
+--resume_checkpoint /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/dino_img/fullset_8-4-ctd5-usemvdata/model_joint_denoise_rec_model2335000.pt \
+"
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/dino_img/fullset_4-24-ctd4/model_joint_denoise_rec_model2315000.pt \
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/dino_img/fullset_8-32-ctd2/model_joint_denoise_rec_model1935000.pt \
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/dino_img/fullset_8-32-ctd/model_joint_denoise_rec_model1915000.pt \
+
+# /nas/shared/public/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/stage1/dino_img/fullset_7-40/model_joint_denoise_rec_model1825000.pt \
+
+# /nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/clay/CA_dinoonly_ditb_overfit_CAFirst_dino518/model_joint_denoise_rec_model1750000.pt \
+
+
+# --resume_checkpoint /nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output/model_joint_denoise_rec_model1600000.pt \
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+# export CUDA_VISIBLE_DEVICES=0,1,2
+
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5
+# export CUDA_VISIBLE_DEVICES=0,1,2,3
+# export CUDA_VISIBLE_DEVICES=0,1,2,3
+# export CUDA_VISIBLE_DEVICES=3,4,5,6
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6
+# export CUDA_VISIBLE_DEVICES=3,4,5,6
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+# export CUDA_VISIBLE_DEVICES=1,2
+# export CUDA_VISIBLE_DEVICES=3,4
+# export CUDA_VISIBLE_DEVICES=0,1
+# export CUDA_VISIBLE_DEVICES=4,5
+# export CUDA_VISIBLE_DEVICES=4,5,6,7
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:23378 \
+ scripts/vit_triplane_sit_train.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key img \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk True \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type img-uniform-gvp-dino \
+ --load_pcd True \
+ --num_frames 8 \
+ --split_chunk_size 16 \
+ --load_caption_dataset False \
+ --plane_n 1 \
+ --pooling_ctx_dim 768 \
+ --pcd_path ${pcd_path} \
+ --mv_latent_dir ${mv_latent_dir}
\ No newline at end of file
diff --git a/shell_scripts/release/train/stage2-t23d/.gitkeep b/shell_scripts/release/train/stage2-t23d/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/shell_scripts/release/train/stage2-t23d/t23d-klfeat-gen.sh b/shell_scripts/release/train/stage2-t23d/t23d-klfeat-gen.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d3a8695a15b1c698e818b8806ee58f2c4e8d84e0
--- /dev/null
+++ b/shell_scripts/release/train/stage2-t23d/t23d-klfeat-gen.sh
@@ -0,0 +1,222 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+
+# ! on a100
+# num_workers=8
+# NUM_GPUS=8
+# batch_size=48
+
+# ! on v100, test
+num_workers=2
+NUM_GPUS=1
+# batch_size=16
+batch_size=1
+
+microbatch=${batch_size}
+
+data_dir=/cpfs01/user/lanyushi.p/data/unzip4_img
+pcd_path=/mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd
+mv_latent_dir=/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange/latent_dir
+
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+microbatch=${batch_size}
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=1e-4
+# lr=5e-5
+# lr=1e-5
+# lr=7.5e-5 # 192/256
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1.0
+
+triplane_scaling_divider=0.25 #
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 25000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 768 \
+"
+# --pred_type x0 \
+# --iw_sample_p drop_all_uniform \
+# --loss_type x0 \
+
+# ! diffusion steps and noise schedule not used, since the continuous diffusion is adopted.
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 10 \
+--denoise_out_channels 10 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d False \
+--dit_model_arch DiT-PCD-L-stage2-xyz2feat \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=./logs/LSGM/t23d/FM/${dataset_name}/gs-disentangle/cascade_check/clay/stage2/clip_text/fullset_${NUM_GPUS}-${batch_size}-qknorm-scale${triplane_scaling_divider}
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade_x8x4x4 \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 10 \
+--resume_checkpoint /mnt/sfs-common/yslan/open-source/checkpoints/t23d/stage-2/model_joint_denoise_rec_model2725000.pt \
+"
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export CUDA_VISIBLE_DEVICES=0
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:23377 \
+ scripts/vit_triplane_sit_train.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key caption \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk True \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type stage2-t23d \
+ --load_pcd True \
+ --num_frames 8 \
+ --split_chunk_size 16 \
+ --load_caption_dataset True \
+ --plane_n 1 \
+ --pooling_ctx_dim 768 \
+ --pcd_path ${pcd_path} \
+ --mv_latent_dir ${mv_latent_dir}
\ No newline at end of file
diff --git a/shell_scripts/release/train/stage2-t23d/t23d-pcd-gen.sh b/shell_scripts/release/train/stage2-t23d/t23d-pcd-gen.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b4290886da5b9479cc4d4d2fa151eb60bd10900e
--- /dev/null
+++ b/shell_scripts/release/train/stage2-t23d/t23d-pcd-gen.sh
@@ -0,0 +1,226 @@
+set -x 
+
+lpips_lambda=0.8
+
+image_size=512
+image_size_encoder=${image_size}
+
+patch_size=14
+
+
+cfg_dropout_prob=0.1 # SD config
+dataset_name="9cls"
+
+# ! on a100
+# num_workers=8
+# NUM_GPUS=8
+# batch_size=48
+
+# ! on v100, test
+num_workers=2
+NUM_GPUS=1
+#batch_size=16
+batch_size=1
+
+microbatch=${batch_size}
+
+data_dir=/cpfs01/user/lanyushi.p/data/unzip4_img
+pcd_path=/mnt/sfs-common/yslan/Dataset/Obajverse/FPS_PCD/pcd-V=10_4096_polish_fullset/fps-pcd
+mv_latent_dir=/mnt/sfs-common/yslan/open-source/latent_dir/gs-latent-dim=10-fullset-cascade-fixpcd-adv_xyzaug_loweradv_768-fixinprange/latent_dir
+
+DATASET_FLAGS="
+ --data_dir ${data_dir} \
+ --eval_data_dir ${data_dir} \
+"
+
+lr=1e-4
+
+
+kl_lambda=0
+vit_lr=1e-5 # for improved-diffusion unet
+ce_lambda=0 # ?
+conv_lr=5e-5
+alpha_lambda=1
+scale_clip_encoding=1
+
+triplane_scaling_divider=1.0 #
+
+LR_FLAGS="--encoder_lr $vit_lr \
+ --vit_decoder_lr $vit_lr \
+ --lpips_lambda $lpips_lambda \
+ --triplane_decoder_lr $conv_lr \
+ --super_resolution_lr $conv_lr \
+ --lr $lr \
+ --kl_lambda ${kl_lambda} \
+ --bg_lamdba 0.01 \
+ --alpha_lambda ${alpha_lambda} \
+"
+
+TRAIN_FLAGS="--iterations 10001 --anneal_lr False \
+ --batch_size $batch_size --save_interval 25000 \
+ --microbatch ${microbatch} \
+ --image_size_encoder $image_size_encoder \
+ --image_size $image_size \
+ --dino_version mv-sd-dit-srt-pcd-structured-nopcd \
+ --sr_training False \
+ --encoder_cls_token False \
+ --decoder_cls_token False \
+ --cls_token False \
+ --weight_decay 0.05 \
+ --no_dim_up_mlp True \
+ --uvit_skip_encoder True \
+ --decoder_load_pretrained True \
+ --fg_mse False \
+ --vae_p 2 \
+ --plucker_embedding True \
+ --encoder_in_channels 15 \
+ --arch_dit_decoder DiT2-B/2 \
+ --sd_E_ch 64 \
+ --sd_E_num_res_blocks 1 \
+ --lrm_decoder False \
+ "
+
+
+DDPM_MODEL_FLAGS="
+--learn_sigma False \
+--num_heads 8 \
+--num_res_blocks 2 \
+--num_channels 320 \
+--attention_resolutions "4,2,1" \
+--use_spatial_transformer True \
+--transformer_depth 1 \
+--context_dim 768 \
+"
+# --pred_type x0 \
+# --iw_sample_p drop_all_uniform \
+# --loss_type x0 \
+
+# ! diffusion steps and noise schedule not used, since the continuous diffusion is adopted.
+DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear \
+--use_kl False \
+--triplane_scaling_divider ${triplane_scaling_divider} \
+--trainer_name flow_matching_gs \
+--mixed_prediction False \
+--train_vae False \
+--denoise_in_channels 3 \
+--denoise_out_channels 3 \
+--diffusion_input_size 32 \
+--diffusion_ce_anneal True \
+--create_controlnet False \
+--p_rendering_loss False \
+--pred_type x_start \
+--predict_v False \
+--create_dit True \
+--i23d False \
+--dit_model_arch DiT-PCD-L \
+--train_vae False \
+--use_eos_feature False \
+--roll_out True \
+"
+
+logdir=./logs/LSGM/t23d/FM/${dataset_name}/gs-disentangle/cascade_check/clay/stage1/clip_text/fullset_${NUM_GPUS}-${batch_size}
+
+SR_TRAIN_FLAGS_v1_2XC="
+--decoder_in_chans 32 \
+--out_chans 96 \
+--ae_classname vit.vit_triplane.pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade_x8x4x4 \
+--logdir $logdir \
+--arch_encoder vits \
+--arch_decoder vitb \
+--vit_decoder_wd 0.001 \
+--encoder_weight_decay 0.001 \
+--color_criterion mse \
+--triplane_in_chans 32 \
+--decoder_output_dim 10 \
+--resume_checkpoint /mnt/sfs-common/yslan/open-source/checkpoints/t23d/stage-1/model_joint_denoise_rec_model1950000.pt \
+"
+
+# --resume_checkpoint yslan/GaussianAnything/ckpts/checkpoints/t23d/stage-1/model_joint_denoise_rec_model1950000.pt \
+
+
+# --resume_checkpoint /nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output_fullset/model_joint_denoise_rec_model1700000.pt \
+
+# --resume_checkpoint /nas/shared/V2V/yslan/logs/nips24/LSGM/t23d/FM/9cls/gs-disentangle/cascade_check/xyz_output/model_joint_denoise_rec_model1600000.pt \
+
+SR_TRAIN_FLAGS=${SR_TRAIN_FLAGS_v1_2XC}
+
+rm -rf "$logdir"/runs
+mkdir -p "$logdir"/
+cp "$0" "$logdir"/
+
+export OMP_NUM_THREADS=12
+# export LC_ALL=en_US.UTF-8 # save caption txt bug
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export OPENCV_IO_ENABLE_OPENEXR=1
+export NCCL_IB_GID_INDEX=3 # https://github.com/huggingface/accelerate/issues/314#issuecomment-1821973930
+# export CUDA_VISIBLE_DEVICES=0,1,2
+
+export CUDA_VISIBLE_DEVICES=0
+
+torchrun --nproc_per_node=$NUM_GPUS \
+  --nnodes 1 \
+  --rdzv-endpoint=localhost:22477 \
+ scripts/vit_triplane_sit_train.py \
+ --num_workers ${num_workers} \
+ --depth_lambda 0 \
+ ${TRAIN_FLAGS}  \
+ ${SR_TRAIN_FLAGS} \
+ ${DATASET_FLAGS} \
+ ${DIFFUSION_FLAGS} \
+ ${DDPM_MODEL_FLAGS} \
+ --overfitting False \
+ --load_pretrain_encoder False \
+ --iterations 5000001 \
+ --eval_interval 5000 \
+ --decomposed True \
+ --logdir $logdir \
+ --cfg objverse_tuneray_aug_resolution_64_64_auto \
+ --patch_size ${patch_size} \
+ --eval_batch_size 1 \
+ ${LR_FLAGS} \
+ --ce_lambda ${ce_lambda} \
+ --negative_entropy_lambda ${ce_lambda} \
+ --triplane_fg_bg False \
+ --grad_clip True \
+ --interval 5 \
+ --log_interval 100 \
+ --normalize_clip_encoding True \
+ --scale_clip_encoding ${scale_clip_encoding} \
+ --mixing_logit_init 10000 \
+ --objv_dataset True \
+ --cfg_dropout_prob ${cfg_dropout_prob} \
+ --cond_key caption \
+ --use_lmdb_compressed False \
+ --use_lmdb False \
+ --use_amp True \
+ --append_xyz True \
+ --allow_tf32 True \
+ --gs_cam_format True \
+ --gs_rendering True \
+ --shuffle_across_cls True \
+ --z_channels 10 \
+ --ldm_z_channels 10 \
+ --ldm_embed_dim 10 \
+ --load_wds_diff False \
+ --load_wds_latent False \
+ --compile False \
+ --split_chunk_input True \
+ --append_depth False \
+ --mv_input True \
+ --duplicate_sample False \
+ --read_normal True \
+ --enable_mixing_normal False \
+ --use_wds False \
+ --use_chunk True \
+ --pt_ft_kl False \
+ --surfel_rendering True \
+ --clip_grad_throld 1.0 \
+ --snr-type stage1-t23d \
+ --load_pcd True \
+ --num_frames 8 \
+ --split_chunk_size 16 \
+ --load_caption_dataset True \
+ --plane_n 1 \
+ --pooling_ctx_dim 768 \
+ --pcd_path ${pcd_path} \
+ --mv_latent_dir ${mv_latent_dir}
diff --git a/third_party/diff-surfel-rasterization b/third_party/diff-surfel-rasterization
new file mode 160000
index 0000000000000000000000000000000000000000..e0ed0207b3e0669960cfad70852200a4a5847f61
--- /dev/null
+++ b/third_party/diff-surfel-rasterization
@@ -0,0 +1 @@
+Subproject commit e0ed0207b3e0669960cfad70852200a4a5847f61
diff --git a/torch_utils/CLIP.png b/torch_utils/CLIP.png
new file mode 100644
index 0000000000000000000000000000000000000000..a1b5ec9171fd7a51e36e845a02304eb837142ba1
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diff --git a/torch_utils/__init__.py b/torch_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfebd04f47e6f6b1b44984c14c23b57d56f72240
--- /dev/null
+++ b/torch_utils/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+# empty
diff --git a/torch_utils/__pycache__/__init__.cpython-39.pyc b/torch_utils/__pycache__/__init__.cpython-39.pyc
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diff --git a/torch_utils/__pycache__/persistence.cpython-39.pyc b/torch_utils/__pycache__/persistence.cpython-39.pyc
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diff --git a/torch_utils/clip_practice.py b/torch_utils/clip_practice.py
new file mode 100644
index 0000000000000000000000000000000000000000..eeb7890aabafd7d43287824d6227e2bc8124c5f0
--- /dev/null
+++ b/torch_utils/clip_practice.py
@@ -0,0 +1,38 @@
+import torch
+import clip
+from PIL import Image
+
+from pdb import set_trace as st
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/16", device=device)
+
+image = preprocess(Image.open("torch_utils/CLIP.png")).unsqueeze(0).to(device)
+text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
+
+# with torch.no_grad():
+#     image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    
+#     logits_per_image, logits_per_text = model(image, text)
+#     probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+with torch.no_grad():
+    x = image.type(model.dtype) # 1 3 224 224
+    self = model.visual
+    x = self.conv1(x)  # shape = [*, width, grid, grid]
+    x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+    x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+    x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
+    x = x + self.positional_embedding.to(x.dtype)
+    x = self.ln_pre(x)
+
+    x = x.permute(1, 0, 2)  # NLD -> LND
+    x = self.transformer(x)
+    x = x.permute(1, 0, 2)  # LND -> NLD , 1, 50, 768
+    st()
+
+    pass
+
+
+print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]
\ No newline at end of file
diff --git a/torch_utils/components.py b/torch_utils/components.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7b9aa055759bd0c6972abe790304621d036789e
--- /dev/null
+++ b/torch_utils/components.py
@@ -0,0 +1,445 @@
+# https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+# https://github.com/JingyunLiang/SwinIR/blob/main/models/network_swinir.py#L812
+
+import copy
+import math
+from collections import namedtuple
+from contextlib import contextmanager, nullcontext
+from functools import partial, wraps
+from pathlib import Path
+from random import random
+
+from einops import rearrange, repeat, reduce, pack, unpack
+
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+from torch import einsum, nn
+from beartype.typing import List, Union
+from beartype import beartype
+from tqdm.auto import tqdm
+from pdb import set_trace as st
+
+# helper functions, from:
+# https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+
+
+def exists(val):
+    return val is not None
+
+
+def identity(t, *args, **kwargs):
+    return t
+
+
+def divisible_by(numer, denom):
+    return (numer % denom) == 0
+
+
+def first(arr, d=None):
+    if len(arr) == 0:
+        return d
+    return arr[0]
+
+
+def maybe(fn):
+    @wraps(fn)
+    def inner(x):
+        if not exists(x):
+            return x
+        return fn(x)
+
+    return inner
+
+
+def once(fn):
+    called = False
+
+    @wraps(fn)
+    def inner(x):
+        nonlocal called
+        if called:
+            return
+        called = True
+        return fn(x)
+
+    return inner
+
+
+print_once = once(print)
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if callable(d) else d
+
+
+def compact(input_dict):
+    return {key: value for key, value in input_dict.items() if exists(value)}
+
+
+def maybe_transform_dict_key(input_dict, key, fn):
+    if key not in input_dict:
+        return input_dict
+
+    copied_dict = input_dict.copy()
+    copied_dict[key] = fn(copied_dict[key])
+    return copied_dict
+
+
+def cast_uint8_images_to_float(images):
+    if not images.dtype == torch.uint8:
+        return images
+    return images / 255
+
+
+def module_device(module):
+    return next(module.parameters()).device
+
+
+def zero_init_(m):
+    nn.init.zeros_(m.weight)
+    if exists(m.bias):
+        nn.init.zeros_(m.bias)
+
+
+def eval_decorator(fn):
+    def inner(model, *args, **kwargs):
+        was_training = model.training
+        model.eval()
+        out = fn(model, *args, **kwargs)
+        model.train(was_training)
+        return out
+
+    return inner
+
+
+def pad_tuple_to_length(t, length, fillvalue=None):
+    remain_length = length - len(t)
+    if remain_length <= 0:
+        return t
+    return (*t, *((fillvalue, ) * remain_length))
+
+
+# helper classes
+
+
+class Identity(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+
+# tensor helpers
+
+
+def log(t, eps: float = 1e-12):
+    return torch.log(t.clamp(min=eps))
+
+
+def l2norm(t):
+    return F.normalize(t, dim=-1)
+
+
+def right_pad_dims_to(x, t):
+    padding_dims = x.ndim - t.ndim
+    if padding_dims <= 0:
+        return t
+    return t.view(*t.shape, *((1, ) * padding_dims))
+
+
+def masked_mean(t, *, dim, mask=None):
+    if not exists(mask):
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, 'b n -> b n 1')
+    masked_t = t.masked_fill(~mask, 0.)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
+
+
+def resize_image_to(image,
+                    target_image_size,
+                    clamp_range=None,
+                    mode='nearest'):
+    orig_image_size = image.shape[-1]
+
+    if orig_image_size == target_image_size:
+        return image
+
+    out = F.interpolate(image, target_image_size, mode=mode)
+
+    if exists(clamp_range):
+        out = out.clamp(*clamp_range)
+
+    return out
+
+
+def calc_all_frame_dims(downsample_factors: List[int], frames):
+    if not exists(frames):
+        return (tuple(), ) * len(downsample_factors)
+
+    all_frame_dims = []
+
+    for divisor in downsample_factors:
+        assert divisible_by(frames, divisor)
+        all_frame_dims.append((frames // divisor, ))
+
+    return all_frame_dims
+
+
+def safe_get_tuple_index(tup, index, default=None):
+    if len(tup) <= index:
+        return default
+    return tup[index]
+
+
+# image normalization functions
+# ddpms expect images to be in the range of -1 to 1
+
+
+def normalize_neg_one_to_one(img):
+    return img * 2 - 1
+
+
+def unnormalize_zero_to_one(normed_img):
+    return (normed_img + 1) * 0.5
+
+
+# def Upsample(dim, dim_out=None):
+#     dim_out = default(dim_out, dim)
+
+#     return nn.Sequential(nn.Upsample(scale_factor=2, mode='nearest'),
+#                          nn.Conv2d(dim, dim_out, 3, padding=1))
+
+
+
+class PixelShuffleUpsample(nn.Module):
+    """
+    code shared by @MalumaDev at DALLE2-pytorch for addressing checkboard artifacts
+    https://arxiv.org/ftp/arxiv/papers/1707/1707.02937.pdf
+    """
+    def __init__(self, dim, dim_out=None):
+        super().__init__()
+        dim_out = default(dim_out, dim)
+        conv = nn.Conv2d(dim, dim_out * 4, 1)
+
+        self.net = nn.Sequential(conv, nn.SiLU(), nn.PixelShuffle(2))
+
+        self.init_conv_(conv)
+
+    def init_conv_(self, conv):
+        o, i, h, w = conv.weight.shape
+        conv_weight = torch.empty(o // 4, i, h, w)
+        nn.init.kaiming_uniform_(conv_weight)
+        conv_weight = repeat(conv_weight, 'o ... -> (o 4) ...')
+
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_out,
+                 dim_inter=None,
+                 use_norm=True,
+                 norm_layer=nn.BatchNorm2d,
+                 bias=False):
+        super().__init__()
+        if dim_inter is None:
+            dim_inter = dim_out
+
+        if use_norm:
+            self.conv = nn.Sequential(
+                norm_layer(dim_in),
+                nn.ReLU(True),
+                nn.Conv2d(dim_in,
+                          dim_inter,
+                          3,
+                          1,
+                          1,
+                          bias=bias,
+                          padding_mode='reflect'),
+                norm_layer(dim_inter),
+                nn.ReLU(True),
+                nn.Conv2d(dim_inter,
+                          dim_out,
+                          3,
+                          1,
+                          1,
+                          bias=bias,
+                          padding_mode='reflect'),
+            )
+        else:
+            self.conv = nn.Sequential(
+                nn.ReLU(True),
+                nn.Conv2d(dim_in, dim_inter, 3, 1, 1),
+                nn.ReLU(True),
+                nn.Conv2d(dim_inter, dim_out, 3, 1, 1),
+            )
+
+        self.short_cut = None
+        if dim_in != dim_out:
+            self.short_cut = nn.Conv2d(dim_in, dim_out, 1, 1)
+
+    def forward(self, feats):
+        feats_out = self.conv(feats)
+        if self.short_cut is not None:
+            feats_out = self.short_cut(feats) + feats_out
+        else:
+            feats_out = feats_out + feats
+        return feats_out
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. '
+                             'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+class PixelUnshuffleUpsample(nn.Module):
+    def __init__(self, output_dim, num_feat=128, num_out_ch=3, sr_ratio=4, *args, **kwargs) -> None:
+        super().__init__()
+
+        self.conv_after_body = nn.Conv2d(output_dim, output_dim, 3, 1, 1)
+        self.conv_before_upsample = nn.Sequential(
+            nn.Conv2d(output_dim, num_feat, 3, 1, 1),
+            nn.LeakyReLU(inplace=True))
+        self.upsample = Upsample(sr_ratio, num_feat)  # 4 time SR
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+    def forward(self, x, input_skip_connection=True, *args, **kwargs):
+        # x = self.conv_first(x)
+        if input_skip_connection:
+            x = self.conv_after_body(x) + x
+        else:
+            x = self.conv_after_body(x)
+
+        x = self.conv_before_upsample(x)
+        x = self.conv_last(self.upsample(x))
+        return x
+
+
+class Conv3x3TriplaneTransformation(nn.Module):
+    # used in the final layer before triplane
+    def __init__(self, input_dim, output_dim) -> None:
+        super().__init__()
+
+        self.conv_after_unpachify = nn.Sequential(
+            nn.Conv2d(input_dim, output_dim, 3, 1, 1),
+            nn.LeakyReLU(inplace=True)
+        )
+
+        self.conv_before_rendering = nn.Sequential(
+            nn.Conv2d(output_dim, output_dim, 3, 1, 1),
+            nn.LeakyReLU(inplace=True))
+
+    def forward(self, unpachified_latent):
+        latent = self.conv_after_unpachify(unpachified_latent) # no residual connections here
+        latent = self.conv_before_rendering(latent) + latent
+        return latent
+
+
+# https://github.com/JingyunLiang/SwinIR/blob/6545850fbf8df298df73d81f3e8cba638787c8bd/models/network_swinir.py#L750
+class NearestConvSR(nn.Module):
+    """
+    code shared by @MalumaDev at DALLE2-pytorch for addressing checkboard artifacts
+    https://arxiv.org/ftp/arxiv/papers/1707/1707.02937.pdf
+    """
+    def __init__(self, output_dim, num_feat=128, num_out_ch=3, sr_ratio=4, *args, **kwargs) -> None:
+        super().__init__()
+
+        self.upscale = sr_ratio
+
+        self.conv_after_body = nn.Conv2d(output_dim, output_dim, 3, 1, 1)
+        self.conv_before_upsample = nn.Sequential(nn.Conv2d(output_dim, num_feat, 3, 1, 1),
+                                                    nn.LeakyReLU(inplace=True))
+        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        if self.upscale == 4:
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+    
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x, *args, **kwargs):
+
+        # x = self.conv_first(x)
+        x = self.conv_after_body(x) + x
+        x = self.conv_before_upsample(x)
+        x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+        if self.upscale == 4:
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+        x = self.conv_last(self.lrelu(self.conv_hr(x)))
+
+
+        return x
+
+# https://github.com/yumingj/C2-Matching/blob/fa171ca6707c6f16a5d04194ce866ea70bb21d2b/mmsr/models/archs/ref_restoration_arch.py#L65
+class NearestConvSR_Residual(NearestConvSR):
+    # learn residual + normalize
+    
+    def __init__(self, output_dim, num_feat=128, num_out_ch=3, sr_ratio=4, *args, **kwargs) -> None:
+        super().__init__(output_dim, num_feat, num_out_ch, sr_ratio, *args, **kwargs)
+        # self.mean = torch.Tensor((0.485, 0.456, 0.406)).view(1,3,1,1) # imagenet mean
+        self.act = nn.Tanh()
+
+    def forward(self, x, base_x, *args, **kwargs):
+        # base_x: low-resolution 3D rendering, for residual addition
+        # self.mean = self.mean.type_as(x)
+        # x = super().forward(x).clamp(-1,1) 
+        x = super().forward(x)
+        x = self.act(x) # residual normalize to [-1,1]
+        scale = x.shape[-1] // base_x.shape[-1] # 2 or 4
+        x = x + F.interpolate(base_x, None, scale, 'bilinear', False) # add residual; [-1,1] range
+
+        # return x  + 2 * self.mean
+        return x
+    
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+
+# class PixelShuffledDirect(nn.Module):
diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7dabe00493504ffb30ab8962b8c975b13d5c0d9
--- /dev/null
+++ b/torch_utils/custom_ops.py
@@ -0,0 +1,187 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+import glob
+import hashlib
+import importlib
+import os
+import re
+import shutil
+import uuid
+
+import torch
+import torch.utils.cpp_extension
+from torch.utils.file_baton import FileBaton
+
+#----------------------------------------------------------------------------
+# Global options.
+
+verbosity = 'brief'  # Verbosity level: 'none', 'brief', 'full'
+
+#----------------------------------------------------------------------------
+# Internal helper funcs.
+
+
+def _find_compiler_bindir():
+    patterns = [
+        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
+    ]
+    for pattern in patterns:
+        matches = sorted(glob.glob(pattern))
+        if len(matches):
+            return matches[-1]
+    return None
+
+
+#----------------------------------------------------------------------------
+
+
+def _get_mangled_gpu_name():
+    name = torch.cuda.get_device_name().lower()
+    out = []
+    for c in name:
+        if re.match('[a-z0-9_-]+', c):
+            out.append(c)
+        else:
+            out.append('-')
+    return ''.join(out)
+
+
+#----------------------------------------------------------------------------
+# Main entry point for compiling and loading C++/CUDA plugins.
+
+_cached_plugins = dict()
+
+
+def get_plugin(module_name,
+               sources,
+               headers=None,
+               source_dir=None,
+               **build_kwargs):
+    assert verbosity in ['none', 'brief', 'full']
+    if headers is None:
+        headers = []
+    if source_dir is not None:
+        sources = [os.path.join(source_dir, fname) for fname in sources]
+        headers = [os.path.join(source_dir, fname) for fname in headers]
+
+    # Already cached?
+    if module_name in _cached_plugins:
+        return _cached_plugins[module_name]
+
+    # Print status.
+    if verbosity == 'full':
+        print(f'Setting up PyTorch plugin "{module_name}"...')
+    elif verbosity == 'brief':
+        print(f'Setting up PyTorch plugin "{module_name}"... ',
+              end='',
+              flush=True)
+    verbose_build = (verbosity == 'full')
+
+    # Compile and load.
+    try:  # pylint: disable=too-many-nested-blocks
+        # Make sure we can find the necessary compiler binaries.
+        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
+            compiler_bindir = _find_compiler_bindir()
+            if compiler_bindir is None:
+                raise RuntimeError(
+                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".'
+                )
+            os.environ['PATH'] += ';' + compiler_bindir
+
+        # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
+        # break the build or unnecessarily restrict what's available to nvcc.
+        # Unset it to let nvcc decide based on what's available on the
+        # machine.
+        os.environ['TORCH_CUDA_ARCH_LIST'] = ''
+
+        # Incremental build md5sum trickery.  Copies all the input source files
+        # into a cached build directory under a combined md5 digest of the input
+        # source files.  Copying is done only if the combined digest has changed.
+        # This keeps input file timestamps and filenames the same as in previous
+        # extension builds, allowing for fast incremental rebuilds.
+        #
+        # This optimization is done only in case all the source files reside in
+        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
+        # environment variable is set (we take this as a signal that the user
+        # actually cares about this.)
+        #
+        # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
+        # around the *.cu dependency bug in ninja config.
+        #
+        all_source_files = sorted(sources + headers)
+        all_source_dirs = set(
+            os.path.dirname(fname) for fname in all_source_files)
+        if len(all_source_dirs
+               ) == 1:  # and ('TORCH_EXTENSIONS_DIR' in os.environ):
+
+            # Compute combined hash digest for all source files.
+            hash_md5 = hashlib.md5()
+            for src in all_source_files:
+                with open(src, 'rb') as f:
+                    hash_md5.update(f.read())
+
+            # Select cached build directory name.
+            source_digest = hash_md5.hexdigest()
+            build_top_dir = torch.utils.cpp_extension._get_build_directory(
+                module_name, verbose=verbose_build)  # pylint: disable=protected-access
+            cached_build_dir = os.path.join(
+                build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
+
+            if not os.path.isdir(cached_build_dir):
+                tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
+                os.makedirs(tmpdir)
+                for src in all_source_files:
+                    shutil.copyfile(
+                        src, os.path.join(tmpdir, os.path.basename(src)))
+                try:
+                    os.replace(tmpdir, cached_build_dir)  # atomic
+                except OSError:
+                    # source directory already exists, delete tmpdir and its contents.
+                    shutil.rmtree(tmpdir)
+                    if not os.path.isdir(cached_build_dir): raise
+
+            # Compile.
+            cached_sources = [
+                os.path.join(cached_build_dir, os.path.basename(fname))
+                for fname in sources
+            ]
+            torch.utils.cpp_extension.load(name=module_name,
+                                           build_directory=cached_build_dir,
+                                           verbose=verbose_build,
+                                           sources=cached_sources,
+                                           **build_kwargs)
+        else:
+            torch.utils.cpp_extension.load(name=module_name,
+                                           verbose=verbose_build,
+                                           sources=sources,
+                                           **build_kwargs)
+
+        # Load.
+        module = importlib.import_module(module_name)
+
+    except:
+        if verbosity == 'brief':
+            print('Failed!')
+        raise
+
+    # Print status and add to cache dict.
+    if verbosity == 'full':
+        print(f'Done setting up PyTorch plugin "{module_name}".')
+    elif verbosity == 'brief':
+        print('Done.')
+    _cached_plugins[module_name] = module
+    return module
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ddp_practice.py b/torch_utils/ddp_practice.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc96d0d39aebd68e66f4cf2292f29589108c34ea
--- /dev/null
+++ b/torch_utils/ddp_practice.py
@@ -0,0 +1,285 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from argparse import ArgumentParser
+import sys
+import os
+
+sys.path.append('..')
+sys.path.append('.')
+
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, Dataset
+from torch.utils.data.distributed import DistributedSampler
+
+from vit.vision_transformer import VisionTransformer as ViT
+from vit.vit_triplane import ViTTriplane
+from guided_diffusion import dist_util, logger
+
+import click
+import dnnlib
+
+SEED = 42
+BATCH_SIZE = 8
+NUM_EPOCHS = 1
+
+
+class YourDataset(Dataset):
+    def __init__(self):
+        pass
+
+
+@click.command()
+@click.option('--cfg', help='Base configuration', type=str, default='ffhq')
+@click.option('--sr-module',
+              help='Superresolution module override',
+              metavar='STR',
+              required=False,
+              default=None)
+@click.option('--density_reg',
+              help='Density regularization strength.',
+              metavar='FLOAT',
+              type=click.FloatRange(min=0),
+              default=0.25,
+              required=False,
+              show_default=True)
+@click.option('--density_reg_every',
+              help='lazy density reg',
+              metavar='int',
+              type=click.FloatRange(min=1),
+              default=4,
+              required=False,
+              show_default=True)
+@click.option('--density_reg_p_dist',
+              help='density regularization strength.',
+              metavar='FLOAT',
+              type=click.FloatRange(min=0),
+              default=0.004,
+              required=False,
+              show_default=True)
+@click.option('--reg_type',
+              help='Type of regularization',
+              metavar='STR',
+              type=click.Choice([
+                  'l1', 'l1-alt', 'monotonic-detach', 'monotonic-fixed',
+                  'total-variation'
+              ]),
+              required=False,
+              default='l1')
+@click.option('--decoder_lr_mul',
+              help='decoder learning rate multiplier.',
+              metavar='FLOAT',
+              type=click.FloatRange(min=0),
+              default=1,
+              required=False,
+              show_default=True)
+@click.option('--c_scale',
+              help='Scale factor for generator pose conditioning.',
+              metavar='FLOAT',
+              type=click.FloatRange(min=0),
+              required=False,
+              default=1)
+def main(**kwargs):
+    # parser = ArgumentParser('DDP usage example')
+    # parser.add_argument('--local_rank', type=int, default=-1, metavar='N', help='Local process rank.')  # you need this argument in your scripts for DDP to work
+    # args = parser.parse_args()
+
+    opts = dnnlib.EasyDict(kwargs)  # Command line arguments.
+    c = dnnlib.EasyDict()  # Main config dict.
+
+    rendering_options = {
+        # 'image_resolution': c.training_set_kwargs.resolution,
+        'image_resolution': 256,
+        'disparity_space_sampling': False,
+        'clamp_mode': 'softplus',
+        # 'superresolution_module': sr_module,
+        # 'c_gen_conditioning_zero': not opts.
+        # gen_pose_cond,  # if true, fill generator pose conditioning label with dummy zero vector
+        # 'gpc_reg_prob': opts.gpc_reg_prob if opts.gen_pose_cond else None,
+        'c_scale':
+        opts.c_scale,  # mutliplier for generator pose conditioning label
+        # 'superresolution_noise_mode': opts.
+        # sr_noise_mode,  # [random or none], whether to inject pixel noise into super-resolution layers
+        'density_reg': opts.density_reg,  # strength of density regularization
+        'density_reg_p_dist': opts.
+        density_reg_p_dist,  # distance at which to sample perturbed points for density regularization
+        'reg_type': opts.
+        reg_type,  # for experimenting with variations on density regularization
+        'decoder_lr_mul':
+        opts.decoder_lr_mul,  # learning rate multiplier for decoder
+        'sr_antialias': True,
+        'return_triplane_features': True,  # for DDF supervision
+        'return_sampling_details_flag': True,
+    }
+
+    if opts.cfg == 'ffhq':
+        rendering_options.update({
+            'focal': 2985.29 / 700,
+            'depth_resolution':
+            # 48,  # number of uniform samples to take per ray.
+            36,  # number of uniform samples to take per ray.
+            'depth_resolution_importance':
+            # 48,  # number of importance samples to take per ray.
+            36,  # number of importance samples to take per ray.
+            'ray_start':
+            2.25,  # near point along each ray to start taking samples.
+            'ray_end':
+            3.3,  # far point along each ray to stop taking samples. 
+            'box_warp':
+            1,  # the side-length of the bounding box spanned by the tri-planes; box_warp=1 means [-0.5, -0.5, -0.5] -> [0.5, 0.5, 0.5].
+            'avg_camera_radius':
+            2.7,  # used only in the visualizer to specify camera orbit radius.
+            'avg_camera_pivot': [
+                0, 0, 0.2
+            ],  # used only in the visualizer to control center of camera rotation.
+        })
+    elif opts.cfg == 'afhq':
+        rendering_options.update({
+            'focal': 4.2647,
+            'depth_resolution': 48,
+            'depth_resolution_importance': 48,
+            'ray_start': 2.25,
+            'ray_end': 3.3,
+            'box_warp': 1,
+            'avg_camera_radius': 2.7,
+            'avg_camera_pivot': [0, 0, -0.06],
+        })
+    elif opts.cfg == 'shapenet':
+        rendering_options.update({
+            'depth_resolution': 64,
+            'depth_resolution_importance': 64,
+            # 'ray_start': 0.1,
+            # 'ray_end': 2.6,
+            'ray_start': 0.1,
+            'ray_end': 3.3,
+            'box_warp': 1.6,
+            'white_back': True,
+            'avg_camera_radius': 1.7,
+            'avg_camera_pivot': [0, 0, 0],
+        })
+    else:
+        assert False, "Need to specify config"
+
+    c.rendering_kwargs = rendering_options
+
+    args = opts
+
+    # keep track of whether the current process is the `master` process (totally optional, but I find it useful for data laoding, logging, etc.)
+    args.local_rank = int(os.environ["LOCAL_RANK"])
+    args.is_master = args.local_rank == 0
+
+    # set the device
+    # device = torch.cuda.device(args.local_rank)
+    device = torch.device(f"cuda:{args.local_rank}")
+
+    # initialize PyTorch distributed using environment variables (you could also do this more explicitly by specifying `rank` and `world_size`, but I find using environment variables makes it so that you can easily use the same script on different machines)
+    dist.init_process_group(backend='nccl',
+                            init_method='env://',
+                            rank=args.local_rank,
+                            world_size=torch.cuda.device_count())
+    print(f"{args.local_rank=} init complete")
+    torch.cuda.set_device(args.local_rank)
+
+    # set the seed for all GPUs (also make sure to set the seed for random, numpy, etc.)
+    torch.cuda.manual_seed_all(SEED)
+
+    # initialize your model (BERT in this example)
+    # model = BertForMaskedLM.from_pretrained('bert-base-uncased')
+
+    # model = ViT(
+    #     image_size = 256,
+    #     patch_size = 32,
+    #     num_classes = 1000,
+    #     dim = 1024,
+    #     depth = 6,
+    #     heads = 16,
+    #     mlp_dim = 2048,
+    #     dropout = 0.1,
+    #     emb_dropout = 0.1
+    # )
+
+    # TODO, check pre-trained ViT encoder cfgs
+    model = ViTTriplane(
+        img_size=[224],
+        patch_size=16,
+        in_chans=384,
+        num_classes=0,
+        embed_dim=384,  # Check ViT encoder dim
+        depth=2,
+        num_heads=16,
+        mlp_ratio=4.,
+        qkv_bias=False,
+        qk_scale=None,
+        drop_rate=0.1,
+        attn_drop_rate=0.,
+        drop_path_rate=0.,
+        norm_layer=nn.LayerNorm,
+        out_chans=96,
+        c_dim=25,  # Conditioning label (C) dimensionality.
+        img_resolution=128,  # Output resolution.
+        img_channels=3,  # Number of output color channels.
+        cls_token=False,
+        # TODO, replace with c
+        rendering_kwargs=c.rendering_kwargs,
+    )
+    # noise = torch.randn(1, 8, 8, 1024)
+
+    # send your model to GPU
+    model = model.to(device)
+
+    # initialize distributed data parallel (DDP)
+    model = DDP(model,
+                device_ids=[args.local_rank],
+                output_device=args.local_rank)
+
+    dist_util.sync_params(model.named_parameters())
+
+    # # initialize your dataset
+    # dataset = YourDataset()
+
+    # # initialize the DistributedSampler
+    # sampler = DistributedSampler(dataset)
+
+    # # initialize the dataloader
+    # dataloader = DataLoader(
+    #     dataset=dataset,
+    #     sampler=sampler,
+    #     batch_size=BATCH_SIZE
+    # )
+
+    # start your training!
+    for epoch in range(NUM_EPOCHS):
+        # put model in train mode
+        model.train()
+
+        # let all processes sync up before starting with a new epoch of training
+        dist.barrier()
+
+        noise = torch.randn(1, 14 * 14, 384).to(device)  # B, L, C
+        img = model(noise, torch.zeros(1, 25).to(device))
+        print(img['image'].shape)
+    # st()
+
+    # img = torch.randn(1, 3, 256, 256).to(device)
+
+    # preds = model(img)
+    # print(preds.shape)
+    # assert preds.shape == (1, 1000), 'correct logits outputted'
+
+    # for step, batch in enumerate(dataloader):
+    #     # send batch to device
+    #     batch = tuple(t.to(args.device) for t in batch)
+
+    #     # forward pass
+    #     outputs = model(*batch)
+
+    #     # compute loss
+    #     loss = outputs[0]
+
+    #     # etc.
+
+
+if __name__ == '__main__':
+    main()
diff --git a/torch_utils/dist_practice.py b/torch_utils/dist_practice.py
new file mode 100644
index 0000000000000000000000000000000000000000..e103870819b989b1ea1ed5f1f9deeecb436cf9e3
--- /dev/null
+++ b/torch_utils/dist_practice.py
@@ -0,0 +1,43 @@
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import os
+
+
+def find_free_port():
+    """ https://stackoverflow.com/questions/1365265/on-localhost-how-do-i-pick-a-free-port-number """
+    import socket
+    from contextlib import closing
+
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
+        s.bind(('', 0))
+        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        return str(s.getsockname()[1])
+
+
+def setup_process(rank, master_addr, master_port, world_size, backend='nccl'):
+    print(f'setting up {rank=} {world_size=} {backend=}')
+
+    # set up the master's ip address so this child process can coordinate
+    os.environ['MASTER_ADDR'] = master_addr
+    os.environ['MASTER_PORT'] = master_port
+    print(f"{master_addr=} {master_port=}")
+
+    # Initializes the default distributed process group, and this will also initialize the distributed package.
+    dist.init_process_group(backend, rank=rank, world_size=world_size)
+    print(f"{rank=} init complete")
+    dist.destroy_process_group()
+    print(f"{rank=} destroy complete")
+
+
+if __name__ == '__main__':
+    world_size = 2
+    master_addr = '127.0.0.1'
+    master_port = find_free_port()
+    mp.spawn(setup_process,
+             args=(
+                 master_addr,
+                 master_port,
+                 world_size,
+             ),
+             nprocs=world_size)
diff --git a/torch_utils/distributions/__init__.py b/torch_utils/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/torch_utils/distributions/__pycache__/__init__.cpython-39.pyc b/torch_utils/distributions/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e78de3043b783d4bcb48bc9a04a7eb65243ed17
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diff --git a/torch_utils/distributions/__pycache__/distributions.cpython-39.pyc b/torch_utils/distributions/__pycache__/distributions.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ddef35c3cabc41b394f47955362550ab4b0794ef
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diff --git a/torch_utils/distributions/distributions.py b/torch_utils/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..13112924abd5dab81952bca8590d87765ac29e38
--- /dev/null
+++ b/torch_utils/distributions/distributions.py
@@ -0,0 +1,149 @@
+# https://raw.githubusercontent.com/CompVis/latent-diffusion/e66308c7f2e64cb581c6d27ab6fbeb846828253b/ldm/modules/distributions/distributions.py
+
+import torch
+import numpy as np
+from pdb import set_trace as st
+
+
+class AbstractDistribution:
+
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+@torch.jit.script
+def soft_clamp20(x: torch.Tensor):
+    return x.div(20.).tanh().mul(
+        20.
+    )  # 5. * torch.tanh(x / 5.) <--> soft differentiable clamp between [-5, 5]
+
+
+# @torch.jit.script
+# def soft_clamp(x: torch.Tensor, a: torch.Tensor):
+#     return x.div(a).tanh_().mul(a)
+
+
+class DiagonalGaussianDistribution(object):
+
+    def __init__(self, parameters, deterministic=False, soft_clamp=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+
+        if soft_clamp:
+            # self.mean, self.logvar = soft_clamp5(self.mean), soft_clamp5(self.logvar) # as in LSGM, bound the range. needs re-training?
+            self.logvar = soft_clamp20(
+                self.logvar)  # as in LSGM, bound the range. [-20, 20]
+        else:
+            self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(
+                self.mean).to(device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(
+            self.mean.shape).to(device=self.parameters.device)
+        return x
+
+    # https://github.dev/NVlabs/LSGM/util/distributions.py
+    def log_p(self, samples):
+        # for calculating the negative encoder entropy term
+        normalized_samples = (samples - self.mean) / self.var
+        log_p = -0.5 * normalized_samples * normalized_samples - 0.5 * np.log(
+            2 * np.pi) - self.logvar  #
+
+        return log_p  # ! TODO
+
+    def normal_entropy(self):
+        # for calculating normal entropy. Motivation: supervise logvar directly.
+        # normalized_samples = (samples - self.mean) / self.var
+        # log_p = - 0.5 * normalized_samples * normalized_samples - 0.5 * np.log(2 * np.pi) - self.logvar #
+        # entropy = torch.sum(self.logvar + 0.5 * (np.log(2 * np.pi) + 1),
+        #                     dim=[1, 2, 3]).mean(0)
+        # entropy = torch.mean(self.logvar + 0.5 * (np.log(2 * np.pi) + 1)) # follow eps loss tradition here, average overall dims.
+        entropy = self.logvar + 0.5 * (np.log(2 * np.pi) + 1) # follow eps loss tradition here, average overall dims.
+                            
+
+        return entropy  # ! TODO
+
+    def kl(self, other=None, pt_ft_separate=False, ft_separate=False):
+
+        def kl_fn(mean, var, logvar):
+            return 0.5 * torch.sum(
+                torch.pow(mean, 2) + var - 1.0 - logvar,
+                dim=list(range(1,mean.ndim))) # support B L C-like VAE latent
+
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                if pt_ft_separate: # as in LION
+                    pt_kl = kl_fn(self.mean[:, :3], self.var[:, :3], self.logvar[:, :3]) # (B C L) input
+                    ft_kl = kl_fn(self.mean[:, 3:], self.var[:, 3:], self.logvar[:, 3:]) # (B C L) input
+                    return pt_kl, ft_kl
+                elif ft_separate:
+                    ft_kl = kl_fn(self.mean[:, :], self.var[:, :], self.logvar[:, :]) # (B C L) input
+                    return ft_kl
+                else:
+                    return 0.5 * torch.sum(
+                        torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                        dim=list(range(1,self.mean.ndim))) # support B L C-like VAE latent
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var +
+                    self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1, 2, 3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(logtwopi + self.logvar +
+                               torch.pow(sample - self.mean, 2) / self.var,
+                               dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) +
+                  ((mean1 - mean2)**2) * torch.exp(-logvar2))
diff --git a/torch_utils/inference_matt.py b/torch_utils/inference_matt.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b80917995a2fe280a08029fcfe8f88e5973fcf9
--- /dev/null
+++ b/torch_utils/inference_matt.py
@@ -0,0 +1,139 @@
+# https://github.com/xinntao/facexlib/blob/master/inference/inference_matting.py
+
+from tqdm import tqdm, trange
+import argparse
+from pathlib import Path
+import cv2
+import numpy as np
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+
+from facexlib.matting import init_matting_model
+from facexlib.utils import img2tensor
+
+
+def matt_single(args):
+    modnet = init_matting_model()
+
+    # read image
+    img = cv2.imread(args.img_path) / 255.
+    # unify image channels to 3
+    if len(img.shape) == 2:
+        img = img[:, :, None]
+    if img.shape[2] == 1:
+        img = np.repeat(img, 3, axis=2)
+    elif img.shape[2] == 4:
+        img = img[:, :, 0:3]
+
+    img_t = img2tensor(img, bgr2rgb=True, float32=True)
+    normalize(img_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+    img_t = img_t.unsqueeze(0).cuda()
+
+    # resize image for input
+    _, _, im_h, im_w = img_t.shape
+    ref_size = 512
+    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
+        if im_w >= im_h:
+            im_rh = ref_size
+            im_rw = int(im_w / im_h * ref_size)
+        elif im_w < im_h:
+            im_rw = ref_size
+            im_rh = int(im_h / im_w * ref_size)
+    else:
+        im_rh = im_h
+        im_rw = im_w
+    im_rw = im_rw - im_rw % 32
+    im_rh = im_rh - im_rh % 32
+    img_t = F.interpolate(img_t, size=(im_rh, im_rw), mode='area')
+
+    # inference
+    _, _, matte = modnet(img_t, True)
+
+    # resize and save matte
+    matte = F.interpolate(matte, size=(im_h, im_w), mode='area')
+    matte = matte[0][0].data.cpu().numpy()
+    cv2.imwrite(args.save_path, (matte * 255).astype('uint8'))
+
+    # get foreground
+    matte = matte[:, :, None]
+    foreground = img * matte + np.full(img.shape, 1) * (1 - matte)
+    cv2.imwrite(args.save_path.replace('.png', '_fg.png'), foreground * 255)
+
+def matt_directory(args): # for extracting ffhq imgs foreground 
+    modnet = init_matting_model()
+
+    all_imgs = list(Path(args.img_dir_path).rglob('*.png'))
+    print('all imgs: ', len(all_imgs))
+
+    tgt_dir_path = '/mnt/lustre/share/yslan/ffhq/unzipped_ffhq_matte/'
+    # tgt_img_path = '/mnt/lustre/share/yslan/ffhq/unzipped_ffhq_matting/'
+
+    for img_path in tqdm(all_imgs):
+
+        # read image
+        # img = cv2.imread(args.img_path) / 255.
+        img = cv2.imread(str(img_path)) / 255.
+
+        relative_img_path = Path(img_path).relative_to('/mnt/lustre/share/yslan/ffhq/unzipped_ffhq_512/')
+        tgt_save_path = tgt_dir_path / relative_img_path
+
+        (tgt_save_path.parent).mkdir(parents=True, exist_ok=True)
+
+        # unify image channels to 3
+        if len(img.shape) == 2:
+            img = img[:, :, None]
+        if img.shape[2] == 1:
+            img = np.repeat(img, 3, axis=2)
+        elif img.shape[2] == 4:
+            img = img[:, :, 0:3]
+
+        img_t = img2tensor(img, bgr2rgb=True, float32=True)
+        normalize(img_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+        img_t = img_t.unsqueeze(0).cuda()
+
+        # resize image for input
+        _, _, im_h, im_w = img_t.shape
+        ref_size = 512
+        if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
+            if im_w >= im_h:
+                im_rh = ref_size
+                im_rw = int(im_w / im_h * ref_size)
+            elif im_w < im_h:
+                im_rw = ref_size
+                im_rh = int(im_h / im_w * ref_size)
+        else:
+            im_rh = im_h
+            im_rw = im_w
+        im_rw = im_rw - im_rw % 32
+        im_rh = im_rh - im_rh % 32
+        img_t = F.interpolate(img_t, size=(im_rh, im_rw), mode='area')
+
+        # inference
+        _, _, matte = modnet(img_t, True)
+
+        # resize and save matte
+        matte = F.interpolate(matte, size=(im_h, im_w), mode='area')
+        matte = matte[0][0].data.cpu().numpy()
+        # cv2.imwrite(args.save_path, (matte * 255).astype('uint8'))
+        cv2.imwrite(str(tgt_save_path), (matte * 255).astype('uint8'))
+
+        assert tgt_save_path.exists()
+
+        # get foreground
+        # matte = matte[:, :, None]
+        # foreground = img * matte + np.full(img.shape, 1) * (1 - matte)
+        # cv2.imwrite(args.save_path.replace('.png', '_fg.png'), foreground * 255)
+
+    pass
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--img_path', type=str, default='assets/test.jpg')
+    parser.add_argument('--save_path', type=str, default='test_matting.png')
+
+    parser.add_argument('--img_dir_path', type=str, default='assets', required=False)
+    args = parser.parse_args()
+
+    # matt_single(args)
+    matt_directory(args)
\ No newline at end of file
diff --git a/torch_utils/legacy.py b/torch_utils/legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f2f0ac5c42d0e36a4808f0235276f25ee5bcbf3
--- /dev/null
+++ b/torch_utils/legacy.py
@@ -0,0 +1,368 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+"""Converting legacy network pickle into the new format."""
+
+from pdb import set_trace as st
+import click
+import pickle
+import re
+import copy
+import numpy as np
+import torch
+import dnnlib
+from torch_utils import misc
+
+#----------------------------------------------------------------------------
+
+def load_network_pkl(f, device, force_fp16=False):
+    data = _LegacyUnpickler(f).load()
+
+    # Legacy TensorFlow pickle => convert.
+    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
+        tf_G, tf_D, tf_Gs = data
+        G = convert_tf_generator(tf_G)
+        D = convert_tf_discriminator(tf_D)
+        G_ema = convert_tf_generator(tf_Gs)
+        data = dict(G=G, D=D, G_ema=G_ema)
+    
+    # for k, module in data.items():
+    # for key in ['G', 'D', 'G_ema']:
+    #     data[key].to(device)
+
+    # Add missing fields.
+    if 'training_set_kwargs' not in data:
+        data['training_set_kwargs'] = None
+    if 'augment_pipe' not in data:
+        data['augment_pipe'] = None
+
+    # Validate contents.
+    assert isinstance(data['G'], torch.nn.Module)
+    assert isinstance(data['D'], torch.nn.Module)
+    assert isinstance(data['G_ema'], torch.nn.Module)
+    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
+    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
+
+    # Force FP16.
+    if force_fp16:
+        for key in ['G', 'D', 'G_ema']:
+            old = data[key]
+            kwargs = copy.deepcopy(old.init_kwargs)
+            fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
+            fp16_kwargs.num_fp16_res = 4
+            fp16_kwargs.conv_clamp = 256
+            if kwargs != old.init_kwargs:
+                new = type(old)(**kwargs).eval().requires_grad_(False)
+                misc.copy_params_and_buffers(old, new, require_all=True)
+                data[key] = new
+    return data
+
+def load_network_pkl_E(f, force_fp16=False):
+    data = _LegacyUnpickler(f).load()
+
+    # Legacy TensorFlow pickle => convert.
+    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
+        tf_E = data
+        E = convert_tf_generator(tf_E)
+        # D = convert_tf_discriminator(tf_D)
+        # G_ema = convert_tf_generator(tf_Gs)
+        data = dict(G=E)
+
+    # Add missing fields.
+    if 'training_set_kwargs' not in data:
+        data['training_set_kwargs'] = None
+    if 'augment_pipe' not in data:
+        data['augment_pipe'] = None
+
+    # Validate contents.
+    assert isinstance(data['E'], torch.nn.Module)
+    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
+    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
+
+    # Force FP16.
+    if force_fp16:
+        for key in ['E']:
+            old = data[key]
+            kwargs = copy.deepcopy(old.init_kwargs)
+            fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
+            fp16_kwargs.num_fp16_res = 4
+            fp16_kwargs.conv_clamp = 256
+            if kwargs != old.init_kwargs:
+                new = type(old)(**kwargs).eval().requires_grad_(False)
+                misc.copy_params_and_buffers(old, new, require_all=True)
+                data[key] = new
+    return data
+#----------------------------------------------------------------------------
+
+class _TFNetworkStub(dnnlib.EasyDict):
+    pass
+
+class _LegacyUnpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        if module == 'dnnlib.tflib.network' and name == 'Network':
+            return _TFNetworkStub
+        if 'training' in module:
+            module = module.replace('training', 'nsr') # map module position from eg3d repo
+
+        return super().find_class(module, name)
+
+#----------------------------------------------------------------------------
+
+def _collect_tf_params(tf_net):
+    # pylint: disable=protected-access
+    tf_params = dict()
+    def recurse(prefix, tf_net):
+        for name, value in tf_net.variables:
+            tf_params[prefix + name] = value
+        for name, comp in tf_net.components.items():
+            recurse(prefix + name + '/', comp)
+    recurse('', tf_net)
+    return tf_params
+
+#----------------------------------------------------------------------------
+
+def _populate_module_params(module, *patterns):
+    for name, tensor in misc.named_params_and_buffers(module):
+        found = False
+        value = None
+        for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
+            match = re.fullmatch(pattern, name)
+            if match:
+                found = True
+                if value_fn is not None:
+                    value = value_fn(*match.groups())
+                break
+        try:
+            assert found
+            if value is not None:
+                tensor.copy_(torch.from_numpy(np.array(value)))
+        except:
+            print(name, list(tensor.shape))
+            raise
+
+#----------------------------------------------------------------------------
+
+def convert_tf_generator(tf_G):
+    if tf_G.version < 4:
+        raise ValueError('TensorFlow pickle version too low')
+
+    # Collect kwargs.
+    tf_kwargs = tf_G.static_kwargs
+    known_kwargs = set()
+    def kwarg(tf_name, default=None, none=None):
+        known_kwargs.add(tf_name)
+        val = tf_kwargs.get(tf_name, default)
+        return val if val is not None else none
+
+    # Convert kwargs.
+    from training import networks_stylegan2
+    network_class = networks_stylegan2.Generator
+    kwargs = dnnlib.EasyDict(
+        z_dim               = kwarg('latent_size',          512),
+        c_dim               = kwarg('label_size',           0),
+        w_dim               = kwarg('dlatent_size',         512),
+        img_resolution      = kwarg('resolution',           1024),
+        img_channels        = kwarg('num_channels',         3),
+        channel_base        = kwarg('fmap_base',            16384) * 2,
+        channel_max         = kwarg('fmap_max',             512),
+        num_fp16_res        = kwarg('num_fp16_res',         0),
+        conv_clamp          = kwarg('conv_clamp',           None),
+        architecture        = kwarg('architecture',         'skip'),
+        resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
+        use_noise           = kwarg('use_noise',            True),
+        activation          = kwarg('nonlinearity',         'lrelu'),
+        mapping_kwargs      = dnnlib.EasyDict(
+            num_layers      = kwarg('mapping_layers',       8),
+            embed_features  = kwarg('label_fmaps',          None),
+            layer_features  = kwarg('mapping_fmaps',        None),
+            activation      = kwarg('mapping_nonlinearity', 'lrelu'),
+            lr_multiplier   = kwarg('mapping_lrmul',        0.01),
+            w_avg_beta      = kwarg('w_avg_beta',           0.995,  none=1),
+        ),
+    )
+
+    # Check for unknown kwargs.
+    kwarg('truncation_psi')
+    kwarg('truncation_cutoff')
+    kwarg('style_mixing_prob')
+    kwarg('structure')
+    kwarg('conditioning')
+    kwarg('fused_modconv')
+    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
+    if len(unknown_kwargs) > 0:
+        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
+
+    # Collect params.
+    tf_params = _collect_tf_params(tf_G)
+    for name, value in list(tf_params.items()):
+        match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
+        if match:
+            r = kwargs.img_resolution // (2 ** int(match.group(1)))
+            tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
+            kwargs.synthesis.kwargs.architecture = 'orig'
+    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
+
+    # Convert params.
+    G = network_class(**kwargs).eval().requires_grad_(False)
+    # pylint: disable=unnecessary-lambda
+    # pylint: disable=f-string-without-interpolation
+    _populate_module_params(G,
+        r'mapping\.w_avg',                                  lambda:     tf_params[f'dlatent_avg'],
+        r'mapping\.embed\.weight',                          lambda:     tf_params[f'mapping/LabelEmbed/weight'].transpose(),
+        r'mapping\.embed\.bias',                            lambda:     tf_params[f'mapping/LabelEmbed/bias'],
+        r'mapping\.fc(\d+)\.weight',                        lambda i:   tf_params[f'mapping/Dense{i}/weight'].transpose(),
+        r'mapping\.fc(\d+)\.bias',                          lambda i:   tf_params[f'mapping/Dense{i}/bias'],
+        r'synthesis\.b4\.const',                            lambda:     tf_params[f'synthesis/4x4/Const/const'][0],
+        r'synthesis\.b4\.conv1\.weight',                    lambda:     tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
+        r'synthesis\.b4\.conv1\.bias',                      lambda:     tf_params[f'synthesis/4x4/Conv/bias'],
+        r'synthesis\.b4\.conv1\.noise_const',               lambda:     tf_params[f'synthesis/noise0'][0, 0],
+        r'synthesis\.b4\.conv1\.noise_strength',            lambda:     tf_params[f'synthesis/4x4/Conv/noise_strength'],
+        r'synthesis\.b4\.conv1\.affine\.weight',            lambda:     tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
+        r'synthesis\.b4\.conv1\.affine\.bias',              lambda:     tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.conv0\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
+        r'synthesis\.b(\d+)\.conv0\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
+        r'synthesis\.b(\d+)\.conv0\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
+        r'synthesis\.b(\d+)\.conv0\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
+        r'synthesis\.b(\d+)\.conv0\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
+        r'synthesis\.b(\d+)\.conv0\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.conv1\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
+        r'synthesis\.b(\d+)\.conv1\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
+        r'synthesis\.b(\d+)\.conv1\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
+        r'synthesis\.b(\d+)\.conv1\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
+        r'synthesis\.b(\d+)\.conv1\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
+        r'synthesis\.b(\d+)\.conv1\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.torgb\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
+        r'synthesis\.b(\d+)\.torgb\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
+        r'synthesis\.b(\d+)\.torgb\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
+        r'synthesis\.b(\d+)\.torgb\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.skip\.weight',                 lambda r:   tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
+        r'.*\.resample_filter',                             None,
+        r'.*\.act_filter',                                  None,
+    )
+    return G
+
+#----------------------------------------------------------------------------
+
+def convert_tf_discriminator(tf_D):
+    if tf_D.version < 4:
+        raise ValueError('TensorFlow pickle version too low')
+
+    # Collect kwargs.
+    tf_kwargs = tf_D.static_kwargs
+    known_kwargs = set()
+    def kwarg(tf_name, default=None):
+        known_kwargs.add(tf_name)
+        return tf_kwargs.get(tf_name, default)
+
+    # Convert kwargs.
+    kwargs = dnnlib.EasyDict(
+        c_dim                   = kwarg('label_size',           0),
+        img_resolution          = kwarg('resolution',           1024),
+        img_channels            = kwarg('num_channels',         3),
+        architecture            = kwarg('architecture',         'resnet'),
+        channel_base            = kwarg('fmap_base',            16384) * 2,
+        channel_max             = kwarg('fmap_max',             512),
+        num_fp16_res            = kwarg('num_fp16_res',         0),
+        conv_clamp              = kwarg('conv_clamp',           None),
+        cmap_dim                = kwarg('mapping_fmaps',        None),
+        block_kwargs = dnnlib.EasyDict(
+            activation          = kwarg('nonlinearity',         'lrelu'),
+            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
+            freeze_layers       = kwarg('freeze_layers',        0),
+        ),
+        mapping_kwargs = dnnlib.EasyDict(
+            num_layers          = kwarg('mapping_layers',       0),
+            embed_features      = kwarg('mapping_fmaps',        None),
+            layer_features      = kwarg('mapping_fmaps',        None),
+            activation          = kwarg('nonlinearity',         'lrelu'),
+            lr_multiplier       = kwarg('mapping_lrmul',        0.1),
+        ),
+        epilogue_kwargs = dnnlib.EasyDict(
+            mbstd_group_size    = kwarg('mbstd_group_size',     None),
+            mbstd_num_channels  = kwarg('mbstd_num_features',   1),
+            activation          = kwarg('nonlinearity',         'lrelu'),
+        ),
+    )
+
+    # Check for unknown kwargs.
+    kwarg('structure')
+    kwarg('conditioning')
+    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
+    if len(unknown_kwargs) > 0:
+        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
+
+    # Collect params.
+    tf_params = _collect_tf_params(tf_D)
+    for name, value in list(tf_params.items()):
+        match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
+        if match:
+            r = kwargs.img_resolution // (2 ** int(match.group(1)))
+            tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
+            kwargs.architecture = 'orig'
+    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
+
+    # Convert params.
+    from training import networks_stylegan2
+    D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
+    # pylint: disable=unnecessary-lambda
+    # pylint: disable=f-string-without-interpolation
+    _populate_module_params(D,
+        r'b(\d+)\.fromrgb\.weight',     lambda r:       tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
+        r'b(\d+)\.fromrgb\.bias',       lambda r:       tf_params[f'{r}x{r}/FromRGB/bias'],
+        r'b(\d+)\.conv(\d+)\.weight',   lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
+        r'b(\d+)\.conv(\d+)\.bias',     lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
+        r'b(\d+)\.skip\.weight',        lambda r:       tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
+        r'mapping\.embed\.weight',      lambda:         tf_params[f'LabelEmbed/weight'].transpose(),
+        r'mapping\.embed\.bias',        lambda:         tf_params[f'LabelEmbed/bias'],
+        r'mapping\.fc(\d+)\.weight',    lambda i:       tf_params[f'Mapping{i}/weight'].transpose(),
+        r'mapping\.fc(\d+)\.bias',      lambda i:       tf_params[f'Mapping{i}/bias'],
+        r'b4\.conv\.weight',            lambda:         tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
+        r'b4\.conv\.bias',              lambda:         tf_params[f'4x4/Conv/bias'],
+        r'b4\.fc\.weight',              lambda:         tf_params[f'4x4/Dense0/weight'].transpose(),
+        r'b4\.fc\.bias',                lambda:         tf_params[f'4x4/Dense0/bias'],
+        r'b4\.out\.weight',             lambda:         tf_params[f'Output/weight'].transpose(),
+        r'b4\.out\.bias',               lambda:         tf_params[f'Output/bias'],
+        r'.*\.resample_filter',         None,
+    )
+    return D
+
+#----------------------------------------------------------------------------
+
+@click.command()
+@click.option('--source', help='Input pickle', required=True, metavar='PATH')
+@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
+@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
+def convert_network_pickle(source, dest, force_fp16):
+    """Convert legacy network pickle into the native PyTorch format.
+
+    The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
+    It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
+
+    Example:
+
+    \b
+    python legacy.py \\
+        --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
+        --dest=stylegan2-cat-config-f.pkl
+    """
+    print(f'Loading "{source}"...')
+    with dnnlib.util.open_url(source) as f:
+        data = load_network_pkl(f, force_fp16=force_fp16)
+    print(f'Saving "{dest}"...')
+    with open(dest, 'wb') as f:
+        pickle.dump(data, f)
+    print('Done.')
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    convert_network_pickle() # pylint: disable=no-value-for-parameter
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/misc.py b/torch_utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..98ecc786f81740321cd6917ea33f19a3deeb5b64
--- /dev/null
+++ b/torch_utils/misc.py
@@ -0,0 +1,344 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+import re
+import contextlib
+import numpy as np
+import torch
+import warnings
+import dnnlib
+
+from guided_diffusion import dist_util, logger
+
+#----------------------------------------------------------------------------
+# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
+# same constant is used multiple times.
+
+_constant_cache = dict()
+
+
+def constant(value, shape=None, dtype=None, device=None, memory_format=None):
+    value = np.asarray(value)
+    if shape is not None:
+        shape = tuple(shape)
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+    if device is None:
+        device = torch.device('cpu')
+    if memory_format is None:
+        memory_format = torch.contiguous_format
+
+    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device,
+           memory_format)
+    tensor = _constant_cache.get(key, None)
+    if tensor is None:
+        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
+        if shape is not None:
+            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
+        tensor = tensor.contiguous(memory_format=memory_format)
+        _constant_cache[key] = tensor
+    return tensor
+
+
+#----------------------------------------------------------------------------
+# Replace NaN/Inf with specified numerical values.
+
+try:
+    nan_to_num = torch.nan_to_num  # 1.8.0a0
+except AttributeError:
+
+    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None):  # pylint: disable=redefined-builtin
+        assert isinstance(input, torch.Tensor)
+        if posinf is None:
+            posinf = torch.finfo(input.dtype).max
+        if neginf is None:
+            neginf = torch.finfo(input.dtype).min
+        assert nan == 0
+        return torch.clamp(input.unsqueeze(0).nansum(0),
+                           min=neginf,
+                           max=posinf,
+                           out=out)
+
+
+#----------------------------------------------------------------------------
+# Symbolic assert.
+
+try:
+    symbolic_assert = torch._assert  # 1.8.0a0 # pylint: disable=protected-access
+except AttributeError:
+    symbolic_assert = torch.Assert  # 1.7.0
+
+#----------------------------------------------------------------------------
+# Context manager to temporarily suppress known warnings in torch.jit.trace().
+# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672
+
+
+@contextlib.contextmanager
+def suppress_tracer_warnings():
+    flt = ('ignore', None, torch.jit.TracerWarning, None, 0)
+    warnings.filters.insert(0, flt)
+    yield
+    warnings.filters.remove(flt)
+
+
+#----------------------------------------------------------------------------
+# Assert that the shape of a tensor matches the given list of integers.
+# None indicates that the size of a dimension is allowed to vary.
+# Performs symbolic assertion when used in torch.jit.trace().
+
+
+def assert_shape(tensor, ref_shape):
+    if tensor.ndim != len(ref_shape):
+        raise AssertionError(
+            f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}'
+        )
+    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
+        if ref_size is None:
+            pass
+        elif isinstance(ref_size, torch.Tensor):
+            with suppress_tracer_warnings(
+            ):  # as_tensor results are registered as constants
+                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size),
+                                f'Wrong size for dimension {idx}')
+        elif isinstance(size, torch.Tensor):
+            with suppress_tracer_warnings(
+            ):  # as_tensor results are registered as constants
+                symbolic_assert(
+                    torch.equal(size, torch.as_tensor(ref_size)),
+                    f'Wrong size for dimension {idx}: expected {ref_size}')
+        elif size != ref_size:
+            raise AssertionError(
+                f'Wrong size for dimension {idx}: got {size}, expected {ref_size}'
+            )
+
+
+#----------------------------------------------------------------------------
+# Function decorator that calls torch.autograd.profiler.record_function().
+
+
+def profiled_function(fn):
+    def decorator(*args, **kwargs):
+        with torch.autograd.profiler.record_function(fn.__name__):
+            return fn(*args, **kwargs)
+
+    decorator.__name__ = fn.__name__
+    return decorator
+
+
+#----------------------------------------------------------------------------
+# Sampler for torch.utils.data.DataLoader that loops over the dataset
+# indefinitely, shuffling items as it goes.
+
+
+class InfiniteSampler(torch.utils.data.Sampler):
+    def __init__(self,
+                 dataset,
+                 rank=0,
+                 num_replicas=1,
+                 shuffle=True,
+                 seed=0,
+                 window_size=0.5):
+        assert len(dataset) > 0
+        assert num_replicas > 0
+        assert 0 <= rank < num_replicas
+        assert 0 <= window_size <= 1
+        super().__init__(dataset)
+        self.dataset = dataset
+        self.rank = rank
+        self.num_replicas = num_replicas
+        self.shuffle = shuffle
+        self.seed = seed
+        self.window_size = window_size
+
+    def __iter__(self):
+        order = np.arange(len(self.dataset))
+        rnd = None
+        window = 0
+        if self.shuffle:
+            rnd = np.random.RandomState(self.seed)
+            rnd.shuffle(order)
+            window = int(np.rint(order.size * self.window_size))
+
+        idx = 0
+        while True:
+            i = idx % order.size
+            if idx % self.num_replicas == self.rank:
+                yield order[i]
+            if window >= 2:
+                j = (i - rnd.randint(window)) % order.size
+                order[i], order[j] = order[j], order[i]
+            idx += 1
+
+
+#----------------------------------------------------------------------------
+# Utilities for operating with torch.nn.Module parameters and buffers.
+
+
+def params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.parameters()) + list(module.buffers())
+
+
+def named_params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.named_parameters()) + list(module.named_buffers())
+
+
+def copy_params_and_buffers(src_module, dst_module, require_all=False, load_except=(), model_name=''):
+    assert isinstance(src_module, torch.nn.Module)
+    assert isinstance(dst_module, torch.nn.Module)
+    src_tensors = dict(named_params_and_buffers(src_module))
+    for name, tensor in named_params_and_buffers(dst_module):
+        assert (name in src_tensors) or (not require_all)
+        if name in src_tensors:
+            try:
+                if name in load_except:
+                    logger.log('ignore load_except module: ', name)
+                else:
+                    tensor.copy_(src_tensors[name].detach()).requires_grad_(
+                        tensor.requires_grad)
+            except:
+                print(name)
+
+#----------------------------------------------------------------------------
+# Context manager for easily enabling/disabling DistributedDataParallel
+# synchronization.
+
+
+@contextlib.contextmanager
+def ddp_sync(module, sync):
+    assert isinstance(module, torch.nn.Module)
+    if sync or not isinstance(module,
+                              torch.nn.parallel.DistributedDataParallel):
+        yield
+    else:
+        with module.no_sync():
+            yield
+
+
+#----------------------------------------------------------------------------
+# Check DistributedDataParallel consistency across processes.
+
+
+def check_ddp_consistency(module, ignore_regex=None):
+    assert isinstance(module, torch.nn.Module)
+    for name, tensor in named_params_and_buffers(module):
+        fullname = type(module).__name__ + '.' + name
+        # print(fullname)
+        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
+            continue
+        tensor = tensor.detach()
+        if tensor.is_floating_point():
+            tensor = nan_to_num(tensor)
+        other = tensor.clone()
+        torch.distributed.broadcast(tensor=other, src=0)
+        assert (tensor == other).all(), fullname
+
+
+#----------------------------------------------------------------------------
+# Print summary table of module hierarchy.
+
+
+def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
+    assert isinstance(module, torch.nn.Module)
+    assert not isinstance(module, torch.jit.ScriptModule)
+    assert isinstance(inputs, (tuple, list))
+
+    # Register hooks.
+    entries = []
+    nesting = [0]
+
+    def pre_hook(_mod, _inputs):
+        nesting[0] += 1
+
+    def post_hook(mod, _inputs, outputs):
+        nesting[0] -= 1
+        if nesting[0] <= max_nesting:
+            outputs = list(outputs) if isinstance(outputs,
+                                                  (tuple,
+                                                   list)) else [outputs]
+            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
+            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
+
+    hooks = [
+        mod.register_forward_pre_hook(pre_hook) for mod in module.modules()
+    ]
+    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
+
+    # Run module.
+    outputs = module(*inputs)
+    for hook in hooks:
+        hook.remove()
+
+    # Identify unique outputs, parameters, and buffers.
+    tensors_seen = set()
+    for e in entries:
+        e.unique_params = [
+            t for t in e.mod.parameters() if id(t) not in tensors_seen
+        ]
+        e.unique_buffers = [
+            t for t in e.mod.buffers() if id(t) not in tensors_seen
+        ]
+        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
+        tensors_seen |= {
+            id(t)
+            for t in e.unique_params + e.unique_buffers + e.unique_outputs
+        }
+
+    # Filter out redundant entries.
+    if skip_redundant:
+        entries = [
+            e for e in entries if len(e.unique_params) or len(e.unique_buffers)
+            or len(e.unique_outputs)
+        ]
+
+    # Construct table.
+    rows = [[
+        type(module).__name__, 'Parameters', 'Buffers', 'Output shape',
+        'Datatype'
+    ]]
+    rows += [['---'] * len(rows[0])]
+    param_total = 0
+    buffer_total = 0
+    submodule_names = {mod: name for name, mod in module.named_modules()}
+    for e in entries:
+        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
+        param_size = sum(t.numel() for t in e.unique_params)
+        buffer_size = sum(t.numel() for t in e.unique_buffers)
+        output_shapes = [str(list(t.shape)) for t in e.outputs]
+        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
+        rows += [[
+            name + (':0' if len(e.outputs) >= 2 else ''),
+            str(param_size) if param_size else '-',
+            str(buffer_size) if buffer_size else '-',
+            (output_shapes + ['-'])[0],
+            (output_dtypes + ['-'])[0],
+        ]]
+        for idx in range(1, len(e.outputs)):
+            rows += [[
+                name + f':{idx}', '-', '-', output_shapes[idx],
+                output_dtypes[idx]
+            ]]
+        param_total += param_size
+        buffer_total += buffer_size
+    rows += [['---'] * len(rows[0])]
+    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
+
+    # Print table.
+    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
+    print()
+    for row in rows:
+        print('  '.join(cell + ' ' * (width - len(cell))
+                        for cell, width in zip(row, widths)))
+    print()
+    return outputs
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/__init__.py b/torch_utils/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfebd04f47e6f6b1b44984c14c23b57d56f72240
--- /dev/null
+++ b/torch_utils/ops/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+# empty
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diff --git a/torch_utils/ops/bias_act.cpp b/torch_utils/ops/bias_act.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..ee6f6d0caaf4f84b94851d223e384344e1109cdc
--- /dev/null
+++ b/torch_utils/ops/bias_act.cpp
@@ -0,0 +1,103 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+
+static bool has_same_layout(torch::Tensor x, torch::Tensor y)
+{
+    if (x.dim() != y.dim())
+        return false;
+    for (int64_t i = 0; i < x.dim(); i++)
+    {
+        if (x.size(i) != y.size(i))
+            return false;
+        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
+            return false;
+    }
+    return true;
+}
+
+//------------------------------------------------------------------------
+
+static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
+{
+    // Validate arguments.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
+    TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
+    TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
+    TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
+    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
+    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
+    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
+    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
+    TORCH_CHECK(grad >= 0, "grad must be non-negative");
+
+    // Validate layout.
+    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
+    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
+    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
+    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
+    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
+
+    // Create output tensor.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+    torch::Tensor y = torch::empty_like(x);
+    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
+
+    // Initialize CUDA kernel parameters.
+    bias_act_kernel_params p;
+    p.x     = x.data_ptr();
+    p.b     = (b.numel()) ? b.data_ptr() : NULL;
+    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
+    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
+    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
+    p.y     = y.data_ptr();
+    p.grad  = grad;
+    p.act   = act;
+    p.alpha = alpha;
+    p.gain  = gain;
+    p.clamp = clamp;
+    p.sizeX = (int)x.numel();
+    p.sizeB = (int)b.numel();
+    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
+
+    // Choose CUDA kernel.
+    void* kernel;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
+    {
+        kernel = choose_bias_act_kernel<scalar_t>(p);
+    });
+    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
+
+    // Launch CUDA kernel.
+    p.loopX = 4;
+    int blockSize = 4 * 32;
+    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
+    void* args[] = {&p};
+    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
+    return y;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("bias_act", &bias_act);
+}
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/bias_act.cu b/torch_utils/ops/bias_act.cu
new file mode 100644
index 0000000000000000000000000000000000000000..71ca3900deda41e62d80044f0e409875f4c794b5
--- /dev/null
+++ b/torch_utils/ops/bias_act.cu
@@ -0,0 +1,177 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <c10/util/Half.h>
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>     { typedef double scalar_t; };
+template <> struct InternalType<float>      { typedef float  scalar_t; };
+template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
+
+//------------------------------------------------------------------------
+// CUDA kernel.
+
+template <class T, int A>
+__global__ void bias_act_kernel(bias_act_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    int G                 = p.grad;
+    scalar_t alpha        = (scalar_t)p.alpha;
+    scalar_t gain         = (scalar_t)p.gain;
+    scalar_t clamp        = (scalar_t)p.clamp;
+    scalar_t one          = (scalar_t)1;
+    scalar_t two          = (scalar_t)2;
+    scalar_t expRange     = (scalar_t)80;
+    scalar_t halfExpRange = (scalar_t)40;
+    scalar_t seluScale    = (scalar_t)1.0507009873554804934193349852946;
+    scalar_t seluAlpha    = (scalar_t)1.6732632423543772848170429916717;
+
+    // Loop over elements.
+    int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
+    for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
+    {
+        // Load.
+        scalar_t x = (scalar_t)((const T*)p.x)[xi];
+        scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
+        scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
+        scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
+        scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
+        scalar_t yy = (gain != 0) ? yref / gain : 0;
+        scalar_t y = 0;
+
+        // Apply bias.
+        ((G == 0) ? x : xref) += b;
+
+        // linear
+        if (A == 1)
+        {
+            if (G == 0) y = x;
+            if (G == 1) y = x;
+        }
+
+        // relu
+        if (A == 2)
+        {
+            if (G == 0) y = (x > 0) ? x : 0;
+            if (G == 1) y = (yy > 0) ? x : 0;
+        }
+
+        // lrelu
+        if (A == 3)
+        {
+            if (G == 0) y = (x > 0) ? x : x * alpha;
+            if (G == 1) y = (yy > 0) ? x : x * alpha;
+        }
+
+        // tanh
+        if (A == 4)
+        {
+            if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
+            if (G == 1) y = x * (one - yy * yy);
+            if (G == 2) y = x * (one - yy * yy) * (-two * yy);
+        }
+
+        // sigmoid
+        if (A == 5)
+        {
+            if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
+            if (G == 1) y = x * yy * (one - yy);
+            if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
+        }
+
+        // elu
+        if (A == 6)
+        {
+            if (G == 0) y = (x >= 0) ? x : exp(x) - one;
+            if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
+            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
+        }
+
+        // selu
+        if (A == 7)
+        {
+            if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
+            if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
+            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
+        }
+
+        // softplus
+        if (A == 8)
+        {
+            if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
+            if (G == 1) y = x * (one - exp(-yy));
+            if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
+        }
+
+        // swish
+        if (A == 9)
+        {
+            if (G == 0)
+                y = (x < -expRange) ? 0 : x / (exp(-x) + one);
+            else
+            {
+                scalar_t c = exp(xref);
+                scalar_t d = c + one;
+                if (G == 1)
+                    y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
+                else
+                    y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
+                yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
+            }
+        }
+
+        // Apply gain.
+        y *= gain * dy;
+
+        // Clamp.
+        if (clamp >= 0)
+        {
+            if (G == 0)
+                y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
+            else
+                y = (yref > -clamp & yref < clamp) ? y : 0;
+        }
+
+        // Store.
+        ((T*)p.y)[xi] = (T)y;
+    }
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
+{
+    if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
+    if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
+    if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
+    if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
+    if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
+    if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
+    if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
+    if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
+    if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
+    return NULL;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template void* choose_bias_act_kernel<double>       (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel<float>        (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel<c10::Half>    (const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/bias_act.h b/torch_utils/ops/bias_act.h
new file mode 100644
index 0000000000000000000000000000000000000000..8994bfb4e9cae790865348e08de5f685152d3344
--- /dev/null
+++ b/torch_utils/ops/bias_act.h
@@ -0,0 +1,42 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct bias_act_kernel_params
+{
+    const void* x;      // [sizeX]
+    const void* b;      // [sizeB] or NULL
+    const void* xref;   // [sizeX] or NULL
+    const void* yref;   // [sizeX] or NULL
+    const void* dy;     // [sizeX] or NULL
+    void*       y;      // [sizeX]
+
+    int         grad;
+    int         act;
+    float       alpha;
+    float       gain;
+    float       clamp;
+
+    int         sizeX;
+    int         sizeB;
+    int         stepB;
+    int         loopX;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..16a8ec0527471d0774873eba378665014a9b30b8
--- /dev/null
+++ b/torch_utils/ops/bias_act.py
@@ -0,0 +1,308 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Custom PyTorch ops for efficient bias and activation."""
+
+import os
+import numpy as np
+import torch
+import dnnlib
+
+from .. import custom_ops
+from .. import misc
+
+#----------------------------------------------------------------------------
+
+activation_funcs = {
+    'linear':
+    dnnlib.EasyDict(func=lambda x, **_: x,
+                    def_alpha=0,
+                    def_gain=1,
+                    cuda_idx=1,
+                    ref='',
+                    has_2nd_grad=False),
+    'relu':
+    dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x),
+                    def_alpha=0,
+                    def_gain=np.sqrt(2),
+                    cuda_idx=2,
+                    ref='y',
+                    has_2nd_grad=False),
+    'lrelu':
+    dnnlib.EasyDict(
+        func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha),
+        def_alpha=0.2,
+        def_gain=np.sqrt(2),
+        cuda_idx=3,
+        ref='y',
+        has_2nd_grad=False),
+    'tanh':
+    dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x),
+                    def_alpha=0,
+                    def_gain=1,
+                    cuda_idx=4,
+                    ref='y',
+                    has_2nd_grad=True),
+    'sigmoid':
+    dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x),
+                    def_alpha=0,
+                    def_gain=1,
+                    cuda_idx=5,
+                    ref='y',
+                    has_2nd_grad=True),
+    'elu':
+    dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x),
+                    def_alpha=0,
+                    def_gain=1,
+                    cuda_idx=6,
+                    ref='y',
+                    has_2nd_grad=True),
+    'selu':
+    dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x),
+                    def_alpha=0,
+                    def_gain=1,
+                    cuda_idx=7,
+                    ref='y',
+                    has_2nd_grad=True),
+    'softplus':
+    dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x),
+                    def_alpha=0,
+                    def_gain=1,
+                    cuda_idx=8,
+                    ref='y',
+                    has_2nd_grad=True),
+    'swish':
+    dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x,
+                    def_alpha=0,
+                    def_gain=np.sqrt(2),
+                    cuda_idx=9,
+                    ref='x',
+                    has_2nd_grad=True),
+}
+
+#----------------------------------------------------------------------------
+
+_plugin = None
+_null_tensor = torch.empty([0])
+
+
+def _init():
+    global _plugin
+    if _plugin is None:
+        _plugin = custom_ops.get_plugin(
+            module_name='bias_act_plugin',
+            sources=['bias_act.cpp', 'bias_act.cu'],
+            headers=['bias_act.h'],
+            source_dir=os.path.dirname(__file__),
+            extra_cuda_cflags=['--use_fast_math'],
+        )
+    return True
+
+
+#----------------------------------------------------------------------------
+
+
+# @torch.autocast(device_type='cuda')
+def bias_act(x,
+             b=None,
+             dim=1,
+             act='linear',
+             alpha=None,
+             gain=None,
+             clamp=None,
+             impl='cuda'):
+    r"""Fused bias and activation function.
+
+    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
+    and scales the result by `gain`. Each of the steps is optional. In most cases,
+    the fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports first and second order gradients,
+    but not third order gradients.
+
+    Args:
+        x:      Input activation tensor. Can be of any shape.
+        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
+                as `x`. The shape must be known, and it must match the dimension of `x`
+                corresponding to `dim`.
+        dim:    The dimension in `x` corresponding to the elements of `b`.
+                The value of `dim` is ignored if `b` is not specified.
+        act:    Name of the activation function to evaluate, or `"linear"` to disable.
+                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
+                See `activation_funcs` for a full list. `None` is not allowed.
+        alpha:  Shape parameter for the activation function, or `None` to use the default.
+        gain:   Scaling factor for the output tensor, or `None` to use default.
+                See `activation_funcs` for the default scaling of each activation function.
+                If unsure, consider specifying 1.
+        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
+                the clamping (default).
+        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+    Returns:
+        Tensor of the same shape and datatype as `x`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _bias_act_cuda(dim=dim,
+                              act=act,
+                              alpha=alpha,
+                              gain=gain,
+                              clamp=clamp).apply(x, b)
+    return _bias_act_ref(x=x,
+                         b=b,
+                         dim=dim,
+                         act=act,
+                         alpha=alpha,
+                         gain=gain,
+                         clamp=clamp)
+
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+def _bias_act_ref(x,
+                  b=None,
+                  dim=1,
+                  act='linear',
+                  alpha=None,
+                  gain=None,
+                  clamp=None):
+    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Add bias.
+    if b is not None:
+        assert isinstance(b, torch.Tensor) and b.ndim == 1
+        assert 0 <= dim < x.ndim
+        assert b.shape[0] == x.shape[dim]
+        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
+
+    # Evaluate activation function.
+    alpha = float(alpha)
+    x = spec.func(x, alpha=alpha)
+
+    # Scale by gain.
+    gain = float(gain)
+    if gain != 1:
+        x = x * gain
+
+    # Clamp.
+    if clamp >= 0:
+        x = x.clamp(-clamp, clamp)  # pylint: disable=invalid-unary-operand-type
+    return x
+
+
+#----------------------------------------------------------------------------
+
+_bias_act_cuda_cache = dict()
+
+
+# @torch.autocast(device_type='cuda')
+def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
+    """Fast CUDA implementation of `bias_act()` using custom ops.
+    """
+    # Parse arguments.
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Lookup from cache.
+    key = (dim, act, alpha, gain, clamp)
+    if key in _bias_act_cuda_cache:
+        return _bias_act_cuda_cache[key]
+
+    # Forward op.
+    class BiasActCuda(torch.autograd.Function):
+        @staticmethod
+        # @torch.cuda.amp.custom_fwd
+        def forward(ctx, x, b):  # pylint: disable=arguments-differ
+            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(
+                1) == 1 else torch.contiguous_format
+            x = x.contiguous(memory_format=ctx.memory_format)
+            b = b.contiguous() if b is not None else _null_tensor
+            y = x
+            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
+                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor,
+                                     _null_tensor, 0, dim, spec.cuda_idx,
+                                     alpha, gain, clamp)
+            ctx.save_for_backward(
+                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
+                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
+                y if 'y' in spec.ref else _null_tensor)
+            return y
+
+        @staticmethod
+        # @torch.cuda.amp.custom_bwd
+        def backward(ctx, dy):  # pylint: disable=arguments-differ
+            dy = dy.contiguous(memory_format=ctx.memory_format)
+            x, b, y = ctx.saved_tensors
+            dx = None
+            db = None
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
+                dx = dy
+                if act != 'linear' or gain != 1 or clamp >= 0:
+                    dx = BiasActCudaGrad.apply(dy, x, b, y)
+
+            if ctx.needs_input_grad[1]:
+                db = dx.sum([i for i in range(dx.ndim) if i != dim])
+
+            return dx, db
+
+    # Backward op.
+    class BiasActCudaGrad(torch.autograd.Function):
+        @staticmethod
+        # @torch.cuda.amp.custom_fwd
+        def forward(ctx, dy, x, b, y):  # pylint: disable=arguments-differ
+            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(
+                1) == 1 else torch.contiguous_format
+            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim,
+                                  spec.cuda_idx, alpha, gain, clamp)
+            ctx.save_for_backward(dy if spec.has_2nd_grad else _null_tensor, x,
+                                  b, y)
+            return dx
+
+        @staticmethod
+        # @torch.cuda.amp.custom_bwd
+        def backward(ctx, d_dx):  # pylint: disable=arguments-differ
+            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
+            dy, x, b, y = ctx.saved_tensors
+            d_dy = None
+            d_x = None
+            d_b = None
+            d_y = None
+
+            if ctx.needs_input_grad[0]:
+                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
+
+            if spec.has_2nd_grad and (ctx.needs_input_grad[1]
+                                      or ctx.needs_input_grad[2]):
+                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim,
+                                       spec.cuda_idx, alpha, gain, clamp)
+
+            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
+                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
+
+            return d_dy, d_x, d_b, d_y
+
+    # Add to cache.
+    _bias_act_cuda_cache[key] = BiasActCuda
+    return BiasActCuda
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/conv2d_gradfix.py b/torch_utils/ops/conv2d_gradfix.py
new file mode 100644
index 0000000000000000000000000000000000000000..c539b82a2a580ac301af741a65a745f9a72ef3d7
--- /dev/null
+++ b/torch_utils/ops/conv2d_gradfix.py
@@ -0,0 +1,302 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Custom replacement for `torch.nn.functional.conv2d` that supports
+arbitrarily high order gradients with zero performance penalty."""
+
+import contextlib
+import torch
+from pdb import set_trace as st
+import traceback
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False  # Enable the custom op by setting this to true.
+weight_gradients_disabled = False  # Forcefully disable computation of gradients with respect to the weights.
+
+
+@contextlib.contextmanager
+def no_weight_gradients(disable=True):
+    global weight_gradients_disabled
+    old = weight_gradients_disabled
+    if disable:
+        weight_gradients_disabled = True
+    yield
+    weight_gradients_disabled = old
+
+
+#----------------------------------------------------------------------------
+
+
+def conv2d(input,
+           weight,
+           bias=None,
+           stride=1,
+           padding=0,
+           dilation=1,
+           groups=1):
+    if _should_use_custom_op(input):
+        return _conv2d_gradfix(transpose=False,
+                               weight_shape=weight.shape,
+                               stride=stride,
+                               padding=padding,
+                               output_padding=0,
+                               dilation=dilation,
+                               groups=groups).apply(input, weight, bias)
+    return torch.nn.functional.conv2d(input=input,
+                                      weight=weight,
+                                      bias=bias,
+                                      stride=stride,
+                                      padding=padding,
+                                      dilation=dilation,
+                                      groups=groups)
+
+
+def conv_transpose2d(input,
+                     weight,
+                     bias=None,
+                     stride=1,
+                     padding=0,
+                     output_padding=0,
+                     groups=1,
+                     dilation=1):
+    if _should_use_custom_op(input):
+        return _conv2d_gradfix(transpose=True,
+                               weight_shape=weight.shape,
+                               stride=stride,
+                               padding=padding,
+                               output_padding=output_padding,
+                               groups=groups,
+                               dilation=dilation).apply(input, weight, bias)
+    return torch.nn.functional.conv_transpose2d(input=input,
+                                                weight=weight,
+                                                bias=bias,
+                                                stride=stride,
+                                                padding=padding,
+                                                output_padding=output_padding,
+                                                groups=groups,
+                                                dilation=dilation)
+
+
+#----------------------------------------------------------------------------
+
+
+def _should_use_custom_op(input):
+    assert isinstance(input, torch.Tensor)
+    if (not enabled) or (not torch.backends.cudnn.enabled):
+        return False
+    if input.device.type != 'cuda':
+        return False
+    return True
+
+
+def _tuple_of_ints(xs, ndim):
+    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs, ) * ndim
+    assert len(xs) == ndim
+    assert all(isinstance(x, int) for x in xs)
+    return xs
+
+
+#----------------------------------------------------------------------------
+
+_conv2d_gradfix_cache = dict()
+_null_tensor = torch.empty([0])
+
+
+def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding,
+                    dilation, groups):
+    # Parse arguments.
+    ndim = 2
+    weight_shape = tuple(weight_shape)
+    stride = _tuple_of_ints(stride, ndim)
+    padding = _tuple_of_ints(padding, ndim)
+    output_padding = _tuple_of_ints(output_padding, ndim)
+    dilation = _tuple_of_ints(dilation, ndim)
+
+    # Lookup from cache.
+    key = (transpose, weight_shape, stride, padding, output_padding, dilation,
+           groups)
+    if key in _conv2d_gradfix_cache:
+        return _conv2d_gradfix_cache[key]
+
+    # Validate arguments.
+    assert groups >= 1
+    assert len(weight_shape) == ndim + 2
+    assert all(stride[i] >= 1 for i in range(ndim))
+    assert all(padding[i] >= 0 for i in range(ndim))
+    assert all(dilation[i] >= 0 for i in range(ndim))
+    if not transpose:
+        assert all(output_padding[i] == 0 for i in range(ndim))
+    else:  # transpose
+        assert all(0 <= output_padding[i] < max(stride[i], dilation[i])
+                   for i in range(ndim))
+
+    # Helpers.
+    common_kwargs = dict(stride=stride,
+                         padding=padding,
+                         dilation=dilation,
+                         groups=groups)
+
+    def calc_output_padding(input_shape, output_shape):
+        if transpose:
+            return [0, 0]
+        return [
+            input_shape[i + 2] - (output_shape[i + 2] - 1) * stride[i] -
+            (1 - 2 * padding[i]) - dilation[i] * (weight_shape[i + 2] - 1)
+            for i in range(ndim)
+        ]
+
+    # Forward & backward.
+    class Conv2d(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, input, weight, bias):
+            assert weight.shape == weight_shape
+            ctx.save_for_backward(
+                input if weight.requires_grad else _null_tensor,
+                weight if input.requires_grad else _null_tensor,
+            )
+            ctx.input_shape = input.shape
+
+            # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
+            if weight_shape[2:] == stride == dilation == (
+                    1, 1) and padding == (
+                        0, 0) and torch.cuda.get_device_capability(
+                            input.device) < (8, 0):
+                a = weight.reshape(groups, weight_shape[0] // groups,
+                                   weight_shape[1])
+                b = input.reshape(input.shape[0], groups,
+                                  input.shape[1] // groups, -1)
+                c = (a.transpose(1, 2) if transpose else a) @ b.permute(
+                    1, 2, 0, 3).flatten(2)
+                c = c.reshape(-1, input.shape[0],
+                              *input.shape[2:]).transpose(0, 1)
+                c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(
+                    2).unsqueeze(3)
+                return c.contiguous(
+                    memory_format=(torch.channels_last if input.stride(1) ==
+                                   1 else torch.contiguous_format))
+
+            # General case => cuDNN.
+            if transpose:
+                return torch.nn.functional.conv_transpose2d(
+                    input=input,
+                    weight=weight,
+                    bias=bias,
+                    output_padding=output_padding,
+                    **common_kwargs)
+            return torch.nn.functional.conv2d(input=input,
+                                              weight=weight,
+                                              bias=bias,
+                                              **common_kwargs)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            input, weight = ctx.saved_tensors
+            input_shape = ctx.input_shape
+            grad_input = None
+            grad_weight = None
+            grad_bias = None
+
+            if ctx.needs_input_grad[0]:
+                p = calc_output_padding(input_shape=input_shape,
+                                        output_shape=grad_output.shape)
+                op = _conv2d_gradfix(transpose=(not transpose),
+                                     weight_shape=weight_shape,
+                                     output_padding=p,
+                                     **common_kwargs)
+                grad_input = op.apply(grad_output, weight, None)
+                assert grad_input.shape == input_shape
+
+            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
+                grad_weight = Conv2dGradWeight.apply(grad_output, input,
+                                                     weight)
+                assert grad_weight.shape == weight_shape
+
+            if ctx.needs_input_grad[2]:
+                grad_bias = grad_output.sum([0, 2, 3])
+
+            return grad_input, grad_weight, grad_bias
+
+    # Gradient with respect to the weights.
+    class Conv2dGradWeight(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, grad_output, input, weight):
+            ctx.save_for_backward(
+                grad_output if input.requires_grad else _null_tensor,
+                input if grad_output.requires_grad else _null_tensor,
+            )
+            ctx.grad_output_shape = grad_output.shape
+            ctx.input_shape = input.shape
+
+            # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
+            if weight_shape[2:] == stride == dilation == (
+                    1, 1) and padding == (0, 0):
+                a = grad_output.reshape(grad_output.shape[0], groups,
+                                        grad_output.shape[1] // groups,
+                                        -1).permute(1, 2, 0, 3).flatten(2)
+                b = input.reshape(input.shape[0], groups,
+                                  input.shape[1] // groups,
+                                  -1).permute(1, 2, 0, 3).flatten(2)
+                c = (b @ a.transpose(1, 2) if transpose else
+                     a @ b.transpose(1, 2)).reshape(weight_shape)
+                return c.contiguous(
+                    memory_format=(torch.channels_last if input.stride(1) ==
+                                   1 else torch.contiguous_format))
+
+            # General case => cuDNN.
+            # print(input.device, weight.device, flush=True)
+            # for line in traceback.format_stack():
+            #     print(line.strip(), flush=True)
+            return torch.ops.aten.convolution_backward(
+                grad_output=grad_output,
+                input=input,
+                weight=weight,
+                bias_sizes=None,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                transposed=transpose,
+                output_padding=output_padding,
+                groups=groups,
+                output_mask=[False, True, False])[1]
+
+        @staticmethod
+        def backward(ctx, grad2_grad_weight):
+            grad_output, input = ctx.saved_tensors
+            grad_output_shape = ctx.grad_output_shape
+            input_shape = ctx.input_shape
+            grad2_grad_output = None
+            grad2_input = None
+
+            if ctx.needs_input_grad[0]:
+                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight,
+                                                 None)
+                assert grad2_grad_output.shape == grad_output_shape
+
+            if ctx.needs_input_grad[1]:
+                p = calc_output_padding(input_shape=input_shape,
+                                        output_shape=grad_output_shape)
+                op = _conv2d_gradfix(transpose=(not transpose),
+                                     weight_shape=weight_shape,
+                                     output_padding=p,
+                                     **common_kwargs)
+                grad2_input = op.apply(grad_output, grad2_grad_weight, None)
+                assert grad2_input.shape == input_shape
+
+            return grad2_grad_output, grad2_input
+
+    _conv2d_gradfix_cache[key] = Conv2d
+    return Conv2d
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/conv2d_resample.py b/torch_utils/ops/conv2d_resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..df7bea8fd7d0b921227cec546bebbd0e836d9da8
--- /dev/null
+++ b/torch_utils/ops/conv2d_resample.py
@@ -0,0 +1,208 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""2D convolution with optional up/downsampling."""
+
+import torch
+
+from .. import misc
+from . import conv2d_gradfix
+from . import upfirdn2d
+from .upfirdn2d import _parse_padding
+from .upfirdn2d import _get_filter_size
+
+#----------------------------------------------------------------------------
+
+
+def _get_weight_shape(w):
+    with misc.suppress_tracer_warnings(
+    ):  # this value will be treated as a constant
+        shape = [int(sz) for sz in w.shape]
+    misc.assert_shape(w, shape)
+    return shape
+
+
+#----------------------------------------------------------------------------
+
+
+def _conv2d_wrapper(x,
+                    w,
+                    stride=1,
+                    padding=0,
+                    groups=1,
+                    transpose=False,
+                    flip_weight=True):
+    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
+    """
+    _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w)
+
+    # Flip weight if requested.
+    # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
+    if not flip_weight and (kw > 1 or kh > 1):
+        w = w.flip([2, 3])
+
+    # Execute using conv2d_gradfix.
+    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
+    return op(x, w, stride=stride, padding=padding, groups=groups)
+
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+def conv2d_resample(x,
+                    w,
+                    f=None,
+                    up=1,
+                    down=1,
+                    padding=0,
+                    groups=1,
+                    flip_weight=True,
+                    flip_filter=False):
+    r"""2D convolution with optional up/downsampling.
+
+    Padding is performed only once at the beginning, not between the operations.
+
+    Args:
+        x:              Input tensor of shape
+                        `[batch_size, in_channels, in_height, in_width]`.
+        w:              Weight tensor of shape
+                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
+        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
+                        calling upfirdn2d.setup_filter(). None = identity (default).
+        up:             Integer upsampling factor (default: 1).
+        down:           Integer downsampling factor (default: 1).
+        padding:        Padding with respect to the upsampled image. Can be a single number
+                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                        (default: 0).
+        groups:         Split input channels into N groups (default: 1).
+        flip_weight:    False = convolution, True = correlation (default: True).
+        flip_filter:    False = convolution, True = correlation (default: False).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
+    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype
+                                                              == x.dtype)
+    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+                         and f.dtype == torch.float32)
+    assert isinstance(up, int) and (up >= 1)
+    assert isinstance(down, int) and (down >= 1)
+    assert isinstance(groups, int) and (groups >= 1)
+    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
+    fw, fh = _get_filter_size(f)
+    px0, px1, py0, py1 = _parse_padding(padding)
+
+    # Adjust padding to account for up/downsampling.
+    if up > 1:
+        px0 += (fw + up - 1) // 2
+        px1 += (fw - up) // 2
+        py0 += (fh + up - 1) // 2
+        py1 += (fh - up) // 2
+    if down > 1:
+        px0 += (fw - down + 1) // 2
+        px1 += (fw - down) // 2
+        py0 += (fh - down + 1) // 2
+        py1 += (fh - down) // 2
+
+    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
+    if kw == 1 and kh == 1 and (down > 1 and up == 1):
+        x = upfirdn2d.upfirdn2d(x=x,
+                                f=f,
+                                down=down,
+                                padding=[px0, px1, py0, py1],
+                                flip_filter=flip_filter)
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        return x
+
+    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
+    if kw == 1 and kh == 1 and (up > 1 and down == 1):
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        x = upfirdn2d.upfirdn2d(x=x,
+                                f=f,
+                                up=up,
+                                padding=[px0, px1, py0, py1],
+                                gain=up**2,
+                                flip_filter=flip_filter)
+        return x
+
+    # Fast path: downsampling only => use strided convolution.
+    if down > 1 and up == 1:
+        x = upfirdn2d.upfirdn2d(x=x,
+                                f=f,
+                                padding=[px0, px1, py0, py1],
+                                flip_filter=flip_filter)
+        x = _conv2d_wrapper(x=x,
+                            w=w,
+                            stride=down,
+                            groups=groups,
+                            flip_weight=flip_weight)
+        return x
+
+    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
+    if up > 1:
+        if groups == 1:
+            w = w.transpose(0, 1)
+        else:
+            w = w.reshape(groups, out_channels // groups,
+                          in_channels_per_group, kh, kw)
+            w = w.transpose(1, 2)
+            w = w.reshape(groups * in_channels_per_group,
+                          out_channels // groups, kh, kw)
+        px0 -= kw - 1
+        px1 -= kw - up
+        py0 -= kh - 1
+        py1 -= kh - up
+        pxt = max(min(-px0, -px1), 0)
+        pyt = max(min(-py0, -py1), 0)
+        x = _conv2d_wrapper(x=x,
+                            w=w,
+                            stride=up,
+                            padding=[pyt, pxt],
+                            groups=groups,
+                            transpose=True,
+                            flip_weight=(not flip_weight))
+        x = upfirdn2d.upfirdn2d(
+            x=x,
+            f=f,
+            padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt],
+            gain=up**2,
+            flip_filter=flip_filter)
+        if down > 1:
+            x = upfirdn2d.upfirdn2d(x=x,
+                                    f=f,
+                                    down=down,
+                                    flip_filter=flip_filter)
+        return x
+
+    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
+    if up == 1 and down == 1:
+        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
+            return _conv2d_wrapper(x=x,
+                                   w=w,
+                                   padding=[py0, px0],
+                                   groups=groups,
+                                   flip_weight=flip_weight)
+
+    # Fallback: Generic reference implementation.
+    x = upfirdn2d.upfirdn2d(x=x,
+                            f=(f if up > 1 else None),
+                            up=up,
+                            padding=[px0, px1, py0, py1],
+                            gain=up**2,
+                            flip_filter=flip_filter)
+    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+    if down > 1:
+        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+    return x
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/filtered_lrelu.cpp b/torch_utils/ops/filtered_lrelu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..4f55466235a020b0f5e150350bfdcd8b2a1e579d
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu.cpp
@@ -0,0 +1,304 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "filtered_lrelu.h"
+
+//------------------------------------------------------------------------
+
+static std::tuple<torch::Tensor, torch::Tensor, int> filtered_lrelu(
+    torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
+    int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
+{
+    // Set CUDA device.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+
+    // Validate arguments.
+    TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
+    TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
+    TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
+    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
+    TORCH_CHECK(x.numel() > 0, "x is empty");
+    TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
+    TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
+    TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
+    TORCH_CHECK(fu.numel() > 0, "fu is empty");
+    TORCH_CHECK(fd.numel() > 0, "fd is empty");
+    TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
+    TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
+
+    // Figure out how much shared memory is available on the device.
+    int maxSharedBytes = 0;
+    AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
+    int sharedKB = maxSharedBytes >> 10;
+
+    // Populate enough launch parameters to check if a CUDA kernel exists.
+    filtered_lrelu_kernel_params p;
+    p.up      = up;
+    p.down    = down;
+    p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
+    p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
+    filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel<float, int32_t, false, false>(p, sharedKB);
+    if (!test_spec.exec)
+    {
+        // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
+        return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
+    }
+
+    // Input/output element size.
+    int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
+
+    // Input sizes.
+    int64_t xw = (int)x.size(3);
+    int64_t xh = (int)x.size(2);
+    int64_t fut_w = (int)fu.size(-1) - 1;
+    int64_t fut_h = (int)fu.size(0)  - 1;
+    int64_t fdt_w = (int)fd.size(-1) - 1;
+    int64_t fdt_h = (int)fd.size(0)  - 1;
+
+    // Logical size of upsampled buffer.
+    int64_t cw = xw * up + (px0 + px1) - fut_w;
+    int64_t ch = xh * up + (py0 + py1) - fut_h;
+    TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
+    TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
+
+    // Compute output size and allocate.
+    int64_t yw = (cw - fdt_w + (down - 1)) / down;
+    int64_t yh = (ch - fdt_h + (down - 1)) / down;
+    TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
+    TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
+    torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
+
+    // Allocate sign tensor.
+    torch::Tensor so;
+    torch::Tensor s = si;
+    bool readSigns = !!s.numel();
+    int64_t sw_active = 0; // Active width of sign tensor.
+    if (writeSigns)
+    {
+        sw_active = yw * down - (down - 1) + fdt_w;     // Active width in elements.
+        int64_t sh = yh * down - (down - 1) + fdt_h;    // Height = active height.
+        int64_t sw = (sw_active + 15) & ~15;            // Width  = active width in elements, rounded up to multiple of 16.
+        TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
+        s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
+    }
+    else if (readSigns)
+        sw_active = s.size(3) << 2;
+
+    // Validate sign tensor if in use.
+    if (readSigns || writeSigns)
+    {
+        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
+        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
+        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
+        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
+        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
+        TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
+    }
+
+    // Populate rest of CUDA kernel parameters.
+    p.x         = x.data_ptr();
+    p.y         = y.data_ptr();
+    p.b         = b.data_ptr();
+    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
+    p.fu        = fu.data_ptr<float>();
+    p.fd        = fd.data_ptr<float>();
+    p.pad0      = make_int2(px0, py0);
+    p.gain      = gain;
+    p.slope     = slope;
+    p.clamp     = clamp;
+    p.flip      = (flip_filters) ? 1 : 0;
+    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.yShape    = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
+    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
+    p.sOfs      = make_int2(sx, sy);
+    p.swLimit   = (sw_active + 3) >> 2; // Rounded up to bytes.
+
+    // x, y, b strides are in bytes.
+    p.xStride   = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
+    p.yStride   = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
+    p.bStride   = sz * b.stride(0);
+
+    // fu, fd strides are in elements.
+    p.fuStride  = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
+    p.fdStride  = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
+
+    // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
+    bool index64b = false;
+    if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
+    if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
+    if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) >  INT_MAX) index64b = true;
+    if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
+    if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) >  INT_MAX) index64b = true;
+    if (s.numel() > INT_MAX) index64b = true;
+
+    // Choose CUDA kernel.
+    filtered_lrelu_kernel_spec spec = { 0 };
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
+    {
+        if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
+        {
+            // Choose kernel based on index type, datatype and sign read/write modes.
+            if      (!index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, true,  false>(p, sharedKB);
+            else if (!index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, true >(p, sharedKB);
+            else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, false>(p, sharedKB);
+            else if ( index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, true,  false>(p, sharedKB);
+            else if ( index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, true >(p, sharedKB);
+            else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, false>(p, sharedKB);
+        }
+    });
+    TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    int bx = spec.numWarps * 32;
+    int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
+    int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
+    int gz = p.yShape.z * p.yShape.w;
+
+    // Repeat multiple horizontal tiles in a CTA?
+    if (spec.xrep)
+    {
+        p.tilesXrep = spec.xrep;
+        p.tilesXdim = gx;
+
+        gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
+        std::swap(gx, gy);
+    }
+    else
+    {
+        p.tilesXrep = 0;
+        p.tilesXdim = 0;
+    }
+
+    // Launch filter setup kernel.
+    AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
+
+    // Copy kernels to constant memory.
+    if      ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<true,  false>(at::cuda::getCurrentCUDAStream())));
+    else if (!writeSigns &&  readSigns) AT_CUDA_CHECK((copy_filters<false, true >(at::cuda::getCurrentCUDAStream())));
+    else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<false, false>(at::cuda::getCurrentCUDAStream())));
+
+    // Set cache and shared memory configurations for main kernel.
+    AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
+    if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
+        AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
+    AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
+
+    // Launch main kernel.
+    const int maxSubGz = 65535; // CUDA maximum for block z dimension.
+    for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
+    {
+        p.blockZofs = zofs;
+        int subGz = std::min(maxSubGz, gz - zofs);
+        AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
+    }
+
+    // Done.
+    return std::make_tuple(y, so, 0);
+}
+
+//------------------------------------------------------------------------
+
+static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
+{
+    // Set CUDA device.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+
+    // Validate arguments.
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
+    TORCH_CHECK(x.numel() > 0, "x is empty");
+    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
+
+    // Output signs if we don't have sign input.
+    torch::Tensor so;
+    torch::Tensor s = si;
+    bool readSigns = !!s.numel();
+    if (writeSigns)
+    {
+        int64_t sw = x.size(3);
+        sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
+        s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
+    }
+
+    // Validate sign tensor if in use.
+    if (readSigns || writeSigns)
+    {
+        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
+        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
+        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
+        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
+        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
+        TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
+    }
+
+    // Initialize CUDA kernel parameters.
+    filtered_lrelu_act_kernel_params p;
+    p.x         = x.data_ptr();
+    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
+    p.gain      = gain;
+    p.slope     = slope;
+    p.clamp     = clamp;
+    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.xStride   = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
+    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
+    p.sOfs      = make_int2(sx, sy);
+
+    // Choose CUDA kernel.
+    void* func = 0;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
+    {
+        if (writeSigns)
+            func = choose_filtered_lrelu_act_kernel<scalar_t, true, false>();
+        else if (readSigns)
+            func = choose_filtered_lrelu_act_kernel<scalar_t, false, true>();
+        else
+            func = choose_filtered_lrelu_act_kernel<scalar_t, false, false>();
+    });
+    TORCH_CHECK(func, "internal error - CUDA kernel not found");
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    int bx = 128; // 4 warps per block.
+
+    // Logical size of launch = writeSigns ? p.s : p.x
+    uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
+    uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
+    uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
+    gx = (gx - 1) / bx + 1;
+
+    // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
+    const uint32_t gmax = 65535;
+    gy = std::min(gy, gmax);
+    gz = std::min(gz, gmax);
+
+    // Launch.
+    AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
+    return so;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("filtered_lrelu",      &filtered_lrelu);      // The whole thing.
+    m.def("filtered_lrelu_act_", &filtered_lrelu_act);  // Activation and sign tensor handling only. Modifies data tensor in-place.
+}
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/filtered_lrelu.cu b/torch_utils/ops/filtered_lrelu.cu
new file mode 100644
index 0000000000000000000000000000000000000000..aaac95408365f023ffaa4cb89348d499d3b948f0
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu.cu
@@ -0,0 +1,1288 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <c10/util/Half.h>
+#include "filtered_lrelu.h"
+#include <cstdint>
+
+//------------------------------------------------------------------------
+// Helpers.
+
+enum // Filter modes.
+{
+    MODE_SUSD = 0,  // Separable upsampling, separable downsampling.
+    MODE_FUSD = 1,  // Full upsampling, separable downsampling.
+    MODE_SUFD = 2,  // Separable upsampling, full downsampling.
+    MODE_FUFD = 3,  // Full upsampling, full downsampling.
+};
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>
+{
+    typedef double scalar_t; typedef double2 vec2_t; typedef double4 vec4_t;
+    __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_double2(0, 0); }
+    __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_double4(0, 0, 0, 0); }
+    __device__ __forceinline__ static double clamp(double x, double c) { return fmin(fmax(x, -c), c); }
+};
+template <> struct InternalType<float>
+{
+    typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t;
+    __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); }
+    __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); }
+    __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); }
+};
+template <> struct InternalType<c10::Half>
+{
+    typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t;
+    __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); }
+    __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); }
+    __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); }
+};
+
+#define MIN(A, B)       ((A) < (B) ? (A) : (B))
+#define MAX(A, B)       ((A) > (B) ? (A) : (B))
+#define CEIL_DIV(A, B) (((B)==1) ? (A) : \
+                        ((B)==2) ? ((int)((A)+1) >> 1) : \
+                        ((B)==4) ? ((int)((A)+3) >> 2) : \
+                        (((A) + ((A) > 0 ? (B) - 1 : 0)) / (B)))
+
+// This works only up to blocks of size 256 x 256 and for all N that are powers of two.
+template <int N> __device__ __forceinline__ void fast_div_mod(int& x, int& y, unsigned int i)
+{
+    if ((N & (N-1)) && N <= 256)
+        y = (i * ((1<<24)/N + 1)) >> 24; // Assumes N <= 256, i < N*256.
+    else
+        y = i/N;
+
+    x = i - y*N;
+}
+
+// Type cast stride before reading it.
+template <class T> __device__ __forceinline__ T get_stride(const int64_t& x)
+{
+    return *reinterpret_cast<const T*>(&x);
+}
+
+//------------------------------------------------------------------------
+// Filters, setup kernel, copying function.
+
+#define MAX_FILTER_SIZE 32
+
+// Combined up/down filter buffers so that transfer can be done with one copy.
+__device__              float g_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in global memory, written by setup kernel.
+__device__ __constant__ float c_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in constant memory, read by main kernel.
+
+// Accessors to combined buffers to index up/down filters individually.
+#define c_fu (c_fbuf)
+#define c_fd (c_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE)
+#define g_fu (g_fbuf)
+#define g_fd (g_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE)
+
+// Set up filters into global memory buffer.
+static __global__ void setup_filters_kernel(filtered_lrelu_kernel_params p)
+{
+    for (int idx = threadIdx.x; idx < MAX_FILTER_SIZE * MAX_FILTER_SIZE; idx += blockDim.x)
+    {
+        int x, y;
+        fast_div_mod<MAX_FILTER_SIZE>(x, y, idx);
+
+        int fu_x = p.flip ? x : (p.fuShape.x - 1 - x);
+        int fu_y = p.flip ? y : (p.fuShape.y - 1 - y);
+        if (p.fuShape.y > 0)
+            g_fu[idx] = (x >= p.fuShape.x || y >= p.fuShape.y) ? 0.0f : p.fu[fu_x * p.fuStride.x + fu_y * p.fuStride.y];
+        else
+            g_fu[idx] = (x >= p.fuShape.x || y > 0) ? 0.0f : p.fu[fu_x * p.fuStride.x];
+
+        int fd_x = p.flip ? x : (p.fdShape.x - 1 - x);
+        int fd_y = p.flip ? y : (p.fdShape.y - 1 - y);
+        if (p.fdShape.y > 0)
+            g_fd[idx] = (x >= p.fdShape.x || y >= p.fdShape.y) ? 0.0f : p.fd[fd_x * p.fdStride.x + fd_y * p.fdStride.y];
+        else
+            g_fd[idx] = (x >= p.fdShape.x || y > 0) ? 0.0f : p.fd[fd_x * p.fdStride.x];
+    }
+}
+
+// Host function to copy filters written by setup kernel into constant buffer for main kernel.
+template <bool, bool> static cudaError_t copy_filters(cudaStream_t stream)
+{
+    void* src = 0;
+    cudaError_t err = cudaGetSymbolAddress(&src, g_fbuf);
+    if (err) return err;
+    return cudaMemcpyToSymbolAsync(c_fbuf, src, 2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream);
+}
+
+//------------------------------------------------------------------------
+// Coordinate spaces:
+// - Relative to input tensor:      inX, inY, tileInX, tileInY
+// - Relative to input tile:        relInX, relInY, tileInW, tileInH
+// - Relative to upsampled tile:    relUpX, relUpY, tileUpW, tileUpH
+// - Relative to output tile:       relOutX, relOutY, tileOutW, tileOutH
+// - Relative to output tensor:     outX, outY, tileOutX, tileOutY
+//
+// Relationships between coordinate spaces:
+// - inX = tileInX + relInX
+// - inY = tileInY + relInY
+// - relUpX = relInX * up + phaseInX
+// - relUpY = relInY * up + phaseInY
+// - relUpX = relOutX * down
+// - relUpY = relOutY * down
+// - outX = tileOutX + relOutX
+// - outY = tileOutY + relOutY
+
+extern __shared__ char s_buf_raw[]; // When sharedKB <= 48, allocate shared memory statically inside the kernel, otherwise use the externally allocated shared memory buffer.
+
+template <class T, class index_t, int sharedKB, bool signWrite, bool signRead, int filterMode, int up, int fuSize, int down, int fdSize, int tileOutW, int tileOutH, int threadsPerBlock, bool enableXrep, bool enableWriteSkip>
+static __global__ void filtered_lrelu_kernel(filtered_lrelu_kernel_params p)
+{
+    // Check that we don't try to support non-existing filter modes.
+    static_assert(up   == 1 || up   == 2 || up   == 4, "only up=1, up=2, up=4 scales supported");
+    static_assert(down == 1 || down == 2 || down == 4, "only down=1, down=2, down=4 scales supported");
+    static_assert(fuSize >= up,   "upsampling filter size must be at least upsampling factor");
+    static_assert(fdSize >= down, "downsampling filter size must be at least downsampling factor");
+    static_assert(fuSize % up   == 0, "upsampling filter size must be divisible with upsampling factor");
+    static_assert(fdSize % down == 0, "downsampling filter size must be divisible with downsampling factor");
+    static_assert(fuSize <= MAX_FILTER_SIZE && fdSize <= MAX_FILTER_SIZE, "filter size greater than MAX_FILTER_SIZE");
+    static_assert(up   != 1 || (fuSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "up=1 supported only for 1x1 full filters");
+    static_assert(down != 1 || (fdSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "down=1 supported only for 1x1 full filters");
+    static_assert(!(up   == 4 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "full filters not supported for up=4");
+    static_assert(!(down == 4 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "full filters not supported for down=4");
+
+    // Static definitions.
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    typedef typename InternalType<T>::vec2_t vec2_t;
+    typedef typename InternalType<T>::vec4_t vec4_t;
+    const int tileUpW    = (tileOutW * down + (fdSize - 1) - (down - 1) + 3) & ~3;  // Upsampled tile width, rounded up to multiple of 4.
+    const int tileUpH    = tileOutH * down + (fdSize - 1) - (down - 1);             // Upsampled tile height.
+    const int tileInW    = CEIL_DIV(tileUpW  + (fuSize - 1), up);                   // Input tile width.
+    const int tileInH    = CEIL_DIV(tileUpH  + (fuSize - 1), up);                   // Input tile height.
+    const int tileUpH_up = CEIL_DIV(tileUpH, up) * up;                              // Upsampled tile height rounded up to a multiple of up.
+    const int tileInH_up = CEIL_DIV(tileUpH_up + (fuSize - 1), up);                 // For allocations only, to avoid shared memory read overruns with up=2 and up=4.
+
+    // Merge 1x1 downsampling into last upsampling step for upf1 and ups2.
+    const bool downInline = (down == 1) && ((up == 1 && filterMode == MODE_FUFD) || (up == 2 && filterMode == MODE_SUFD));
+
+    // Sizes of logical buffers.
+    const int szIn    = tileInH_up * tileInW;
+    const int szUpX   = tileInH_up * tileUpW;
+    const int szUpXY  = downInline ? 0 : (tileUpH * tileUpW);
+    const int szDownX = tileUpH * tileOutW;
+
+    // Sizes for shared memory arrays.
+    const int s_buf0_size_base =
+        (filterMode == MODE_SUSD) ? MAX(szIn, szUpXY) :
+        (filterMode == MODE_FUSD) ? MAX(szIn, szDownX) :
+        (filterMode == MODE_SUFD) ? MAX(szIn, szUpXY) :
+        (filterMode == MODE_FUFD) ? szIn :
+        -1;
+    const int s_buf1_size_base =
+        (filterMode == MODE_SUSD) ? MAX(szUpX, szDownX) :
+        (filterMode == MODE_FUSD) ? szUpXY :
+        (filterMode == MODE_SUFD) ? szUpX  :
+        (filterMode == MODE_FUFD) ? szUpXY :
+        -1;
+
+    // Ensure U128 alignment.
+    const int s_buf0_size = (s_buf0_size_base + 3) & ~3;
+    const int s_buf1_size = (s_buf1_size_base + 3) & ~3;
+
+    // Check at compile time that we don't use too much shared memory.
+    static_assert((s_buf0_size + s_buf1_size) * sizeof(scalar_t) <= (sharedKB << 10), "shared memory overflow");
+
+    // Declare shared memory arrays.
+    scalar_t* s_buf0;
+    scalar_t* s_buf1;
+    if (sharedKB <= 48)
+    {
+        // Allocate shared memory arrays here.
+        __shared__ scalar_t s_buf0_st[(sharedKB > 48) ? (1<<24) : (s_buf0_size + s_buf1_size)]; // Prevent launching if this isn't optimized away when unused.
+        s_buf0 = s_buf0_st;
+        s_buf1 = s_buf0 + s_buf0_size;
+    }
+    else
+    {
+        // Use the dynamically allocated shared memory array.
+        s_buf0 = (scalar_t*)s_buf_raw;
+        s_buf1 = s_buf0 + s_buf0_size;
+    }
+
+    // Pointers to the buffers.
+    scalar_t* s_tileIn;       // Input tile:                      [relInX * tileInH + relInY]
+    scalar_t* s_tileUpX;      // After horizontal upsampling:     [relInY * tileUpW + relUpX]
+    scalar_t* s_tileUpXY;     // After upsampling:                [relUpY * tileUpW + relUpX]
+    scalar_t* s_tileDownX;    // After horizontal downsampling:   [relUpY * tileOutW + relOutX]
+    if (filterMode == MODE_SUSD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpX   = s_buf1;
+        s_tileUpXY  = s_buf0;
+        s_tileDownX = s_buf1;
+    }
+    else if (filterMode == MODE_FUSD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpXY  = s_buf1;
+        s_tileDownX = s_buf0;
+    }
+    else if (filterMode == MODE_SUFD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpX   = s_buf1;
+        s_tileUpXY  = s_buf0;
+    }
+    else if (filterMode == MODE_FUFD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpXY  = s_buf1;
+    }
+
+    // Allow large grids in z direction via per-launch offset.
+    int channelIdx = blockIdx.z + p.blockZofs;
+    int batchIdx = channelIdx / p.yShape.z;
+    channelIdx -= batchIdx * p.yShape.z;
+
+    // Offset to output feature map. In bytes.
+    index_t mapOfsOut = channelIdx * get_stride<index_t>(p.yStride.z) + batchIdx * get_stride<index_t>(p.yStride.w);
+
+    // Sign shift amount.
+    uint32_t signXo = ((threadIdx.x + p.sOfs.x) << 1) & 6;
+
+    // Inner tile loop.
+    #pragma unroll 1
+    for (int tileIdx = 0; !enableXrep || (tileIdx < MIN(p.tilesXrep, p.tilesXdim - p.tilesXrep * blockIdx.y)); tileIdx++)
+    {
+        // Locate output tile.
+        int tileX = enableXrep ? blockIdx.y * p.tilesXrep + tileIdx : blockIdx.x;
+        int tileOutX = tileX * tileOutW;
+        int tileOutY = (enableXrep ? blockIdx.x : blockIdx.y) * tileOutH;
+
+        // Locate input tile.
+        int tmpX = tileOutX * down - p.pad0.x;
+        int tmpY = tileOutY * down - p.pad0.y;
+        int tileInX = CEIL_DIV(tmpX, up);
+        int tileInY = CEIL_DIV(tmpY, up);
+        const int phaseInX = tileInX * up - tmpX;
+        const int phaseInY = tileInY * up - tmpY;
+
+        // Extra sync if input and output buffers are the same and we are not on first tile.
+        if (enableXrep && tileIdx > 0 && (filterMode == MODE_FUSD || (filterMode == MODE_SUFD && !downInline) || (filterMode == MODE_FUFD && downInline)))
+            __syncthreads();
+
+        // Load input tile & apply bias. Unrolled.
+        scalar_t b = (scalar_t)*(const T*)((const char*)p.b + (channelIdx * get_stride<index_t>(p.bStride)));
+        index_t mapOfsIn = channelIdx * get_stride<index_t>(p.xStride.z) + batchIdx * get_stride<index_t>(p.xStride.w);
+        int idx = threadIdx.x;
+        const int loopCountIN = CEIL_DIV(tileInW * tileInH, threadsPerBlock);
+        #pragma unroll
+        for (int loop = 0; loop < loopCountIN; loop++)
+        {
+            int relInX, relInY;
+            fast_div_mod<tileInW>(relInX, relInY, idx);
+            int inX = tileInX + relInX;
+            int inY = tileInY + relInY;
+            scalar_t v = 0;
+
+            if ((uint32_t)inX < p.xShape.x && (uint32_t)inY < p.xShape.y)
+                v = (scalar_t)*((const T*)((const char*)p.x + (inX * get_stride<index_t>(p.xStride.x) + inY * get_stride<index_t>(p.xStride.y) + mapOfsIn))) + b;
+
+            bool skip = (loop == loopCountIN-1) && (idx >= tileInW * tileInH);
+            if (!skip)
+                s_tileIn[idx] = v;
+
+            idx += threadsPerBlock;
+        }
+
+        if (filterMode == MODE_SUSD || filterMode == MODE_SUFD) // Separable upsampling filter.
+        {
+            // Horizontal upsampling.
+            __syncthreads();
+            if (up == 4)
+            {
+                for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up)
+                {
+                    int relUpX0, relInY;
+                    fast_div_mod<tileUpW>(relUpX0, relInY, idx);
+                    int relInX0 = relUpX0 / up;
+                    int src0 = relInX0 + tileInW * relInY;
+                    int dst = relInY * tileUpW + relUpX0;
+                    vec4_t v = InternalType<T>::zero_vec4();
+                    scalar_t a = s_tileIn[src0];
+                    if (phaseInX == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 3];
+                            v.z += a * (scalar_t)c_fu[step * up + 2];
+                            v.w += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else if (phaseInX == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.z += a * (scalar_t)c_fu[step * up + 3];
+                            v.w += a * (scalar_t)c_fu[step * up + 2];
+                        }
+                    }
+                    else if (phaseInX == 2)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 2];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                            v.z += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.w += a * (scalar_t)c_fu[step * up + 3];
+                        }
+                    }
+                    else // (phaseInX == 3)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 3];
+                            v.y += a * (scalar_t)c_fu[step * up + 2];
+                            v.z += a * (scalar_t)c_fu[step * up + 1];
+                            v.w += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                        }
+                    }
+                    s_tileUpX[dst+0] = v.x;
+                    s_tileUpX[dst+1] = v.y;
+                    s_tileUpX[dst+2] = v.z;
+                    s_tileUpX[dst+3] = v.w;
+                }
+            }
+            else if (up == 2)
+            {
+                bool p0 = (phaseInX == 0);
+                for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up)
+                {
+                    int relUpX0, relInY;
+                    fast_div_mod<tileUpW>(relUpX0, relInY, idx);
+                    int relInX0 = relUpX0 / up;
+                    int src0 = relInX0 + tileInW * relInY;
+                    int dst = relInY * tileUpW + relUpX0;
+                    vec2_t v = InternalType<T>::zero_vec2();
+                    scalar_t a = s_tileIn[src0];
+                    if (p0) // (phaseInX == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else // (phaseInX == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                        }
+                    }
+                    s_tileUpX[dst+0] = v.x;
+                    s_tileUpX[dst+1] = v.y;
+                }
+            }
+
+            // Vertical upsampling & nonlinearity.
+
+            __syncthreads();
+            int groupMask = 15 << ((threadIdx.x & 31) & ~3);
+            int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs.
+            int sShapeMaxY = MIN(p.sShape.y, tileOutY * down + tileUpH); // Avoid out-of-tile sign writes.
+            if (up == 4)
+            {
+                minY -= 3; // Adjust according to block height.
+                for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x)
+                {
+                    int relUpX, relInY0;
+                    fast_div_mod<tileUpW>(relUpX, relInY0, idx);
+                    int relUpY0 = relInY0 * up;
+                    int src0 = relInY0 * tileUpW + relUpX;
+                    int dst = relUpY0 * tileUpW + relUpX;
+                    vec4_t v = InternalType<T>::zero_vec4();
+
+                    scalar_t a = s_tileUpX[src0];
+                    if (phaseInY == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.y += a * (scalar_t)c_fu[step * up + 3];
+                            v.z += a * (scalar_t)c_fu[step * up + 2];
+                            v.w += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else if (phaseInY == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.z += a * (scalar_t)c_fu[step * up + 3];
+                            v.w += a * (scalar_t)c_fu[step * up + 2];
+                        }
+                    }
+                    else if (phaseInY == 2)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 2];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                            v.z += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.w += a * (scalar_t)c_fu[step * up + 3];
+                        }
+                    }
+                    else // (phaseInY == 3)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 3];
+                            v.y += a * (scalar_t)c_fu[step * up + 2];
+                            v.z += a * (scalar_t)c_fu[step * up + 1];
+                            v.w += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                        }
+                    }
+
+                    int x = tileOutX * down + relUpX;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+                    index_t si1 = si0 + p.sShape.x;
+                    index_t si2 = si0 + p.sShape.x * 2;
+                    index_t si3 = si0 + p.sShape.x * 3;
+
+                    v.x *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.y *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.z *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.w *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write signs.
+                            int sx = __float_as_uint(v.x) >> 31 <<  0;
+                            int sy = __float_as_uint(v.y) >> 31 <<  8;
+                            int sz = __float_as_uint(v.z) >> 31 << 16;
+                            int sw = __float_as_uint(v.w) >> 31 << 24;
+                            if (sx) v.x *= p.slope;
+                            if (sy) v.y *= p.slope;
+                            if (sz) v.z *= p.slope;
+                            if (sw) v.w *= p.slope;
+                            if (fabsf(v.x) > p.clamp) { sx = 2 <<  0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                            if (fabsf(v.y) > p.clamp) { sy = 2 <<  8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                            if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                            if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                // Combine signs.
+                                uint32_t s = sx + sy + sw + sz;
+                                s <<= (signX & 3) << 1;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                                if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); }
+                                if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); }
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write signs.
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                int sx = __float_as_uint(v.x) >> 31 <<  0;
+                                int sy = __float_as_uint(v.y) >> 31 <<  8;
+                                int sz = __float_as_uint(v.z) >> 31 << 16;
+                                int sw = __float_as_uint(v.w) >> 31 << 24;
+                                if (sx) v.x *= p.slope;
+                                if (sy) v.y *= p.slope;
+                                if (sz) v.z *= p.slope;
+                                if (sw) v.w *= p.slope;
+                                if (fabsf(v.x) > p.clamp) { sx = 2 <<  0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                                if (fabsf(v.y) > p.clamp) { sy = 2 <<  8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                                if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                                if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                                // Combine signs.
+                                uint32_t s = sx + sy + sw + sz;
+                                s <<= (signX & 3) << 1;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                                if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); }
+                                if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); }
+                            }
+                            else
+                            {
+                                // Just compute the values.
+                                if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                                if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                                if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                                if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead) // Read signs and apply.
+                    {
+                        if ((uint32_t)signXb < p.swLimit)
+                        {
+                            int ss = (signX & 3) << 1;
+                            if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> ss; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; }
+                            if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> ss; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; }
+                            if ((uint32_t)(signY + 2) < p.sShape.y) { int s = p.s[si2] >> ss; if (s & 1) v.z *= p.slope; if (s & 2) v.z = 0.f; }
+                            if ((uint32_t)(signY + 3) < p.sShape.y) { int s = p.s[si3] >> ss; if (s & 1) v.w *= p.slope; if (s & 2) v.w = 0.f; }
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                        if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                        if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                        if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                    }
+
+                    s_tileUpXY[dst + 0 * tileUpW] = v.x;
+                    if (relUpY0 + 1 < tileUpH) s_tileUpXY[dst + 1 * tileUpW] = v.y;
+                    if (relUpY0 + 2 < tileUpH) s_tileUpXY[dst + 2 * tileUpW] = v.z;
+                    if (relUpY0 + 3 < tileUpH) s_tileUpXY[dst + 3 * tileUpW] = v.w;
+                }
+            }
+            else if (up == 2)
+            {
+                minY -= 1; // Adjust according to block height.
+                for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x)
+                {
+                    int relUpX, relInY0;
+                    fast_div_mod<tileUpW>(relUpX, relInY0, idx);
+                    int relUpY0 = relInY0 * up;
+                    int src0 = relInY0 * tileUpW + relUpX;
+                    int dst = relUpY0 * tileUpW + relUpX;
+                    vec2_t v = InternalType<T>::zero_vec2();
+
+                    scalar_t a = s_tileUpX[src0];
+                    if (phaseInY == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else // (phaseInY == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                        }
+                    }
+
+                    int x = tileOutX * down + relUpX;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+                    index_t si1 = si0 + p.sShape.x;
+
+                    v.x *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.y *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write signs.
+                            int sx = __float_as_uint(v.x) >> 31 << 0;
+                            int sy = __float_as_uint(v.y) >> 31 << 8;
+                            if (sx) v.x *= p.slope;
+                            if (sy) v.y *= p.slope;
+                            if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                            if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                // Combine signs.
+                                int s = sx + sy;
+                                s <<= signXo;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write signs.
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                int sx = __float_as_uint(v.x) >> 31 << 0;
+                                int sy = __float_as_uint(v.y) >> 31 << 8;
+                                if (sx) v.x *= p.slope;
+                                if (sy) v.y *= p.slope;
+                                if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                                if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+
+                                // Combine signs.
+                                int s = sx + sy;
+                                s <<= signXo;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                            }
+                            else
+                            {
+                                // Just compute the values.
+                                if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                                if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead) // Read signs and apply.
+                    {
+                        if ((uint32_t)signXb < p.swLimit)
+                        {
+                            if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> signXo; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; }
+                            if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> signXo; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; }
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                        if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                    }
+
+                    if (!downInline)
+                    {
+                        // Write into temporary buffer.
+                        s_tileUpXY[dst] = v.x;
+                        if (relUpY0 < tileUpH - 1)
+                            s_tileUpXY[dst + tileUpW] = v.y;
+                    }
+                    else
+                    {
+                        // Write directly into output buffer.
+                        if ((uint32_t)x < p.yShape.x)
+                        {
+                            int ymax = MIN(p.yShape.y, tileUpH + tileOutY * down);
+                            index_t ofs = x * get_stride<index_t>(p.yStride.x) + y * get_stride<index_t>(p.yStride.y) + mapOfsOut;
+                            if ((uint32_t)y + 0 < p.yShape.y) *((T*)((char*)p.y + ofs)) = (T)(v.x * (scalar_t)c_fd[0]);
+                            if ((uint32_t)y + 1 < ymax) *((T*)((char*)p.y + ofs + get_stride<index_t>(p.yStride.y))) = (T)(v.y * (scalar_t)c_fd[0]);
+                        }
+                    }
+                }
+            }
+        }
+        else if (filterMode == MODE_FUSD || filterMode == MODE_FUFD)
+        {
+            // Full upsampling filter.
+
+            if (up == 2)
+            {
+                // 2 x 2-wide.
+                __syncthreads();
+                int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH + p.sOfs.y : 0; // Skip already written signs.
+                for (int idx = threadIdx.x * 4; idx < tileUpW * tileUpH; idx += blockDim.x * 4)
+                {
+                    int relUpX0, relUpY0;
+                    fast_div_mod<tileUpW>(relUpX0, relUpY0, idx);
+                    int relInX0 = CEIL_DIV(relUpX0 - phaseInX, up);
+                    int relInY0 = CEIL_DIV(relUpY0 - phaseInY, up);
+                    int src0 = relInX0 + tileInW * relInY0;
+                    int tap0y = (relInY0 * up + phaseInY - relUpY0);
+
+                    #define X_LOOP(TAPY, PX) \
+                        for (int sx = 0; sx < fuSize / up; sx++) \
+                        { \
+                            v.x += a * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \
+                            v.z += b * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 0) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \
+                            v.y += a * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \
+                            v.w += b * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 1) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \
+                        }
+
+                    vec4_t v = InternalType<T>::zero_vec4();
+                    if (tap0y == 0 && phaseInX == 0)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(0, 0) }
+                    if (tap0y == 0 && phaseInX == 1)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(0, 1) }
+                    if (tap0y == 1 && phaseInX == 0)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(1, 0) }
+                    if (tap0y == 1 && phaseInX == 1)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(1, 1) }
+
+                    #undef X_LOOP
+
+                    int x = tileOutX * down + relUpX0;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+
+                    v.x *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.y *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.z *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.w *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write signs.
+                            int sx = __float_as_uint(v.x) >> 31;
+                            int sy = __float_as_uint(v.y) >> 31;
+                            int sz = __float_as_uint(v.z) >> 31;
+                            int sw = __float_as_uint(v.w) >> 31;
+                            if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                            if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                            if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                            if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6);
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write signs.
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                int sx = __float_as_uint(v.x) >> 31;
+                                int sy = __float_as_uint(v.y) >> 31;
+                                int sz = __float_as_uint(v.z) >> 31;
+                                int sw = __float_as_uint(v.w) >> 31;
+                                if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                                if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                                if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                                if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                                p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6);
+                            }
+                            else
+                            {
+                                // Just compute the values.
+                                if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                                if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                                if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                                if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead) // Read sign and apply.
+                    {
+                        if ((uint32_t)signY < p.sShape.y)
+                        {
+                            int s = 0;
+                            if ((uint32_t)signXb     < p.swLimit) s  = p.s[si];
+                            if ((uint32_t)signXb + 1 < p.swLimit) s |= p.s[si + 1] << 8;
+                            s >>= (signX & 3) << 1;
+                            if (s & 0x01) v.x *= p.slope; if (s & 0x02) v.x = 0.f;
+                            if (s & 0x04) v.y *= p.slope; if (s & 0x08) v.y = 0.f;
+                            if (s & 0x10) v.z *= p.slope; if (s & 0x20) v.z = 0.f;
+                            if (s & 0x40) v.w *= p.slope; if (s & 0x80) v.w = 0.f;
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                        if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                        if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                        if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                    }
+
+                    s_tileUpXY[idx + 0] = v.x;
+                    s_tileUpXY[idx + 1] = v.y;
+                    s_tileUpXY[idx + 2] = v.z;
+                    s_tileUpXY[idx + 3] = v.w;
+                }
+            }
+            else if (up == 1)
+            {
+                __syncthreads();
+                uint32_t groupMask = 15 << ((threadIdx.x & 31) & ~3);
+                int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs.
+                for (int idx = threadIdx.x; idx < tileUpW * tileUpH; idx += blockDim.x)
+                {
+                    int relUpX0, relUpY0;
+                    fast_div_mod<tileUpW>(relUpX0, relUpY0, idx);
+                    scalar_t v = s_tileIn[idx] * (scalar_t)c_fu[0]; // 1x1 filter.
+
+                    int x = tileOutX * down + relUpX0;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+                    v *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write sign.
+                            uint32_t s = 0;
+                            uint32_t signXbit = (1u << signXo);
+                            if (v < 0.f)
+                            {
+                                s = signXbit;
+                                v *= p.slope;
+                            }
+                            if (fabsf(v) > p.clamp)
+                            {
+                                s = signXbit * 2;
+                                v = InternalType<T>::clamp(v, p.clamp);
+                            }
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                s += __shfl_xor_sync(groupMask, s, 1);  // Coalesce.
+                                s += __shfl_xor_sync(groupMask, s, 2);  // Coalesce.
+                                p.s[si] = s;                            // Write.
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write sign.
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                uint32_t s = 0;
+                                uint32_t signXbit = (1u << signXo);
+                                if (v < 0.f)
+                                {
+                                    s = signXbit;
+                                    v *= p.slope;
+                                }
+                                if (fabsf(v) > p.clamp)
+                                {
+                                    s = signXbit * 2;
+                                    v = InternalType<T>::clamp(v, p.clamp);
+                                }
+                                s += __shfl_xor_sync(groupMask, s, 1);  // Coalesce.
+                                s += __shfl_xor_sync(groupMask, s, 2);  // Coalesce.
+                                p.s[si] = s;                            // Write.
+                            }
+                            else
+                            {
+                                // Just compute the value.
+                                if (v < 0.f) v *= p.slope;
+                                v = InternalType<T>::clamp(v, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead)
+                    {
+                        // Read sign and apply if within sign tensor bounds.
+                        if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y)
+                        {
+                            int s = p.s[si];
+                            s >>= signXo;
+                            if (s & 1) v *= p.slope;
+                            if (s & 2) v = 0.f;
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v < 0.f) v *= p.slope;
+                        v = InternalType<T>::clamp(v, p.clamp);
+                    }
+
+                    if (!downInline) // Write into temporary buffer.
+                        s_tileUpXY[idx] = v;
+                    else if ((uint32_t)x < p.yShape.x && (uint32_t)y < p.yShape.y) // Write directly into output buffer
+                        *((T*)((char*)p.y + (x * get_stride<index_t>(p.yStride.x) + y * get_stride<index_t>(p.yStride.y) + mapOfsOut))) = (T)(v * (scalar_t)c_fd[0]);
+                }
+            }
+        }
+
+        // Downsampling.
+        if (filterMode == MODE_SUSD || filterMode == MODE_FUSD)
+        {
+            // Horizontal downsampling.
+            __syncthreads();
+            if (down == 4 && tileOutW % 4 == 0)
+            {
+                // Calculate 4 pixels at a time.
+                for (int idx = threadIdx.x * 4; idx < tileOutW * tileUpH; idx += blockDim.x * 4)
+                {
+                    int relOutX0, relUpY;
+                    fast_div_mod<tileOutW>(relOutX0, relUpY, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int src0 = relUpY * tileUpW + relUpX0;
+                    vec4_t v = InternalType<T>::zero_vec4();
+                    #pragma unroll
+                    for (int step = 0; step < fdSize; step++)
+                    {
+                        v.x += s_tileUpXY[src0 +  0 + step] * (scalar_t)c_fd[step];
+                        v.y += s_tileUpXY[src0 +  4 + step] * (scalar_t)c_fd[step];
+                        v.z += s_tileUpXY[src0 +  8 + step] * (scalar_t)c_fd[step];
+                        v.w += s_tileUpXY[src0 + 12 + step] * (scalar_t)c_fd[step];
+                    }
+                    s_tileDownX[idx+0] = v.x;
+                    s_tileDownX[idx+1] = v.y;
+                    s_tileDownX[idx+2] = v.z;
+                    s_tileDownX[idx+3] = v.w;
+                }
+            }
+            else if ((down == 2 || down == 4) && (tileOutW % 2 == 0))
+            {
+                // Calculate 2 pixels at a time.
+                for (int idx = threadIdx.x * 2; idx < tileOutW * tileUpH; idx += blockDim.x * 2)
+                {
+                    int relOutX0, relUpY;
+                    fast_div_mod<tileOutW>(relOutX0, relUpY, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int src0 = relUpY * tileUpW + relUpX0;
+                    vec2_t v = InternalType<T>::zero_vec2();
+                    #pragma unroll
+                    for (int step = 0; step < fdSize; step++)
+                    {
+                        v.x += s_tileUpXY[src0 +    0 + step] * (scalar_t)c_fd[step];
+                        v.y += s_tileUpXY[src0 + down + step] * (scalar_t)c_fd[step];
+                    }
+                    s_tileDownX[idx+0] = v.x;
+                    s_tileDownX[idx+1] = v.y;
+                }
+            }
+            else
+            {
+                // Calculate 1 pixel at a time.
+                for (int idx = threadIdx.x; idx < tileOutW * tileUpH; idx += blockDim.x)
+                {
+                    int relOutX0, relUpY;
+                    fast_div_mod<tileOutW>(relOutX0, relUpY, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int src = relUpY * tileUpW + relUpX0;
+                    scalar_t v = 0.f;
+                    #pragma unroll
+                    for (int step = 0; step < fdSize; step++)
+                        v += s_tileUpXY[src + step] * (scalar_t)c_fd[step];
+                    s_tileDownX[idx] = v;
+                }
+            }
+
+            // Vertical downsampling & store output tile.
+            __syncthreads();
+            for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x)
+            {
+                int relOutX, relOutY0;
+                fast_div_mod<tileOutW>(relOutX, relOutY0, idx);
+                int relUpY0 = relOutY0 * down;
+                int src0 = relUpY0 * tileOutW + relOutX;
+                scalar_t v = 0;
+                #pragma unroll
+                for (int step = 0; step < fdSize; step++)
+                    v += s_tileDownX[src0 + step * tileOutW] * (scalar_t)c_fd[step];
+
+                int outX = tileOutX + relOutX;
+                int outY = tileOutY + relOutY0;
+
+                if (outX < p.yShape.x & outY < p.yShape.y)
+                    *((T*)((char*)p.y + (outX * get_stride<index_t>(p.yStride.x) + outY * get_stride<index_t>(p.yStride.y) + mapOfsOut))) = (T)v;
+            }
+        }
+        else if (filterMode == MODE_SUFD || filterMode == MODE_FUFD)
+        {
+            // Full downsampling filter.
+            if (down == 2)
+            {
+                // 2-wide.
+                __syncthreads();
+                for (int idx = threadIdx.x * 2; idx < tileOutW * tileOutH; idx += blockDim.x * 2)
+                {
+                    int relOutX0, relOutY0;
+                    fast_div_mod<tileOutW>(relOutX0, relOutY0, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int relUpY0 = relOutY0 * down;
+                    int src0 = relUpY0 * tileUpW + relUpX0;
+                    vec2_t v = InternalType<T>::zero_vec2();
+                    #pragma unroll
+                    for (int sy = 0; sy < fdSize; sy++)
+                    #pragma unroll
+                    for (int sx = 0; sx < fdSize; sx++)
+                    {
+                        v.x += s_tileUpXY[src0 + 0 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE];
+                        v.y += s_tileUpXY[src0 + 2 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE];
+                    }
+
+                    int outX = tileOutX + relOutX0;
+                    int outY = tileOutY + relOutY0;
+                    if ((uint32_t)outY < p.yShape.y)
+                    {
+                        index_t ofs = outX * get_stride<index_t>(p.yStride.x) + outY * get_stride<index_t>(p.yStride.y) + mapOfsOut;
+                        if (outX + 0 < p.yShape.x) *((T*)((char*)p.y + ofs)) = (T)v.x;
+                        if (outX + 1 < p.yShape.x) *((T*)((char*)p.y + ofs + get_stride<index_t>(p.yStride.x))) = (T)v.y;
+                    }
+                }
+            }
+            else if (down == 1 && !downInline)
+            {
+                // Thread per pixel.
+                __syncthreads();
+                for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x)
+                {
+                    int relOutX0, relOutY0;
+                    fast_div_mod<tileOutW>(relOutX0, relOutY0, idx);
+                    scalar_t v = s_tileUpXY[idx] * (scalar_t)c_fd[0]; // 1x1 filter.
+
+                    int outX = tileOutX + relOutX0;
+                    int outY = tileOutY + relOutY0;
+                    if ((uint32_t)outX < p.yShape.x && (uint32_t)outY < p.yShape.y)
+                        *((T*)((char*)p.y + (outX * get_stride<index_t>(p.yStride.x) + outY * get_stride<index_t>(p.yStride.y) + mapOfsOut))) = (T)v;
+                }
+            }
+        }
+
+        if (!enableXrep)
+            break;
+    }
+}
+
+//------------------------------------------------------------------------
+// Compute activation function and signs for upsampled data tensor, modifying data tensor in-place. Used for accelerating the generic variant.
+// Sign tensor is known to be contiguous, and p.x and p.s have the same z, w dimensions. 64-bit indexing is always used.
+
+template <class T, bool signWrite, bool signRead>
+static __global__ void filtered_lrelu_act_kernel(filtered_lrelu_act_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+
+    // Indexing.
+    int32_t x = threadIdx.x + blockIdx.x * blockDim.x;
+    int32_t ymax = signWrite ? p.sShape.y : p.xShape.y;
+    int32_t qmax = p.xShape.z * p.xShape.w; // Combined minibatch*channel maximum index.
+
+    // Loop to accommodate oversized tensors.
+    for (int32_t q = blockIdx.z; q < qmax; q += gridDim.z)
+    for (int32_t y = blockIdx.y; y < ymax; y += gridDim.y)
+    {
+        // Extract z and w (channel, minibatch index).
+        int32_t w = q / p.xShape.z;
+        int32_t z = q - w * p.xShape.z;
+
+        // Choose behavior based on sign read/write mode.
+        if (signWrite)
+        {
+            // Process value if in p.x.
+            uint32_t s = 0;
+            if (x < p.xShape.x && y < p.xShape.y)
+            {
+                int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w;
+                T* pv = ((T*)p.x) + ix;
+                scalar_t v = (scalar_t)(*pv);
+
+                // Gain, LReLU, clamp.
+                v *= p.gain;
+                if (v < 0.f)
+                {
+                    v *= p.slope;
+                    s = 1; // Sign.
+                }
+                if (fabsf(v) > p.clamp)
+                {
+                    v = InternalType<T>::clamp(v, p.clamp);
+                    s = 2; // Clamp.
+                }
+
+                *pv = (T)v; // Write value.
+            }
+
+            // Coalesce into threads 0 and 16 of warp.
+            uint32_t m = (threadIdx.x & 16) ? 0xffff0000u : 0x0000ffffu;
+            s <<= ((threadIdx.x & 15) << 1); // Shift into place.
+            s |= __shfl_xor_sync(m, s, 1); // Distribute.
+            s |= __shfl_xor_sync(m, s, 2);
+            s |= __shfl_xor_sync(m, s, 4);
+            s |= __shfl_xor_sync(m, s, 8);
+
+            // Write signs if leader and in p.s.
+            if (!(threadIdx.x & 15) && x < p.sShape.x) // y is always in.
+            {
+                uint64_t is = x + p.sShape.x * (y + (int64_t)p.sShape.y * q); // Contiguous.
+                ((uint32_t*)p.s)[is >> 4] = s;
+            }
+        }
+        else if (signRead)
+        {
+            // Process value if in p.x.
+            if (x < p.xShape.x) // y is always in.
+            {
+                int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w;
+                T* pv = ((T*)p.x) + ix;
+                scalar_t v = (scalar_t)(*pv);
+                v *= p.gain;
+
+                // Apply sign buffer offset.
+                uint32_t sx = x + p.sOfs.x;
+                uint32_t sy = y + p.sOfs.y;
+
+                // Read and apply signs if we land inside valid region of sign buffer.
+                if (sx < p.sShape.x && sy < p.sShape.y)
+                {
+                    uint64_t is = (sx >> 2) + (p.sShape.x >> 2) * (sy + (uint64_t)p.sShape.y * q); // Contiguous.
+                    unsigned char s = p.s[is];
+                    s >>= (sx & 3) << 1; // Shift into place.
+                    if (s & 1) // Sign?
+                        v *= p.slope;
+                    if (s & 2) // Clamp?
+                        v = 0.f;
+                }
+
+                *pv = (T)v; // Write value.
+            }
+        }
+        else
+        {
+            // Forward pass with no sign write. Process value if in p.x.
+            if (x < p.xShape.x) // y is always in.
+            {
+                int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w;
+                T* pv = ((T*)p.x) + ix;
+                scalar_t v = (scalar_t)(*pv);
+                v *= p.gain;
+                if (v < 0.f)
+                    v *= p.slope;
+                if (fabsf(v) > p.clamp)
+                    v = InternalType<T>::clamp(v, p.clamp);
+                *pv = (T)v; // Write value.
+            }
+        }
+    }
+}
+
+template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void)
+{
+    return (void*)filtered_lrelu_act_kernel<T, signWrite, signRead>;
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB)
+{
+    filtered_lrelu_kernel_spec s = { 0 };
+
+    // Return the first matching kernel.
+#define CASE(SH, U, FU, D, FD, MODE, TW, TH, W, XR, WS) \
+    if (sharedKB >= SH) \
+    if ((p.fuShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_SUFD)) || (p.fuShape.y > 0 && (MODE == MODE_FUSD || MODE == MODE_FUFD))) \
+    if ((p.fdShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_FUSD)) || (p.fdShape.y > 0 && (MODE == MODE_SUFD || MODE == MODE_FUFD))) \
+    if (p.up == U && p.fuShape.x <= FU && p.fuShape.y <= FU && p.down == D && p.fdShape.x <= FD && p.fdShape.y <= FD) \
+    { \
+        static_assert((D*TW % 4) == 0, "down * tileWidth must be divisible by 4"); \
+        static_assert(FU % U == 0, "upscaling filter size must be multiple of upscaling factor"); \
+        static_assert(FD % D == 0, "downscaling filter size must be multiple of downscaling factor"); \
+        s.setup = (void*)setup_filters_kernel; \
+        s.exec = (void*)filtered_lrelu_kernel<T, index_t, SH, signWrite, signRead, MODE, U, FU, D, FD, TW, TH, W*32, !!XR, !!WS>; \
+        s.tileOut = make_int2(TW, TH); \
+        s.numWarps = W; \
+        s.xrep = XR; \
+        s.dynamicSharedKB = (SH == 48) ? 0 : SH; \
+        return s; \
+    }
+
+    // Launch parameters for various kernel specializations.
+    // Small filters must be listed before large filters, otherwise the kernel for larger filter will always match first.
+    // Kernels that use more shared memory must be listed before those that use less, for the same reason.
+
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/1,1,  /*mode*/MODE_FUFD, /*tw,th,warps,xrep,wskip*/64,  178, 32,  0,  0) // 1t-upf1-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/1,1,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/152, 95,  16,  0,  0) // 4t-ups2-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/2,8,  /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56,  22,  16,  0,  0) // 4t-upf1-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/2,8,  /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56,  29,  16,  11, 0) // 4t-ups2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/2,8,  /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/60,  28,  16,  0,  0) // 4t-upf2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/2,8,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56,  28,  16,  0,  0) // 4t-ups2-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8,  /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56,  31,  16,  11, 0) // 4t-ups4-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56,  36,  16,  0,  0) // 4t-ups4-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/4,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16,  22,  16,  12, 0) // 4t-ups2-downs4
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/4,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/29,  15,  16,  0,  0) // 4t-upf2-downs4
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/1,1,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/96,  150, 28,  0,  0) // 6t-ups2-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32,  35,  24,  0,  0) // 6t-upf1-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  46,  16,  10, 0) // 6t-ups2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/58,  28,  24,  8,  0) // 6t-upf2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/52,  28,  16,  0,  0) // 6t-ups2-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  51,  16,  5,  0) // 6t-ups4-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32,  56,  16,  6,  0) // 6t-ups4-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16,  18,  16,  12, 0) // 6t-ups2-downs4
+    CASE(/*sharedKB*/96, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27,  31,  32,  6,  0) // 6t-upf2-downs4 96kB
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27,  13,  24,  0,  0) // 6t-upf2-downs4
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/1,1,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/148, 89,  24,  0,  0) // 8t-ups2-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32,  31,  16,  5,  0) // 8t-upf1-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  41,  16,  9,  0) // 8t-ups2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56,  26,  24,  0,  0) // 8t-upf2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32,  40,  16,  0,  0) // 8t-ups2-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  46,  24,  5,  0) // 8t-ups4-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32,  50,  16,  0,  0) // 8t-ups4-downf2
+    CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/24,  24,  32,  12, 1) // 8t-ups2-downs4 96kB
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16,  13,  16,  10, 1) // 8t-ups2-downs4
+    CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25,  28,  28,  4,  0) // 8t-upf2-downs4 96kB
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25,  10,  24,  0,  0) // 8t-upf2-downs4
+
+    #undef CASE
+    return s; // No kernel found.
+}
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/filtered_lrelu.h b/torch_utils/ops/filtered_lrelu.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2bfd1dd537909de9cd3b14765a482056391683b
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu.h
@@ -0,0 +1,94 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <cuda_runtime.h>
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct filtered_lrelu_kernel_params
+{
+    // These parameters decide which kernel to use.
+    int             up;         // upsampling ratio (1, 2, 4)
+    int             down;       // downsampling ratio (1, 2, 4)
+    int2            fuShape;    // [size, 1] | [size, size]
+    int2            fdShape;    // [size, 1] | [size, size]
+
+    int             _dummy;     // Alignment.
+
+    // Rest of the parameters.
+    const void*     x;          // Input tensor.
+    void*           y;          // Output tensor.
+    const void*     b;          // Bias tensor.
+    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
+    const float*    fu;         // Upsampling filter.
+    const float*    fd;         // Downsampling filter.
+
+    int2            pad0;       // Left/top padding.
+    float           gain;       // Additional gain factor.
+    float           slope;      // Leaky ReLU slope on negative side.
+    float           clamp;      // Clamp after nonlinearity.
+    int             flip;       // Filter kernel flip for gradient computation.
+
+    int             tilesXdim;  // Original number of horizontal output tiles.
+    int             tilesXrep;  // Number of horizontal tiles per CTA.
+    int             blockZofs;  // Block z offset to support large minibatch, channel dimensions.
+
+    int4            xShape;     // [width, height, channel, batch]
+    int4            yShape;     // [width, height, channel, batch]
+    int2            sShape;     // [width, height] - width is in bytes. Contiguous. Zeros if unused.
+    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
+    int             swLimit;    // Active width of sign tensor in bytes.
+
+    longlong4       xStride;    // Strides of all tensors except signs, same component order as shapes.
+    longlong4       yStride;    //
+    int64_t         bStride;    //
+    longlong3       fuStride;   //
+    longlong3       fdStride;   //
+};
+
+struct filtered_lrelu_act_kernel_params
+{
+    void*           x;          // Input/output, modified in-place.
+    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
+
+    float           gain;       // Additional gain factor.
+    float           slope;      // Leaky ReLU slope on negative side.
+    float           clamp;      // Clamp after nonlinearity.
+
+    int4            xShape;     // [width, height, channel, batch]
+    longlong4       xStride;    // Input/output tensor strides, same order as in shape.
+    int2            sShape;     // [width, height] - width is in elements. Contiguous. Zeros if unused.
+    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel specialization.
+
+struct filtered_lrelu_kernel_spec
+{
+    void*   setup;              // Function for filter kernel setup.
+    void*   exec;               // Function for main operation.
+    int2    tileOut;            // Width/height of launch tile.
+    int     numWarps;           // Number of warps per thread block, determines launch block size.
+    int     xrep;               // For processing multiple horizontal tiles per thread block.
+    int     dynamicSharedKB;    // How much dynamic shared memory the exec kernel wants.
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB);
+template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void);
+template <bool signWrite, bool signRead> cudaError_t copy_filters(cudaStream_t stream);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/filtered_lrelu.py b/torch_utils/ops/filtered_lrelu.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6d22247caae82ad806a16e967ef50c96be5d78a
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu.py
@@ -0,0 +1,377 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+import os
+import numpy as np
+import torch
+import warnings
+
+from .. import custom_ops
+from .. import misc
+from . import upfirdn2d
+from . import bias_act
+
+#----------------------------------------------------------------------------
+
+_plugin = None
+
+
+def _init():
+    global _plugin
+    if _plugin is None:
+        _plugin = custom_ops.get_plugin(
+            module_name='filtered_lrelu_plugin',
+            sources=[
+                'filtered_lrelu.cpp', 'filtered_lrelu_wr.cu',
+                'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'
+            ],
+            headers=['filtered_lrelu.h', 'filtered_lrelu.cu'],
+            source_dir=os.path.dirname(__file__),
+            extra_cuda_cflags=['--use_fast_math'],
+        )
+    return True
+
+
+def _get_filter_size(f):
+    if f is None:
+        return 1, 1
+    assert isinstance(f, torch.Tensor)
+    assert 1 <= f.ndim <= 2
+    return f.shape[-1], f.shape[0]  # width, height
+
+
+def _parse_padding(padding):
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    assert isinstance(padding, (list, tuple))
+    assert all(isinstance(x, (int, np.integer)) for x in padding)
+    padding = [int(x) for x in padding]
+    if len(padding) == 2:
+        px, py = padding
+        padding = [px, px, py, py]
+    px0, px1, py0, py1 = padding
+    return px0, px1, py0, py1
+
+
+#----------------------------------------------------------------------------
+
+
+def filtered_lrelu(x,
+                   fu=None,
+                   fd=None,
+                   b=None,
+                   up=1,
+                   down=1,
+                   padding=0,
+                   gain=np.sqrt(2),
+                   slope=0.2,
+                   clamp=None,
+                   flip_filter=False,
+                   impl='cuda'):
+    r"""Filtered leaky ReLU for a batch of 2D images.
+
+    Performs the following sequence of operations for each channel:
+
+    1. Add channel-specific bias if provided (`b`).
+
+    2. Upsample the image by inserting N-1 zeros after each pixel (`up`).
+
+    3. Pad the image with the specified number of zeros on each side (`padding`).
+       Negative padding corresponds to cropping the image.
+
+    4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it
+       so that the footprint of all output pixels lies within the input image.
+
+    5. Multiply each value by the provided gain factor (`gain`).
+
+    6. Apply leaky ReLU activation function to each value.
+
+    7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided.
+
+    8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking
+       it so that the footprint of all output pixels lies within the input image.
+
+    9. Downsample the image by keeping every Nth pixel (`down`).
+
+    The fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports gradients of arbitrary order.
+
+    Args:
+        x:           Float32/float16/float64 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        fu:          Float32 upsampling FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        fd:          Float32 downsampling FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        b:           Bias vector, or `None` to disable. Must be a 1D tensor of the same type
+                     as `x`. The length of vector must must match the channel dimension of `x`.
+        up:          Integer upsampling factor (default: 1).
+        down:        Integer downsampling factor. (default: 1).
+        padding:     Padding with respect to the upsampled image. Can be a single number
+                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        gain:        Overall scaling factor for signal magnitude (default: sqrt(2)).
+        slope:       Slope on the negative side of leaky ReLU (default: 0.2).
+        clamp:       Maximum magnitude for leaky ReLU output (default: None).
+        flip_filter: False = convolution, True = correlation (default: False).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _filtered_lrelu_cuda(up=up,
+                                    down=down,
+                                    padding=padding,
+                                    gain=gain,
+                                    slope=slope,
+                                    clamp=clamp,
+                                    flip_filter=flip_filter).apply(
+                                        x, fu, fd, b, None, 0, 0)
+    return _filtered_lrelu_ref(x,
+                               fu=fu,
+                               fd=fd,
+                               b=b,
+                               up=up,
+                               down=down,
+                               padding=padding,
+                               gain=gain,
+                               slope=slope,
+                               clamp=clamp,
+                               flip_filter=flip_filter)
+
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+def _filtered_lrelu_ref(x,
+                        fu=None,
+                        fd=None,
+                        b=None,
+                        up=1,
+                        down=1,
+                        padding=0,
+                        gain=np.sqrt(2),
+                        slope=0.2,
+                        clamp=None,
+                        flip_filter=False):
+    """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using
+    existing `upfirdn2n()` and `bias_act()` ops.
+    """
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    fu_w, fu_h = _get_filter_size(fu)
+    fd_w, fd_h = _get_filter_size(fd)
+    if b is not None:
+        assert isinstance(b, torch.Tensor) and b.dtype == x.dtype
+        misc.assert_shape(b, [x.shape[1]])
+    assert isinstance(up, int) and up >= 1
+    assert isinstance(down, int) and down >= 1
+    px0, px1, py0, py1 = _parse_padding(padding)
+    assert gain == float(gain) and gain > 0
+    assert slope == float(slope) and slope >= 0
+    assert clamp is None or (clamp == float(clamp) and clamp >= 0)
+
+    # Calculate output size.
+    batch_size, channels, in_h, in_w = x.shape
+    in_dtype = x.dtype
+    out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) +
+             (down - 1)) // down
+    out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) +
+             (down - 1)) // down
+
+    # Compute using existing ops.
+    x = bias_act.bias_act(x=x, b=b)  # Apply bias.
+    x = upfirdn2d.upfirdn2d(x=x,
+                            f=fu,
+                            up=up,
+                            padding=[px0, px1, py0, py1],
+                            gain=up**2,
+                            flip_filter=flip_filter)  # Upsample.
+    x = bias_act.bias_act(x=x,
+                          act='lrelu',
+                          alpha=slope,
+                          gain=gain,
+                          clamp=clamp)  # Bias, leaky ReLU, clamp.
+    x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down,
+                            flip_filter=flip_filter)  # Downsample.
+
+    # Check output shape & dtype.
+    misc.assert_shape(x, [batch_size, channels, out_h, out_w])
+    assert x.dtype == in_dtype
+    return x
+
+
+#----------------------------------------------------------------------------
+
+_filtered_lrelu_cuda_cache = dict()
+
+
+def _filtered_lrelu_cuda(up=1,
+                         down=1,
+                         padding=0,
+                         gain=np.sqrt(2),
+                         slope=0.2,
+                         clamp=None,
+                         flip_filter=False):
+    """Fast CUDA implementation of `filtered_lrelu()` using custom ops.
+    """
+    assert isinstance(up, int) and up >= 1
+    assert isinstance(down, int) and down >= 1
+    px0, px1, py0, py1 = _parse_padding(padding)
+    assert gain == float(gain) and gain > 0
+    gain = float(gain)
+    assert slope == float(slope) and slope >= 0
+    slope = float(slope)
+    assert clamp is None or (clamp == float(clamp) and clamp >= 0)
+    clamp = float(clamp if clamp is not None else 'inf')
+
+    # Lookup from cache.
+    key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter)
+    if key in _filtered_lrelu_cuda_cache:
+        return _filtered_lrelu_cuda_cache[key]
+
+    # Forward op.
+    class FilteredLReluCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, fu, fd, b, si, sx, sy):  # pylint: disable=arguments-differ
+            assert isinstance(x, torch.Tensor) and x.ndim == 4
+
+            # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable).
+            if fu is None:
+                fu = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            if fd is None:
+                fd = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            assert 1 <= fu.ndim <= 2
+            assert 1 <= fd.ndim <= 2
+
+            # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1.
+            if up == 1 and fu.ndim == 1 and fu.shape[0] == 1:
+                fu = fu.square()[None]
+            if down == 1 and fd.ndim == 1 and fd.shape[0] == 1:
+                fd = fd.square()[None]
+
+            # Missing sign input tensor.
+            if si is None:
+                si = torch.empty([0])
+
+            # Missing bias tensor.
+            if b is None:
+                b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device)
+
+            # Construct internal sign tensor only if gradients are needed.
+            write_signs = (si.numel() == 0) and (x.requires_grad
+                                                 or b.requires_grad)
+
+            # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout.
+            strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1]
+            if any(a < b for a, b in zip(strides[:-1], strides[1:])):
+                warnings.warn(
+                    "low-performance memory layout detected in filtered_lrelu input",
+                    RuntimeWarning)
+
+            # Call C++/Cuda plugin if datatype is supported.
+            if x.dtype in [torch.float16, torch.float32]:
+                if torch.cuda.current_stream(
+                        x.device) != torch.cuda.default_stream(x.device):
+                    warnings.warn(
+                        "filtered_lrelu called with non-default cuda stream but concurrent execution is not supported",
+                        RuntimeWarning)
+                y, so, return_code = _plugin.filtered_lrelu(
+                    x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy,
+                    gain, slope, clamp, flip_filter, write_signs)
+            else:
+                return_code = -1
+
+            # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because
+            # only the bit-packed sign tensor is retained for gradient computation.
+            if return_code < 0:
+                warnings.warn(
+                    "filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback",
+                    RuntimeWarning)
+
+                y = x.add(b.unsqueeze(-1).unsqueeze(-1))  # Add bias.
+                y = upfirdn2d.upfirdn2d(x=y,
+                                        f=fu,
+                                        up=up,
+                                        padding=[px0, px1, py0, py1],
+                                        gain=up**2,
+                                        flip_filter=flip_filter)  # Upsample.
+                so = _plugin.filtered_lrelu_act_(
+                    y, si, sx, sy, gain, slope, clamp, write_signs
+                )  # Activation function and sign handling. Modifies y in-place.
+                y = upfirdn2d.upfirdn2d(x=y,
+                                        f=fd,
+                                        down=down,
+                                        flip_filter=flip_filter)  # Downsample.
+
+            # Prepare for gradient computation.
+            ctx.save_for_backward(fu, fd, (si if si.numel() else so))
+            ctx.x_shape = x.shape
+            ctx.y_shape = y.shape
+            ctx.s_ofs = sx, sy
+            return y
+
+        @staticmethod
+        def backward(ctx, dy):  # pylint: disable=arguments-differ
+            fu, fd, si = ctx.saved_tensors
+            _, _, xh, xw = ctx.x_shape
+            _, _, yh, yw = ctx.y_shape
+            sx, sy = ctx.s_ofs
+            dx = None  # 0
+            dfu = None
+            assert not ctx.needs_input_grad[1]
+            dfd = None
+            assert not ctx.needs_input_grad[2]
+            db = None  # 3
+            dsi = None
+            assert not ctx.needs_input_grad[4]
+            dsx = None
+            assert not ctx.needs_input_grad[5]
+            dsy = None
+            assert not ctx.needs_input_grad[6]
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]:
+                pp = [
+                    (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0,
+                    xw * up - yw * down + px0 - (up - 1),
+                    (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0,
+                    xh * up - yh * down + py0 - (up - 1),
+                ]
+                gg = gain * (up**2) / (down**2)
+                ff = (not flip_filter)
+                sx = sx - (fu.shape[-1] - 1) + px0
+                sy = sy - (fu.shape[0] - 1) + py0
+                dx = _filtered_lrelu_cuda(up=down,
+                                          down=up,
+                                          padding=pp,
+                                          gain=gg,
+                                          slope=slope,
+                                          clamp=None,
+                                          flip_filter=ff).apply(
+                                              dy, fd, fu, None, si, sx, sy)
+
+            if ctx.needs_input_grad[3]:
+                db = dx.sum([0, 2, 3])
+
+            return dx, dfu, dfd, db, dsi, dsx, dsy
+
+    # Add to cache.
+    _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda
+    return FilteredLReluCuda
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/filtered_lrelu_ns.cu b/torch_utils/ops/filtered_lrelu_ns.cu
new file mode 100644
index 0000000000000000000000000000000000000000..8a3eae46215c3babea2c54e3ae255b05f4d777af
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu_ns.cu
@@ -0,0 +1,31 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include "filtered_lrelu.cu"
+
+// Template/kernel specializations for no signs mode (no gradients required).
+
+// Full op, 32-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int32_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Full op, 64-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int64_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Activation/signs only for generic variant. 64-bit indexing.
+template void* choose_filtered_lrelu_act_kernel<c10::Half, false, false>(void);
+template void* choose_filtered_lrelu_act_kernel<float,     false, false>(void);
+template void* choose_filtered_lrelu_act_kernel<double,    false, false>(void);
+
+// Copy filters to constant memory.
+template cudaError_t copy_filters<false, false>(cudaStream_t stream);
diff --git a/torch_utils/ops/filtered_lrelu_rd.cu b/torch_utils/ops/filtered_lrelu_rd.cu
new file mode 100644
index 0000000000000000000000000000000000000000..3cd43ec0648d3db05e5808299fc0ee318e5ceaa6
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu_rd.cu
@@ -0,0 +1,31 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include "filtered_lrelu.cu"
+
+// Template/kernel specializations for sign read mode.
+
+// Full op, 32-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int32_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Full op, 64-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int64_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Activation/signs only for generic variant. 64-bit indexing.
+template void* choose_filtered_lrelu_act_kernel<c10::Half, false, true>(void);
+template void* choose_filtered_lrelu_act_kernel<float,     false, true>(void);
+template void* choose_filtered_lrelu_act_kernel<double,    false, true>(void);
+
+// Copy filters to constant memory.
+template cudaError_t copy_filters<false, true>(cudaStream_t stream);
diff --git a/torch_utils/ops/filtered_lrelu_wr.cu b/torch_utils/ops/filtered_lrelu_wr.cu
new file mode 100644
index 0000000000000000000000000000000000000000..bc2fa06912eb703dd77ca64533208428bdf373ac
--- /dev/null
+++ b/torch_utils/ops/filtered_lrelu_wr.cu
@@ -0,0 +1,31 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include "filtered_lrelu.cu"
+
+// Template/kernel specializations for sign write mode.
+
+// Full op, 32-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int32_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Full op, 64-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int64_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Activation/signs only for generic variant. 64-bit indexing.
+template void* choose_filtered_lrelu_act_kernel<c10::Half, true, false>(void);
+template void* choose_filtered_lrelu_act_kernel<float,     true, false>(void);
+template void* choose_filtered_lrelu_act_kernel<double,    true, false>(void);
+
+// Copy filters to constant memory.
+template cudaError_t copy_filters<true, false>(cudaStream_t stream);
diff --git a/torch_utils/ops/fma.py b/torch_utils/ops/fma.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5feae8693cc3cd36d57f77f0f5d16c4dc6b990d
--- /dev/null
+++ b/torch_utils/ops/fma.py
@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
+
+import torch
+
+#----------------------------------------------------------------------------
+
+
+def fma(a, b, c):  # => a * b + c
+    return _FusedMultiplyAdd.apply(a, b, c)
+
+
+#----------------------------------------------------------------------------
+
+
+class _FusedMultiplyAdd(torch.autograd.Function):  # a * b + c
+    @staticmethod
+    def forward(ctx, a, b, c):  # pylint: disable=arguments-differ
+        out = torch.addcmul(c, a, b)
+        ctx.save_for_backward(a, b)
+        ctx.c_shape = c.shape
+        return out
+
+    @staticmethod
+    def backward(ctx, dout):  # pylint: disable=arguments-differ
+        a, b = ctx.saved_tensors
+        c_shape = ctx.c_shape
+        da = None
+        db = None
+        dc = None
+
+        if ctx.needs_input_grad[0]:
+            da = _unbroadcast(dout * b, a.shape)
+
+        if ctx.needs_input_grad[1]:
+            db = _unbroadcast(dout * a, b.shape)
+
+        if ctx.needs_input_grad[2]:
+            dc = _unbroadcast(dout, c_shape)
+
+        return da, db, dc
+
+
+#----------------------------------------------------------------------------
+
+
+def _unbroadcast(x, shape):
+    extra_dims = x.ndim - len(shape)
+    assert extra_dims >= 0
+    dim = [
+        i for i in range(x.ndim)
+        if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)
+    ]
+    if len(dim):
+        x = x.sum(dim=dim, keepdim=True)
+    if extra_dims:
+        x = x.reshape(-1, *x.shape[extra_dims + 1:])
+    assert x.shape == shape
+    return x
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/grid_sample_gradfix.py b/torch_utils/ops/grid_sample_gradfix.py
new file mode 100644
index 0000000000000000000000000000000000000000..14598b5ddcc76353ac4d2c0792caea5e03eb25d9
--- /dev/null
+++ b/torch_utils/ops/grid_sample_gradfix.py
@@ -0,0 +1,96 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Custom replacement for `torch.nn.functional.grid_sample` that
+supports arbitrarily high order gradients between the input and output.
+Only works on 2D images and assumes
+`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
+
+import torch
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False  # Enable the custom op by setting this to true.
+
+#----------------------------------------------------------------------------
+
+
+def grid_sample(input, grid):
+    if _should_use_custom_op():
+        return _GridSample2dForward.apply(input, grid)
+    return torch.nn.functional.grid_sample(input=input,
+                                           grid=grid,
+                                           mode='bilinear',
+                                           padding_mode='zeros',
+                                           align_corners=False)
+
+
+#----------------------------------------------------------------------------
+
+
+def _should_use_custom_op():
+    return enabled
+
+
+#----------------------------------------------------------------------------
+
+
+class _GridSample2dForward(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, grid):
+        assert input.ndim == 4
+        assert grid.ndim == 4
+        output = torch.nn.functional.grid_sample(input=input,
+                                                 grid=grid,
+                                                 mode='bilinear',
+                                                 padding_mode='zeros',
+                                                 align_corners=False)
+        ctx.save_for_backward(input, grid)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, grid = ctx.saved_tensors
+        grad_input, grad_grid = _GridSample2dBackward.apply(
+            grad_output, input, grid)
+        return grad_input, grad_grid
+
+
+#----------------------------------------------------------------------------
+
+
+class _GridSample2dBackward(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, grad_output, input, grid):
+        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
+        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
+        ctx.save_for_backward(grid)
+        return grad_input, grad_grid
+
+    @staticmethod
+    def backward(ctx, grad2_grad_input, grad2_grad_grid):
+        _ = grad2_grad_grid  # unused
+        grid, = ctx.saved_tensors
+        grad2_grad_output = None
+        grad2_input = None
+        grad2_grid = None
+
+        if ctx.needs_input_grad[0]:
+            grad2_grad_output = _GridSample2dForward.apply(
+                grad2_grad_input, grid)
+
+        assert not ctx.needs_input_grad[2]
+        return grad2_grad_output, grad2_input, grad2_grid
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.cpp b/torch_utils/ops/upfirdn2d.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..c1769c3cbe4dd04f76f9ccef726680720e6f39c8
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.cpp
@@ -0,0 +1,111 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+
+static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
+{
+    // Validate arguments.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
+    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
+    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
+    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
+    TORCH_CHECK(x.numel() > 0, "x has zero size");
+    TORCH_CHECK(f.numel() > 0, "f has zero size");
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
+    TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large");
+    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
+    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
+    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
+
+    // Create output tensor.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
+    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
+    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
+    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
+    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
+    TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large");
+
+    // Initialize CUDA kernel parameters.
+    upfirdn2d_kernel_params p;
+    p.x             = x.data_ptr();
+    p.f             = f.data_ptr<float>();
+    p.y             = y.data_ptr();
+    p.up            = make_int2(upx, upy);
+    p.down          = make_int2(downx, downy);
+    p.pad0          = make_int2(padx0, pady0);
+    p.flip          = (flip) ? 1 : 0;
+    p.gain          = gain;
+    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
+    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
+    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
+    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
+    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
+    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
+    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
+
+    // Choose CUDA kernel.
+    upfirdn2d_kernel_spec spec;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
+    {
+        spec = choose_upfirdn2d_kernel<scalar_t>(p);
+    });
+
+    // Set looping options.
+    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
+    p.loopMinor     = spec.loopMinor;
+    p.loopX         = spec.loopX;
+    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
+    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
+
+    // Compute grid size.
+    dim3 blockSize, gridSize;
+    if (spec.tileOutW < 0) // large
+    {
+        blockSize = dim3(4, 32, 1);
+        gridSize = dim3(
+            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
+            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
+            p.launchMajor);
+    }
+    else // small
+    {
+        blockSize = dim3(256, 1, 1);
+        gridSize = dim3(
+            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
+            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
+            p.launchMajor);
+    }
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
+    return y;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("upfirdn2d", &upfirdn2d);
+}
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.cu b/torch_utils/ops/upfirdn2d.cu
new file mode 100644
index 0000000000000000000000000000000000000000..7d182d7b86a9058d0c007b13716d6e7f08207f42
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.cu
@@ -0,0 +1,388 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <c10/util/Half.h>
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>     { typedef double scalar_t; };
+template <> struct InternalType<float>      { typedef float  scalar_t; };
+template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
+
+static __device__ __forceinline__ int floor_div(int a, int b)
+{
+    int t = 1 - a / b;
+    return (a + t * b) / b - t;
+}
+
+//------------------------------------------------------------------------
+// Generic CUDA implementation for large filters.
+
+template <class T> static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+
+    // Calculate thread index.
+    int minorBase = blockIdx.x * blockDim.x + threadIdx.x;
+    int outY = minorBase / p.launchMinor;
+    minorBase -= outY * p.launchMinor;
+    int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
+    int majorBase = blockIdx.z * p.loopMajor;
+    if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor)
+        return;
+
+    // Setup Y receptive field.
+    int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y;
+    int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y);
+    int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY;
+    int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y;
+    if (p.flip)
+        filterY = p.filterSize.y - 1 - filterY;
+
+    // Loop over major, minor, and X.
+    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
+    for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor)
+    {
+        int nc = major * p.sizeMinor + minor;
+        int n = nc / p.inSize.z;
+        int c = nc - n * p.inSize.z;
+        for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y)
+        {
+            // Setup X receptive field.
+            int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x;
+            int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x);
+            int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX;
+            int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x;
+            if (p.flip)
+                filterX = p.filterSize.x - 1 - filterX;
+
+            // Initialize pointers.
+            const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
+            const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y];
+            int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x;
+            int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y;
+
+            // Inner loop.
+            scalar_t v = 0;
+            for (int y = 0; y < h; y++)
+            {
+                for (int x = 0; x < w; x++)
+                {
+                    v += (scalar_t)(*xp) * (scalar_t)(*fp);
+                    xp += p.inStride.x;
+                    fp += filterStepX;
+                }
+                xp += p.inStride.y - w * p.inStride.x;
+                fp += filterStepY - w * filterStepX;
+            }
+
+            // Store result.
+            v *= p.gain;
+            ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
+        }
+    }
+}
+
+//------------------------------------------------------------------------
+// Specialized CUDA implementation for small filters.
+
+template <class T, int upx, int upy, int downx, int downy, int filterW, int filterH, int tileOutW, int tileOutH, int loopMinor>
+static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1;
+    const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1;
+    __shared__ volatile scalar_t sf[filterH][filterW];
+    __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor];
+
+    // Calculate tile index.
+    int minorBase = blockIdx.x;
+    int tileOutY = minorBase / p.launchMinor;
+    minorBase -= tileOutY * p.launchMinor;
+    minorBase *= loopMinor;
+    tileOutY *= tileOutH;
+    int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
+    int majorBase = blockIdx.z * p.loopMajor;
+    if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor)
+        return;
+
+    // Load filter (flipped).
+    for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x)
+    {
+        int fy = tapIdx / filterW;
+        int fx = tapIdx - fy * filterW;
+        scalar_t v = 0;
+        if (fx < p.filterSize.x & fy < p.filterSize.y)
+        {
+            int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx;
+            int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy;
+            v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y];
+        }
+        sf[fy][fx] = v;
+    }
+
+    // Loop over major and X.
+    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
+    {
+        int baseNC = major * p.sizeMinor + minorBase;
+        int n = baseNC / p.inSize.z;
+        int baseC = baseNC - n * p.inSize.z;
+        for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW)
+        {
+            // Load input pixels.
+            int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x;
+            int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y;
+            int tileInX = floor_div(tileMidX, upx);
+            int tileInY = floor_div(tileMidY, upy);
+            __syncthreads();
+            for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x)
+            {
+                int relC = inIdx;
+                int relInX = relC / loopMinor;
+                int relInY = relInX / tileInW;
+                relC -= relInX * loopMinor;
+                relInX -= relInY * tileInW;
+                int c = baseC + relC;
+                int inX = tileInX + relInX;
+                int inY = tileInY + relInY;
+                scalar_t v = 0;
+                if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z)
+                    v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
+                sx[relInY][relInX][relC] = v;
+            }
+
+            // Loop over output pixels.
+            __syncthreads();
+            for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x)
+            {
+                int relC = outIdx;
+                int relOutX = relC / loopMinor;
+                int relOutY = relOutX / tileOutW;
+                relC -= relOutX * loopMinor;
+                relOutX -= relOutY * tileOutW;
+                int c = baseC + relC;
+                int outX = tileOutX + relOutX;
+                int outY = tileOutY + relOutY;
+
+                // Setup receptive field.
+                int midX = tileMidX + relOutX * downx;
+                int midY = tileMidY + relOutY * downy;
+                int inX = floor_div(midX, upx);
+                int inY = floor_div(midY, upy);
+                int relInX = inX - tileInX;
+                int relInY = inY - tileInY;
+                int filterX = (inX + 1) * upx - midX - 1; // flipped
+                int filterY = (inY + 1) * upy - midY - 1; // flipped
+
+                // Inner loop.
+                if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z)
+                {
+                    scalar_t v = 0;
+                    #pragma unroll
+                    for (int y = 0; y < filterH / upy; y++)
+                        #pragma unroll
+                        for (int x = 0; x < filterW / upx; x++)
+                            v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx];
+                    v *= p.gain;
+                    ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
+                }
+            }
+        }
+    }
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p)
+{
+    int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y;
+    upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,1, 4}; // contiguous
+    if (s == 1)           spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,4, 1}; // channels_last
+
+    // No up/downsampling.
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,24, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,16, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1,  128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1,  128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,   128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24,  32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16,  32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,   32,32,1>, 32,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,24, 32,32,1>,  32,32,1,  1};
+        if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,16, 32,32,1>,  32,32,1,  1};
+        if (s == 1 && fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1,  128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1,  128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,   128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24,  1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16,  1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,   1,128,16>, 1,128,16, 1};
+    }
+
+    // 2x upsampling.
+    if (p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 24,24, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 16,16, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2,   64,16,1>, 64,16,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 24,24, 32,32,1>, 32,32,1, 1};
+        if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 16,16, 32,32,1>, 32,32,1, 1};
+        if (s == 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8,   16,16,8>, 16,16,8, 1};
+        if (s == 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6,   16,16,8>, 16,16,8, 1};
+        if (s == 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4,   16,16,8>, 16,16,8, 1};
+        if (s == 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2,   16,16,8>, 16,16,8, 1};
+    }
+    if (p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
+    }
+
+    // 2x downsampling.
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 24,24, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 16,16, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,   32,8,1>,  32,8,1,  1};
+        if (s != 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,   32,8,1>,  32,8,1,  1};
+        if (s != 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,   32,8,1>,  32,8,1,  1};
+        if (s != 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,   32,8,1>,  32,8,1,  1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 24,24, 16,16,1>, 16,16,1, 1};
+        if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 16,16, 16,16,1>, 16,16,1, 1};
+        if (s == 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,   8,8,8>,   8,8,8,   1};
+        if (s == 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,   8,8,8>,   8,8,8,   1};
+        if (s == 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,   8,8,8>,   8,8,8,   1};
+        if (s == 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,   8,8,8>,   8,8,8,   1};
+    }
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,8,1>, 64,8,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,8,1>, 64,8,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,8,1>, 64,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,1,8>, 64,1,8, 1};
+        if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,1,8>, 64,1,8, 1};
+        if (s == 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,1,8>, 64,1,8, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  32,16,1>, 32,16,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 1,64,8>, 1,64,8, 1};
+        if (s == 1 && fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 1,64,8>, 1,64,8, 1};
+        if (s == 1 && fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  1,64,8>, 1,64,8, 1};
+    }
+
+    // 4x upsampling.
+    if (p.up.x == 4 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 48,48, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 32,32, 64,32,1>, 64,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 48,48, 32,32,1>, 32,32,1, 1};
+        if (s == 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 32,32, 32,32,1>, 32,32,1, 1};
+    }
+    if (p.up.x == 4 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 48,1, 128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 32,1, 128,8,1>, 128,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 48,1, 128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 32,1, 128,1,16>, 128,1,16, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,48, 32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,32, 32,32,1>, 32,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,48, 1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,32, 1,128,16>, 1,128,16, 1};
+    }
+
+    // 4x downsampling (inefficient).
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 4 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 48,1, 32,8,1>, 32,8,1, 1};
+        if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 32,1, 32,8,1>, 32,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 48,1, 32,1,8>, 32,1,8, 1};
+        if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 32,1, 32,1,8>, 32,1,8, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 4)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,48, 32,8,1>, 32,8,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,32, 32,8,1>, 32,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1  && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,48, 1,32,8>, 1,32,8, 1};
+        if (s == 1 && fx <= 1  && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,32, 1,32,8>, 1,32,8, 1};
+    }
+    return spec;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<double>   (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<float>    (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<c10::Half>(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.h b/torch_utils/ops/upfirdn2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5de893d6489921d4689ac1e2cdb45da9a253f18
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.h
@@ -0,0 +1,63 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+ *
+ * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+ * property and proprietary rights in and to this material, related
+ * documentation and any modifications thereto. Any use, reproduction,
+ * disclosure or distribution of this material and related documentation
+ * without an express license agreement from NVIDIA CORPORATION or
+ * its affiliates is strictly prohibited.
+ */
+
+#include <cuda_runtime.h>
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct upfirdn2d_kernel_params
+{
+    const void*     x;
+    const float*    f;
+    void*           y;
+
+    int2            up;
+    int2            down;
+    int2            pad0;
+    int             flip;
+    float           gain;
+
+    int4            inSize;         // [width, height, channel, batch]
+    int4            inStride;
+    int2            filterSize;     // [width, height]
+    int2            filterStride;
+    int4            outSize;        // [width, height, channel, batch]
+    int4            outStride;
+    int             sizeMinor;
+    int             sizeMajor;
+
+    int             loopMinor;
+    int             loopMajor;
+    int             loopX;
+    int             launchMinor;
+    int             launchMajor;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel specialization.
+
+struct upfirdn2d_kernel_spec
+{
+    void*   kernel;
+    int     tileOutW;
+    int     tileOutH;
+    int     loopMinor;
+    int     loopX;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..9784193ea4011820b5ec155ad8a04304a038ab7e
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.py
@@ -0,0 +1,473 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Custom PyTorch ops for efficient resampling of 2D images."""
+
+import os
+import numpy as np
+import torch
+
+from .. import custom_ops
+from .. import misc
+from . import conv2d_gradfix
+
+#----------------------------------------------------------------------------
+
+_plugin = None
+
+
+def _init():
+    global _plugin
+    if _plugin is None:
+        _plugin = custom_ops.get_plugin(
+            module_name='upfirdn2d_plugin',
+            sources=['upfirdn2d.cpp', 'upfirdn2d.cu'],
+            headers=['upfirdn2d.h'],
+            source_dir=os.path.dirname(__file__),
+            extra_cuda_cflags=['--use_fast_math'],
+        )
+    return True
+
+
+def _parse_scaling(scaling):
+    if isinstance(scaling, int):
+        scaling = [scaling, scaling]
+    assert isinstance(scaling, (list, tuple))
+    assert all(isinstance(x, int) for x in scaling)
+    sx, sy = scaling
+    assert sx >= 1 and sy >= 1
+    return sx, sy
+
+
+def _parse_padding(padding):
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    assert isinstance(padding, (list, tuple))
+    assert all(isinstance(x, int) for x in padding)
+    if len(padding) == 2:
+        padx, pady = padding
+        padding = [padx, padx, pady, pady]
+    padx0, padx1, pady0, pady1 = padding
+    return padx0, padx1, pady0, pady1
+
+
+def _get_filter_size(f):
+    if f is None:
+        return 1, 1
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    fw = f.shape[-1]
+    fh = f.shape[0]
+    with misc.suppress_tracer_warnings():
+        fw = int(fw)
+        fh = int(fh)
+    misc.assert_shape(f, [fh, fw][:f.ndim])
+    assert fw >= 1 and fh >= 1
+    return fw, fh
+
+
+#----------------------------------------------------------------------------
+
+
+def setup_filter(f,
+                 device=torch.device('cpu'),
+                 normalize=True,
+                 flip_filter=False,
+                 gain=1,
+                 separable=None):
+    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
+
+    Args:
+        f:           Torch tensor, numpy array, or python list of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable),
+                     `[]` (impulse), or
+                     `None` (identity).
+        device:      Result device (default: cpu).
+        normalize:   Normalize the filter so that it retains the magnitude
+                     for constant input signal (DC)? (default: True).
+        flip_filter: Flip the filter? (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        separable:   Return a separable filter? (default: select automatically).
+
+    Returns:
+        Float32 tensor of the shape
+        `[filter_height, filter_width]` (non-separable) or
+        `[filter_taps]` (separable).
+    """
+    # Validate.
+    if f is None:
+        f = 1
+    f = torch.as_tensor(f, dtype=torch.float32)
+    assert f.ndim in [0, 1, 2]
+    assert f.numel() > 0
+    if f.ndim == 0:
+        f = f[np.newaxis]
+
+    # Separable?
+    if separable is None:
+        separable = (f.ndim == 1 and f.numel() >= 8)
+    if f.ndim == 1 and not separable:
+        f = f.ger(f)
+    assert f.ndim == (1 if separable else 2)
+
+    # Apply normalize, flip, gain, and device.
+    if normalize:
+        f /= f.sum()
+    if flip_filter:
+        f = f.flip(list(range(f.ndim)))
+    f = f * (gain**(f.ndim / 2))
+    f = f.to(device=device)
+    return f
+
+
+#----------------------------------------------------------------------------
+
+
+def upfirdn2d(x,
+              f,
+              up=1,
+              down=1,
+              padding=0,
+              flip_filter=False,
+              gain=1,
+              impl='cuda'):
+    r"""Pad, upsample, filter, and downsample a batch of 2D images.
+
+    Performs the following sequence of operations for each channel:
+
+    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
+
+    2. Pad the image with the specified number of zeros on each side (`padding`).
+       Negative padding corresponds to cropping the image.
+
+    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
+       so that the footprint of all output pixels lies within the input image.
+
+    4. Downsample the image by keeping every Nth pixel (`down`).
+
+    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
+    The fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports gradients of arbitrary order.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the upsampled image. Can be a single number
+                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _upfirdn2d_cuda(up=up,
+                               down=down,
+                               padding=padding,
+                               flip_filter=flip_filter,
+                               gain=gain).apply(x, f)
+    return _upfirdn2d_ref(x,
+                          f,
+                          up=up,
+                          down=down,
+                          padding=padding,
+                          flip_filter=flip_filter,
+                          gain=gain)
+
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    if f is None:
+        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    assert f.dtype == torch.float32 and not f.requires_grad
+    batch_size, num_channels, in_height, in_width = x.shape
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Check that upsampled buffer is not smaller than the filter.
+    upW = in_width * upx + padx0 + padx1
+    upH = in_height * upy + pady0 + pady1
+    assert upW >= f.shape[-1] and upH >= f.shape[0]
+
+    # Upsample by inserting zeros.
+    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
+    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
+    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
+
+    # Pad or crop.
+    x = torch.nn.functional.pad(
+        x, [max(padx0, 0),
+            max(padx1, 0),
+            max(pady0, 0),
+            max(pady1, 0)])
+    x = x[:, :,
+          max(-pady0, 0):x.shape[2] - max(-pady1, 0),
+          max(-padx0, 0):x.shape[3] - max(-padx1, 0)]
+
+    # Setup filter.
+    f = f * (gain**(f.ndim / 2))
+    f = f.to(x.dtype)
+    if not flip_filter:
+        f = f.flip(list(range(f.ndim)))
+
+    # Convolve with the filter.
+    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
+    if f.ndim == 4:
+        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
+    else:
+        x = conv2d_gradfix.conv2d(input=x,
+                                  weight=f.unsqueeze(2),
+                                  groups=num_channels)
+        x = conv2d_gradfix.conv2d(input=x,
+                                  weight=f.unsqueeze(3),
+                                  groups=num_channels)
+
+    # Downsample by throwing away pixels.
+    x = x[:, :, ::downy, ::downx]
+    return x
+
+
+#----------------------------------------------------------------------------
+
+_upfirdn2d_cuda_cache = dict()
+
+
+def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
+    """
+    # Parse arguments.
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Lookup from cache.
+    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter,
+           gain)
+    if key in _upfirdn2d_cuda_cache:
+        return _upfirdn2d_cuda_cache[key]
+
+    # Forward op.
+    class Upfirdn2dCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, f):  # pylint: disable=arguments-differ
+            assert isinstance(x, torch.Tensor) and x.ndim == 4
+            if f is None:
+                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            if f.ndim == 1 and f.shape[0] == 1:
+                f = f.square().unsqueeze(
+                    0)  # Convert separable-1 into full-1x1.
+            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+            y = x
+            if f.ndim == 2:
+                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0,
+                                      padx1, pady0, pady1, flip_filter, gain)
+            else:
+                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1,
+                                      padx0, padx1, 0, 0, flip_filter, 1.0)
+                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0,
+                                      0, pady0, pady1, flip_filter, gain)
+            ctx.save_for_backward(f)
+            ctx.x_shape = x.shape
+            return y
+
+        @staticmethod
+        def backward(ctx, dy):  # pylint: disable=arguments-differ
+            f, = ctx.saved_tensors
+            _, _, ih, iw = ctx.x_shape
+            _, _, oh, ow = dy.shape
+            fw, fh = _get_filter_size(f)
+            p = [
+                fw - padx0 - 1,
+                iw * upx - ow * downx + padx0 - upx + 1,
+                fh - pady0 - 1,
+                ih * upy - oh * downy + pady0 - upy + 1,
+            ]
+            dx = None
+            df = None
+
+            if ctx.needs_input_grad[0]:
+                dx = _upfirdn2d_cuda(up=down,
+                                     down=up,
+                                     padding=p,
+                                     flip_filter=(not flip_filter),
+                                     gain=gain).apply(dy, f)
+
+            assert not ctx.needs_input_grad[1]
+            return dx, df
+
+    # Add to cache.
+    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
+    return Upfirdn2dCuda
+
+
+#----------------------------------------------------------------------------
+
+
+def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Filter a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape matches the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + fw // 2,
+        padx1 + (fw - 1) // 2,
+        pady0 + fh // 2,
+        pady1 + (fh - 1) // 2,
+    ]
+    return upfirdn2d(x,
+                     f,
+                     padding=p,
+                     flip_filter=flip_filter,
+                     gain=gain,
+                     impl=impl)
+
+
+#----------------------------------------------------------------------------
+
+
+def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Upsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a multiple of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    upx, upy = _parse_scaling(up)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw + upx - 1) // 2,
+        padx1 + (fw - upx) // 2,
+        pady0 + (fh + upy - 1) // 2,
+        pady1 + (fh - upy) // 2,
+    ]
+    return upfirdn2d(x,
+                     f,
+                     up=up,
+                     padding=p,
+                     flip_filter=flip_filter,
+                     gain=gain * upx * upy,
+                     impl=impl)
+
+
+#----------------------------------------------------------------------------
+
+
+def downsample2d(x,
+                 f,
+                 down=2,
+                 padding=0,
+                 flip_filter=False,
+                 gain=1,
+                 impl='cuda'):
+    r"""Downsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a fraction of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the input. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw - downx + 1) // 2,
+        padx1 + (fw - downx) // 2,
+        pady0 + (fh - downy + 1) // 2,
+        pady1 + (fh - downy) // 2,
+    ]
+    return upfirdn2d(x,
+                     f,
+                     down=down,
+                     padding=p,
+                     flip_filter=flip_filter,
+                     gain=gain,
+                     impl=impl)
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/persistence.py b/torch_utils/persistence.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1d2703956e6453ce360831e482d5b82ae50a913
--- /dev/null
+++ b/torch_utils/persistence.py
@@ -0,0 +1,272 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Facilities for pickling Python code alongside other data.
+
+The pickled code is automatically imported into a separate Python module
+during unpickling. This way, any previously exported pickles will remain
+usable even if the original code is no longer available, or if the current
+version of the code is not consistent with what was originally pickled."""
+
+import sys
+import pickle
+import io
+import inspect
+import copy
+import uuid
+import types
+import dnnlib
+
+#----------------------------------------------------------------------------
+
+_version = 6  # internal version number
+_decorators = set()  # {decorator_class, ...}
+_import_hooks = []  # [hook_function, ...]
+_module_to_src_dict = dict()  # {module: src, ...}
+_src_to_module_dict = dict()  # {src: module, ...}
+
+#----------------------------------------------------------------------------
+
+
+def persistent_class(orig_class):
+    r"""Class decorator that extends a given class to save its source code
+    when pickled.
+
+    Example:
+
+        from torch_utils import persistence
+
+        @persistence.persistent_class
+        class MyNetwork(torch.nn.Module):
+            def __init__(self, num_inputs, num_outputs):
+                super().__init__()
+                self.fc = MyLayer(num_inputs, num_outputs)
+                ...
+
+        @persistence.persistent_class
+        class MyLayer(torch.nn.Module):
+            ...
+
+    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
+    source code alongside other internal state (e.g., parameters, buffers,
+    and submodules). This way, any previously exported pickle will remain
+    usable even if the class definitions have been modified or are no
+    longer available.
+
+    The decorator saves the source code of the entire Python module
+    containing the decorated class. It does *not* save the source code of
+    any imported modules. Thus, the imported modules must be available
+    during unpickling, also including `torch_utils.persistence` itself.
+
+    It is ok to call functions defined in the same module from the
+    decorated class. However, if the decorated class depends on other
+    classes defined in the same module, they must be decorated as well.
+    This is illustrated in the above example in the case of `MyLayer`.
+
+    It is also possible to employ the decorator just-in-time before
+    calling the constructor. For example:
+
+        cls = MyLayer
+        if want_to_make_it_persistent:
+            cls = persistence.persistent_class(cls)
+        layer = cls(num_inputs, num_outputs)
+
+    As an additional feature, the decorator also keeps track of the
+    arguments that were used to construct each instance of the decorated
+    class. The arguments can be queried via `obj.init_args` and
+    `obj.init_kwargs`, and they are automatically pickled alongside other
+    object state. A typical use case is to first unpickle a previous
+    instance of a persistent class, and then upgrade it to use the latest
+    version of the source code:
+
+        with open('old_pickle.pkl', 'rb') as f:
+            old_net = pickle.load(f)
+        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
+        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
+    """
+    assert isinstance(orig_class, type)
+    if is_persistent(orig_class):
+        return orig_class
+
+    assert orig_class.__module__ in sys.modules
+    orig_module = sys.modules[orig_class.__module__]
+    orig_module_src = _module_to_src(orig_module)
+
+    class Decorator(orig_class):
+        _orig_module_src = orig_module_src
+        _orig_class_name = orig_class.__name__
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            self._init_args = copy.deepcopy(args)
+            self._init_kwargs = copy.deepcopy(kwargs)
+            assert orig_class.__name__ in orig_module.__dict__
+            _check_pickleable(self.__reduce__())
+
+        @property
+        def init_args(self):
+            return copy.deepcopy(self._init_args)
+
+        @property
+        def init_kwargs(self):
+            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
+
+        def __reduce__(self):
+            fields = list(super().__reduce__())
+            fields += [None] * max(3 - len(fields), 0)
+            if fields[0] is not _reconstruct_persistent_obj:
+                meta = dict(type='class',
+                            version=_version,
+                            module_src=self._orig_module_src,
+                            class_name=self._orig_class_name,
+                            state=fields[2])
+                fields[0] = _reconstruct_persistent_obj  # reconstruct func
+                fields[1] = (meta, )  # reconstruct args
+                fields[2] = None  # state dict
+            return tuple(fields)
+
+    Decorator.__name__ = orig_class.__name__
+    _decorators.add(Decorator)
+    return Decorator
+
+
+#----------------------------------------------------------------------------
+
+
+def is_persistent(obj):
+    r"""Test whether the given object or class is persistent, i.e.,
+    whether it will save its source code when pickled.
+    """
+    try:
+        if obj in _decorators:
+            return True
+    except TypeError:
+        pass
+    return type(obj) in _decorators  # pylint: disable=unidiomatic-typecheck
+
+
+#----------------------------------------------------------------------------
+
+
+def import_hook(hook):
+    r"""Register an import hook that is called whenever a persistent object
+    is being unpickled. A typical use case is to patch the pickled source
+    code to avoid errors and inconsistencies when the API of some imported
+    module has changed.
+
+    The hook should have the following signature:
+
+        hook(meta) -> modified meta
+
+    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
+
+        type:       Type of the persistent object, e.g. `'class'`.
+        version:    Internal version number of `torch_utils.persistence`.
+        module_src  Original source code of the Python module.
+        class_name: Class name in the original Python module.
+        state:      Internal state of the object.
+
+    Example:
+
+        @persistence.import_hook
+        def wreck_my_network(meta):
+            if meta.class_name == 'MyNetwork':
+                print('MyNetwork is being imported. I will wreck it!')
+                meta.module_src = meta.module_src.replace("True", "False")
+            return meta
+    """
+    assert callable(hook)
+    _import_hooks.append(hook)
+
+
+#----------------------------------------------------------------------------
+
+
+def _reconstruct_persistent_obj(meta):
+    r"""Hook that is called internally by the `pickle` module to unpickle
+    a persistent object.
+    """
+    meta = dnnlib.EasyDict(meta)
+    meta.state = dnnlib.EasyDict(meta.state)
+    for hook in _import_hooks:
+        meta = hook(meta)
+        assert meta is not None
+
+    assert meta.version == _version
+    module = _src_to_module(meta.module_src)
+
+    assert meta.type == 'class'
+    orig_class = module.__dict__[meta.class_name]
+    decorator_class = persistent_class(orig_class)
+    obj = decorator_class.__new__(decorator_class)
+
+    setstate = getattr(obj, '__setstate__', None)
+    if callable(setstate):
+        setstate(meta.state)  # pylint: disable=not-callable
+    else:
+        obj.__dict__.update(meta.state)
+    return obj
+
+
+#----------------------------------------------------------------------------
+
+
+def _module_to_src(module):
+    r"""Query the source code of a given Python module.
+    """
+    src = _module_to_src_dict.get(module, None)
+    if src is None:
+        src = inspect.getsource(module)
+        _module_to_src_dict[module] = src
+        _src_to_module_dict[src] = module
+    return src
+
+
+def _src_to_module(src):
+    r"""Get or create a Python module for the given source code.
+    """
+    module = _src_to_module_dict.get(src, None)
+    if module is None:
+        module_name = "_imported_module_" + uuid.uuid4().hex
+        module = types.ModuleType(module_name)
+        sys.modules[module_name] = module
+        _module_to_src_dict[module] = src
+        _src_to_module_dict[src] = module
+        exec(src, module.__dict__)  # pylint: disable=exec-used
+    return module
+
+
+#----------------------------------------------------------------------------
+
+
+def _check_pickleable(obj):
+    r"""Check that the given object is pickleable, raising an exception if
+    it is not. This function is expected to be considerably more efficient
+    than actually pickling the object.
+    """
+    def recurse(obj):
+        if isinstance(obj, (list, tuple, set)):
+            return [recurse(x) for x in obj]
+        if isinstance(obj, dict):
+            return [[recurse(x), recurse(y)] for x, y in obj.items()]
+        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
+            return None  # Python primitive types are pickleable.
+        if f'{type(obj).__module__}.{type(obj).__name__}' in [
+                'numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter'
+        ]:
+            return None  # NumPy arrays and PyTorch tensors are pickleable.
+        if is_persistent(obj):
+            return None  # Persistent objects are pickleable, by virtue of the constructor check.
+        return obj
+
+    with io.BytesIO() as f:
+        pickle.dump(recurse(obj), f)
+
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/training_stats.py b/torch_utils/training_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..28be66082bdb39d07e1383ede5a8f71c4222a47d
--- /dev/null
+++ b/torch_utils/training_stats.py
@@ -0,0 +1,288 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+"""Facilities for reporting and collecting training statistics across
+multiple processes and devices. The interface is designed to minimize
+synchronization overhead as well as the amount of boilerplate in user
+code."""
+
+import re
+import numpy as np
+import torch
+import dnnlib
+
+from . import misc
+
+#----------------------------------------------------------------------------
+
+_num_moments = 3  # [num_scalars, sum_of_scalars, sum_of_squares]
+_reduce_dtype = torch.float32  # Data type to use for initial per-tensor reduction.
+_counter_dtype = torch.float64  # Data type to use for the internal counters.
+_rank = 0  # Rank of the current process.
+_sync_device = None  # Device to use for multiprocess communication. None = single-process.
+_sync_called = False  # Has _sync() been called yet?
+_counters = dict(
+)  # Running counters on each device, updated by report(): name => device => torch.Tensor
+_cumulative = dict(
+)  # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
+
+#----------------------------------------------------------------------------
+
+
+def init_multiprocessing(rank, sync_device):
+    r"""Initializes `torch_utils.training_stats` for collecting statistics
+    across multiple processes.
+
+    This function must be called after
+    `torch.distributed.init_process_group()` and before `Collector.update()`.
+    The call is not necessary if multi-process collection is not needed.
+
+    Args:
+        rank:           Rank of the current process.
+        sync_device:    PyTorch device to use for inter-process
+                        communication, or None to disable multi-process
+                        collection. Typically `torch.device('cuda', rank)`.
+    """
+    global _rank, _sync_device
+    assert not _sync_called
+    _rank = rank
+    _sync_device = sync_device
+
+
+#----------------------------------------------------------------------------
+
+
+@misc.profiled_function
+def report(name, value):
+    r"""Broadcasts the given set of scalars to all interested instances of
+    `Collector`, across device and process boundaries.
+
+    This function is expected to be extremely cheap and can be safely
+    called from anywhere in the training loop, loss function, or inside a
+    `torch.nn.Module`.
+
+    Warning: The current implementation expects the set of unique names to
+    be consistent across processes. Please make sure that `report()` is
+    called at least once for each unique name by each process, and in the
+    same order. If a given process has no scalars to broadcast, it can do
+    `report(name, [])` (empty list).
+
+    Args:
+        name:   Arbitrary string specifying the name of the statistic.
+                Averages are accumulated separately for each unique name.
+        value:  Arbitrary set of scalars. Can be a list, tuple,
+                NumPy array, PyTorch tensor, or Python scalar.
+
+    Returns:
+        The same `value` that was passed in.
+    """
+    if name not in _counters:
+        _counters[name] = dict()
+
+    elems = torch.as_tensor(value)
+    if elems.numel() == 0:
+        return value
+
+    elems = elems.detach().flatten().to(_reduce_dtype)
+    moments = torch.stack([
+        torch.ones_like(elems).sum(),
+        elems.sum(),
+        elems.square().sum(),
+    ])
+    assert moments.ndim == 1 and moments.shape[0] == _num_moments
+    moments = moments.to(_counter_dtype)
+
+    device = moments.device
+    if device not in _counters[name]:
+        _counters[name][device] = torch.zeros_like(moments)
+    _counters[name][device].add_(moments)
+    return value
+
+
+#----------------------------------------------------------------------------
+
+
+def report0(name, value):
+    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
+    but ignores any scalars provided by the other processes.
+    See `report()` for further details.
+    """
+    report(name, value if _rank == 0 else [])
+    return value
+
+
+#----------------------------------------------------------------------------
+
+
+class Collector:
+    r"""Collects the scalars broadcasted by `report()` and `report0()` and
+    computes their long-term averages (mean and standard deviation) over
+    user-defined periods of time.
+
+    The averages are first collected into internal counters that are not
+    directly visible to the user. They are then copied to the user-visible
+    state as a result of calling `update()` and can then be queried using
+    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
+    internal counters for the next round, so that the user-visible state
+    effectively reflects averages collected between the last two calls to
+    `update()`.
+
+    Args:
+        regex:          Regular expression defining which statistics to
+                        collect. The default is to collect everything.
+        keep_previous:  Whether to retain the previous averages if no
+                        scalars were collected on a given round
+                        (default: True).
+    """
+    def __init__(self, regex='.*', keep_previous=True):
+        self._regex = re.compile(regex)
+        self._keep_previous = keep_previous
+        self._cumulative = dict()
+        self._moments = dict()
+        self.update()
+        self._moments.clear()
+
+    def names(self):
+        r"""Returns the names of all statistics broadcasted so far that
+        match the regular expression specified at construction time.
+        """
+        return [name for name in _counters if self._regex.fullmatch(name)]
+
+    def update(self):
+        r"""Copies current values of the internal counters to the
+        user-visible state and resets them for the next round.
+
+        If `keep_previous=True` was specified at construction time, the
+        operation is skipped for statistics that have received no scalars
+        since the last update, retaining their previous averages.
+
+        This method performs a number of GPU-to-CPU transfers and one
+        `torch.distributed.all_reduce()`. It is intended to be called
+        periodically in the main training loop, typically once every
+        N training steps.
+        """
+        if not self._keep_previous:
+            self._moments.clear()
+        for name, cumulative in _sync(self.names()):
+            if name not in self._cumulative:
+                self._cumulative[name] = torch.zeros([_num_moments],
+                                                     dtype=_counter_dtype)
+            delta = cumulative - self._cumulative[name]
+            self._cumulative[name].copy_(cumulative)
+            if float(delta[0]) != 0:
+                self._moments[name] = delta
+
+    def _get_delta(self, name):
+        r"""Returns the raw moments that were accumulated for the given
+        statistic between the last two calls to `update()`, or zero if
+        no scalars were collected.
+        """
+        assert self._regex.fullmatch(name)
+        if name not in self._moments:
+            self._moments[name] = torch.zeros([_num_moments],
+                                              dtype=_counter_dtype)
+        return self._moments[name]
+
+    def num(self, name):
+        r"""Returns the number of scalars that were accumulated for the given
+        statistic between the last two calls to `update()`, or zero if
+        no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        return int(delta[0])
+
+    def mean(self, name):
+        r"""Returns the mean of the scalars that were accumulated for the
+        given statistic between the last two calls to `update()`, or NaN if
+        no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        if int(delta[0]) == 0:
+            return float('nan')
+        return float(delta[1] / delta[0])
+
+    def std(self, name):
+        r"""Returns the standard deviation of the scalars that were
+        accumulated for the given statistic between the last two calls to
+        `update()`, or NaN if no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
+            return float('nan')
+        if int(delta[0]) == 1:
+            return float(0)
+        mean = float(delta[1] / delta[0])
+        raw_var = float(delta[2] / delta[0])
+        return np.sqrt(max(raw_var - np.square(mean), 0))
+
+    def as_dict(self):
+        r"""Returns the averages accumulated between the last two calls to
+        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
+
+            dnnlib.EasyDict(
+                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
+                ...
+            )
+        """
+        stats = dnnlib.EasyDict()
+        for name in self.names():
+            stats[name] = dnnlib.EasyDict(num=self.num(name),
+                                          mean=self.mean(name),
+                                          std=self.std(name))
+        return stats
+
+    def __getitem__(self, name):
+        r"""Convenience getter.
+        `collector[name]` is a synonym for `collector.mean(name)`.
+        """
+        return self.mean(name)
+
+
+#----------------------------------------------------------------------------
+
+
+def _sync(names):
+    r"""Synchronize the global cumulative counters across devices and
+    processes. Called internally by `Collector.update()`.
+    """
+    if len(names) == 0:
+        return []
+    global _sync_called
+    _sync_called = True
+
+    # Collect deltas within current rank.
+    deltas = []
+    device = _sync_device if _sync_device is not None else torch.device('cpu')
+    for name in names:
+        delta = torch.zeros([_num_moments],
+                            dtype=_counter_dtype,
+                            device=device)
+        for counter in _counters[name].values():
+            delta.add_(counter.to(device))
+            counter.copy_(torch.zeros_like(counter))
+        deltas.append(delta)
+    deltas = torch.stack(deltas)
+
+    # Sum deltas across ranks.
+    if _sync_device is not None:
+        torch.distributed.all_reduce(deltas)
+
+    # Update cumulative values.
+    deltas = deltas.cpu()
+    for idx, name in enumerate(names):
+        if name not in _cumulative:
+            _cumulative[name] = torch.zeros([_num_moments],
+                                            dtype=_counter_dtype)
+        _cumulative[name].add_(deltas[idx])
+
+    # Return name-value pairs.
+    return [(name, _cumulative[name]) for name in names]
+
+
+#----------------------------------------------------------------------------
diff --git a/transport/__init__.py b/transport/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c63e3a6cce9b608e6d224a0a0b3a279e160520f5
--- /dev/null
+++ b/transport/__init__.py
@@ -0,0 +1,73 @@
+from pdb import set_trace as st
+from .transport import Transport, ModelType, WeightType, PathType, Sampler, SNRType
+
+def create_transport(
+    path_type='Linear',
+    prediction="velocity",
+    loss_weight=None,
+    train_eps=None,
+    sample_eps=None,
+    snr_type='uniform',
+):
+    """function for creating Transport object
+    **Note**: model prediction defaults to velocity
+    Args:
+    - path_type: type of path to use; default to linear
+    - learn_score: set model prediction to score
+    - learn_noise: set model prediction to noise
+    - velocity_weighted: weight loss by velocity weight
+    - likelihood_weighted: weight loss by likelihood weight
+    - train_eps: small epsilon for avoiding instability during training
+    - sample_eps: small epsilon for avoiding instability during sampling
+    """
+
+    if prediction == "noise":
+        model_type = ModelType.NOISE
+    elif prediction == "score":
+        model_type = ModelType.SCORE
+    else:
+        model_type = ModelType.VELOCITY
+
+    if loss_weight == "velocity":
+        loss_type = WeightType.VELOCITY
+    elif loss_weight == "likelihood":
+        loss_type = WeightType.LIKELIHOOD
+    else:
+        loss_type = WeightType.NONE
+    
+    if snr_type == "lognorm":
+        snr_type = SNRType.LOGNORM
+    elif snr_type == "uniform":
+        snr_type = SNRType.UNIFORM
+    else:
+        raise ValueError(f"Invalid snr type {snr_type}")
+
+    path_choice = {
+        "Linear": PathType.LINEAR,
+        "GVP": PathType.GVP,
+        "VP": PathType.VP,
+    }
+
+    path_type = path_choice[path_type]
+
+    if (path_type in [PathType.VP]):
+        train_eps = 1e-5 if train_eps is None else train_eps
+        sample_eps = 1e-3 if sample_eps is None else sample_eps
+    elif (path_type in [PathType.GVP, PathType.LINEAR] and model_type != ModelType.VELOCITY):
+        train_eps = 1e-3 if train_eps is None else train_eps
+        sample_eps = 1e-3 if sample_eps is None else sample_eps
+    else: # velocity & [GVP, LINEAR] is stable everywhere
+        train_eps = 0
+        sample_eps = 0
+    
+    # create flow state
+    state = Transport(
+        model_type=model_type,
+        path_type=path_type,
+        loss_type=loss_type,
+        train_eps=train_eps,
+        sample_eps=sample_eps,
+        snr_type=snr_type,
+    )
+
+    return state
\ No newline at end of file
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diff --git a/transport/integrators.py b/transport/integrators.py
new file mode 100644
index 0000000000000000000000000000000000000000..64fe4071eea6919b28bf434db551b7255124ef3a
--- /dev/null
+++ b/transport/integrators.py
@@ -0,0 +1,125 @@
+import numpy as np
+import torch as th
+import torch.nn as nn
+from torchdiffeq import odeint
+from functools import partial
+from tqdm import tqdm
+
+class sde:
+    """SDE solver class"""
+    def __init__(
+        self, 
+        drift,
+        diffusion,
+        *,
+        t0,
+        t1,
+        num_steps,
+        sampler_type,
+    ):
+        assert t0 < t1, "SDE sampler has to be in forward time"
+
+        self.num_timesteps = num_steps
+        self.t = th.linspace(t0, t1, num_steps)
+        self.dt = self.t[1] - self.t[0]
+        self.drift = drift
+        self.diffusion = diffusion
+        self.sampler_type = sampler_type
+
+    def __Euler_Maruyama_step(self, x, mean_x, t, model, **model_kwargs):
+        w_cur = th.randn(x.size()).to(x)
+        t = th.ones(x.size(0)).to(x) * t
+        dw = w_cur * th.sqrt(self.dt)
+        drift = self.drift(x, t, model, **model_kwargs)
+        diffusion = self.diffusion(x, t)
+        mean_x = x + drift * self.dt
+        x = mean_x + th.sqrt(2 * diffusion) * dw
+        return x, mean_x
+    
+    def __Heun_step(self, x, _, t, model, **model_kwargs):
+        w_cur = th.randn(x.size()).to(x)
+        dw = w_cur * th.sqrt(self.dt)
+        t_cur = th.ones(x.size(0)).to(x) * t
+        diffusion = self.diffusion(x, t_cur)
+        xhat = x + th.sqrt(2 * diffusion) * dw
+        K1 = self.drift(xhat, t_cur, model, **model_kwargs)
+        xp = xhat + self.dt * K1
+        K2 = self.drift(xp, t_cur + self.dt, model, **model_kwargs)
+        return xhat + 0.5 * self.dt * (K1 + K2), xhat # at last time point we do not perform the heun step
+
+    def __forward_fn(self):
+        """TODO: generalize here by adding all private functions ending with steps to it"""
+        sampler_dict = {
+            "Euler": self.__Euler_Maruyama_step,
+            "Heun": self.__Heun_step,
+        }
+
+        try:
+            sampler = sampler_dict[self.sampler_type]
+        except:
+            raise NotImplementedError("Smapler type not implemented.")
+    
+        return sampler
+
+    def sample(self, init, model, **model_kwargs):
+        """forward loop of sde"""
+        x = init
+        mean_x = init 
+        samples = []
+        sampler = self.__forward_fn()
+        for ti in self.t[:-1]:
+            with th.no_grad():
+                x, mean_x = sampler(x, mean_x, ti, model, **model_kwargs)
+                samples.append(x)
+
+        return samples
+
+class ode:
+    """ODE solver class"""
+    def __init__(
+        self,
+        drift,
+        *,
+        t0,
+        t1,
+        sampler_type,
+        num_steps,
+        atol,
+        rtol,
+        # guider,
+    ):
+        assert t0 < t1, "ODE sampler has to be in forward time"
+
+        self.drift = drift
+        self.t = th.linspace(t0, t1, num_steps)
+        self.atol = atol
+        self.rtol = rtol
+        self.sampler_type = sampler_type
+        # self.guider = guider
+
+    def sample(self, x, model, **model_kwargs):
+        
+        device = x[0].device if isinstance(x, tuple) else x.device
+        def _fn(t, x):
+            t = th.ones(x[0].size(0)).to(device) * t if isinstance(x, tuple) else th.ones(x.size(0)).to(device) * t
+            model_output = self.drift(x, t, model, **model_kwargs)
+            return model_output
+
+        t = self.t.to(device)
+        atol = [self.atol] * len(x) if isinstance(x, tuple) else [self.atol]
+        rtol = [self.rtol] * len(x) if isinstance(x, tuple) else [self.rtol]
+        samples = odeint(
+            _fn,
+            x,
+            t,
+            method=self.sampler_type,
+            atol=atol,
+            rtol=rtol
+        )
+        return samples
+
+    # def sample_cfg(self, x, model, **model_kwargs):
+    # def sample_cfg(self, x, model, cond, uc):
+    #     denoised = self.sample(*self.guider.prepare_inputs(x, cond, uc))
+    #     denoised = self.guider(denoised, sigma)
+    #     return denoised
\ No newline at end of file
diff --git a/transport/path.py b/transport/path.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d57efa4121deba04921ec413bac74f0c777780a
--- /dev/null
+++ b/transport/path.py
@@ -0,0 +1,215 @@
+import torch as th
+import numpy as np
+from functools import partial
+
+def expand_t_like_x(t, x):
+    """Function to reshape time t to broadcastable dimension of x
+    Args:
+      t: [batch_dim,], time vector
+      x: [batch_dim,...], data point
+    """
+    dims = [1] * (len(x.size()) - 1)
+    t = t.view(t.size(0), *dims)
+    return t
+
+
+#################### Coupling Plans ####################
+
+class ICPlan:
+    """Linear Coupling Plan"""
+    def __init__(self, sigma=0.0):
+        self.sigma = sigma
+
+    def compute_alpha_t(self, t):
+        """Compute the data coefficient along the path"""
+        return t, 1
+    
+    def compute_sigma_t(self, t):
+        """Compute the noise coefficient along the path"""
+        return 1 - t, -1
+    
+    def compute_d_alpha_alpha_ratio_t(self, t):
+        """Compute the ratio between d_alpha and alpha"""
+        return 1 / t
+
+    def compute_drift(self, x, t):
+        """We always output sde according to score parametrization; """
+        t = expand_t_like_x(t, x)
+        alpha_ratio = self.compute_d_alpha_alpha_ratio_t(t)
+        sigma_t, d_sigma_t = self.compute_sigma_t(t)
+        drift = alpha_ratio * x
+        diffusion = alpha_ratio * (sigma_t ** 2) - sigma_t * d_sigma_t
+
+        return -drift, diffusion
+
+    def compute_diffusion(self, x, t, form="constant", norm=1.0):
+        """Compute the diffusion term of the SDE
+        Args:
+          x: [batch_dim, ...], data point
+          t: [batch_dim,], time vector
+          form: str, form of the diffusion term
+          norm: float, norm of the diffusion term
+        """
+        t = expand_t_like_x(t, x)
+        choices = {
+            "constant": norm,
+            "SBDM": norm * self.compute_drift(x, t)[1],
+            "sigma": norm * self.compute_sigma_t(t)[0],
+            "linear": norm * (1 - t),
+            "decreasing": 0.25 * (norm * th.cos(np.pi * t) + 1) ** 2,
+            "inccreasing-decreasing": norm * th.sin(np.pi * t) ** 2,
+        }
+
+        try:
+            diffusion = choices[form]
+        except KeyError:
+            raise NotImplementedError(f"Diffusion form {form} not implemented")
+        
+        return diffusion
+
+    def get_score_from_velocity(self, velocity, x, t):
+        """Wrapper function: transfrom velocity prediction model to score
+        Args:
+            velocity: [batch_dim, ...] shaped tensor; velocity model output
+            x: [batch_dim, ...] shaped tensor; x_t data point
+            t: [batch_dim,] time tensor
+        """
+        t = expand_t_like_x(t, x)
+        alpha_t, d_alpha_t = self.compute_alpha_t(t)
+        sigma_t, d_sigma_t = self.compute_sigma_t(t)
+        mean = x
+        reverse_alpha_ratio = alpha_t / d_alpha_t
+        var = sigma_t**2 - reverse_alpha_ratio * d_sigma_t * sigma_t
+        score = (reverse_alpha_ratio * velocity - mean) / var
+        return score
+    
+    def get_noise_from_velocity(self, velocity, x, t):
+        """Wrapper function: transfrom velocity prediction model to denoiser
+        Args:
+            velocity: [batch_dim, ...] shaped tensor; velocity model output
+            x: [batch_dim, ...] shaped tensor; x_t data point
+            t: [batch_dim,] time tensor
+        """
+        t = expand_t_like_x(t, x)
+        alpha_t, d_alpha_t = self.compute_alpha_t(t)
+        sigma_t, d_sigma_t = self.compute_sigma_t(t)
+        mean = x
+        reverse_alpha_ratio = alpha_t / d_alpha_t
+        var = reverse_alpha_ratio * d_sigma_t - sigma_t
+        noise = (reverse_alpha_ratio * velocity - mean) / var
+        return noise
+
+    def get_velocity_from_score(self, score, x, t):
+        """Wrapper function: transfrom score prediction model to velocity
+        Args:
+            score: [batch_dim, ...] shaped tensor; score model output
+            x: [batch_dim, ...] shaped tensor; x_t data point
+            t: [batch_dim,] time tensor
+        """
+        t = expand_t_like_x(t, x)
+        drift, var = self.compute_drift(x, t)
+        velocity = var * score - drift
+        return velocity
+
+    def get_xstart_from_velocity(self, velocity, x_t, t, return_snr=False):
+        """Wrapper function: transfrom velocity prediction model to score
+        Args:
+            velocity: [batch_dim, ...] shaped tensor; velocity model output
+            x_t: [batch_dim, ...] shaped tensor; x_t data point
+            t: [batch_dim,] time tensor
+        """
+        t = expand_t_like_x(t, x_t)
+        alpha_t, _ = self.compute_alpha_t(t)
+        sigma_t, _ = self.compute_sigma_t(t)
+        # mean = x_t
+
+        x_start = (sigma_t * velocity + x_t) / (alpha_t + sigma_t)
+
+        # reverse_alpha_ratio = alpha_t / d_alpha_t
+        # var = sigma_t**2 - reverse_alpha_ratio * d_sigma_t * sigma_t
+        # score = (reverse_alpha_ratio * velocity - mean) / var
+        if return_snr:
+            snr = (alpha_t / sigma_t) ** 2
+            return x_start, snr
+        else:
+            return x_start
+
+    def compute_mu_t(self, t, x0, x1):
+        """Compute the mean of time-dependent density p_t"""
+        t = expand_t_like_x(t, x1)
+        alpha_t, _ = self.compute_alpha_t(t)
+        sigma_t, _ = self.compute_sigma_t(t)
+        return alpha_t * x1 + sigma_t * x0
+    
+    def compute_xt(self, t, x0, x1):
+        """Sample xt from time-dependent density p_t; rng is required"""
+        xt = self.compute_mu_t(t, x0, x1)
+        return xt
+    
+    def compute_ut(self, t, x0, x1, xt):
+        """Compute the vector field corresponding to p_t"""
+        t = expand_t_like_x(t, x1)
+        _, d_alpha_t = self.compute_alpha_t(t)
+        _, d_sigma_t = self.compute_sigma_t(t)
+        return d_alpha_t * x1 + d_sigma_t * x0
+    
+    def plan(self, t, x0, x1):
+        xt = self.compute_xt(t, x0, x1)
+        ut = self.compute_ut(t, x0, x1, xt)
+        return t, xt, ut
+    
+
+class VPCPlan(ICPlan):
+    """class for VP path flow matching"""
+
+    def __init__(self, sigma_min=0.1, sigma_max=20.0):
+        self.sigma_min = sigma_min
+        self.sigma_max = sigma_max
+        self.log_mean_coeff = lambda t: -0.25 * ((1 - t) ** 2) * (self.sigma_max - self.sigma_min) - 0.5 * (1 - t) * self.sigma_min 
+        self.d_log_mean_coeff = lambda t: 0.5 * (1 - t) * (self.sigma_max - self.sigma_min) + 0.5 * self.sigma_min
+
+
+    def compute_alpha_t(self, t):
+        """Compute coefficient of x1"""
+        alpha_t = self.log_mean_coeff(t)
+        alpha_t = th.exp(alpha_t)
+        d_alpha_t = alpha_t * self.d_log_mean_coeff(t)
+        return alpha_t, d_alpha_t
+    
+    def compute_sigma_t(self, t):
+        """Compute coefficient of x0"""
+        p_sigma_t = 2 * self.log_mean_coeff(t)
+        sigma_t = th.sqrt(1 - th.exp(p_sigma_t))
+        d_sigma_t = th.exp(p_sigma_t) * (2 * self.d_log_mean_coeff(t)) / (-2 * sigma_t)
+        return sigma_t, d_sigma_t
+    
+    def compute_d_alpha_alpha_ratio_t(self, t):
+        """Special purposed function for computing numerical stabled d_alpha_t / alpha_t"""
+        return self.d_log_mean_coeff(t)
+
+    def compute_drift(self, x, t):
+        """Compute the drift term of the SDE"""
+        t = expand_t_like_x(t, x)
+        beta_t = self.sigma_min + (1 - t) * (self.sigma_max - self.sigma_min)
+        return -0.5 * beta_t * x, beta_t / 2
+    
+
+class GVPCPlan(ICPlan):
+    def __init__(self, sigma=0.0):
+        super().__init__(sigma)
+    
+    def compute_alpha_t(self, t):
+        """Compute coefficient of x1"""
+        alpha_t = th.sin(t * np.pi / 2)
+        d_alpha_t = np.pi / 2 * th.cos(t * np.pi / 2)
+        return alpha_t, d_alpha_t
+    
+    def compute_sigma_t(self, t):
+        """Compute coefficient of x0"""
+        sigma_t = th.cos(t * np.pi / 2)
+        d_sigma_t = -np.pi / 2 * th.sin(t * np.pi / 2)
+        return sigma_t, d_sigma_t
+    
+    def compute_d_alpha_alpha_ratio_t(self, t):
+        """Special purposed function for computing numerical stabled d_alpha_t / alpha_t"""
+        return np.pi / (2 * th.tan(t * np.pi / 2))
diff --git a/transport/train_utils.py b/transport/train_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b646e63a1ac5bc291b7ca98a7bd7bb2a34fea36
--- /dev/null
+++ b/transport/train_utils.py
@@ -0,0 +1,34 @@
+def none_or_str(value):
+    if value == 'None':
+        return None
+    return value
+
+def parse_transport_args(parser):
+    group = parser.add_argument_group("Transport arguments")
+    group.add_argument("--path-type", type=str, default="Linear", choices=["Linear", "GVP", "VP"])
+    group.add_argument("--prediction", type=str, default="velocity", choices=["velocity", "score", "noise"])
+    group.add_argument("--loss-weight", type=none_or_str, default=None, choices=[None, "velocity", "likelihood"])
+    group.add_argument("--sample-eps", type=float)
+    group.add_argument("--train-eps", type=float)
+    # group.add_argument("--snr-type", type=str, default="uniform", choices=["uniform", "lognorm", "lognorm-mv", "lognorm-mv-plucker", "lognorm-highres"])
+    group.add_argument("--snr-type", type=str, default="uniform")
+
+def parse_ode_args(parser):
+    group = parser.add_argument_group("ODE arguments")
+    group.add_argument("--sampling-method", type=str, default="dopri5", help="blackbox ODE solver methods; for full list check https://github.com/rtqichen/torchdiffeq")
+    group.add_argument("--atol", type=float, default=1e-6, help="Absolute tolerance")
+    group.add_argument("--rtol", type=float, default=1e-3, help="Relative tolerance")
+    group.add_argument("--reverse", action="store_true")
+    group.add_argument("--likelihood", action="store_true")
+
+def parse_sde_args(parser):
+    group = parser.add_argument_group("SDE arguments")
+    group.add_argument("--sampling-method", type=str, default="Euler", choices=["Euler", "Heun"])
+    group.add_argument("--diffusion-form", type=str, default="sigma", \
+                        choices=["constant", "SBDM", "sigma", "linear", "decreasing", "increasing-decreasing"],\
+                        help="form of diffusion coefficient in the SDE")
+    group.add_argument("--diffusion-norm", type=float, default=1.0)
+    group.add_argument("--last-step", type=none_or_str, default="Mean", choices=[None, "Mean", "Tweedie", "Euler"],\
+                        help="form of last step taken in the SDE")
+    group.add_argument("--last-step-size", type=float, default=0.04, \
+                        help="size of the last step taken")
\ No newline at end of file
diff --git a/transport/transport.py b/transport/transport.py
new file mode 100644
index 0000000000000000000000000000000000000000..f65e594478533e64d04470807f3ed1dfb20cc7ce
--- /dev/null
+++ b/transport/transport.py
@@ -0,0 +1,495 @@
+import torch as th
+import numpy as np
+import logging
+
+import enum
+
+from . import path
+from .utils import EasyDict, log_state, mean_flat
+from .integrators import ode, sde
+from pdb import set_trace as st
+
+
+from sgm.util import instantiate_from_config
+
+class ModelType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    NOISE = enum.auto()  # the model predicts epsilon
+    SCORE = enum.auto()  # the model predicts \nabla \log p(x)
+    VELOCITY = enum.auto()  # the model predicts v(x)
+
+class PathType(enum.Enum):
+    """
+    Which type of path to use.
+    """
+
+    LINEAR = enum.auto()
+    GVP = enum.auto()
+    VP = enum.auto()
+
+class WeightType(enum.Enum):
+    """
+    Which type of weighting to use.
+    """
+
+    NONE = enum.auto()
+    VELOCITY = enum.auto()
+    LIKELIHOOD = enum.auto()
+
+class SNRType(enum.Enum):
+    UNIFORM = enum.auto()
+    LOGNORM = enum.auto()
+
+
+
+class Transport:
+
+    def __init__(
+        self,
+        *,
+        model_type,
+        path_type,
+        loss_type,
+        train_eps,
+        sample_eps,
+        snr_type,
+    ):
+        path_options = {
+            PathType.LINEAR: path.ICPlan,
+            PathType.GVP: path.GVPCPlan,
+            PathType.VP: path.VPCPlan,
+        }
+
+        self.loss_type = loss_type
+        self.model_type = model_type
+        self.path_sampler = path_options[path_type]()
+        self.train_eps = train_eps
+        self.sample_eps = sample_eps
+        self.snr_type = snr_type
+        # assert self.snr_type == SNRType.LOGNORM, 'use lognorm schedule plz.'
+
+    def prior_logp(self, z):
+        '''
+            Standard multivariate normal prior
+            Assume z is batched
+        '''
+        shape = th.tensor(z.size())
+        N = th.prod(shape[1:])
+        _fn = lambda x: -N / 2. * np.log(2 * np.pi) - th.sum(x ** 2) / 2.
+        return th.vmap(_fn)(z)
+    
+
+    def check_interval(
+        self, 
+        train_eps, 
+        sample_eps, 
+        *, 
+        diffusion_form="SBDM",
+        sde=False, 
+        reverse=False, 
+        eval=False,
+        last_step_size=0.0,
+    ):
+        t0 = 0
+        t1 = 1
+        eps = train_eps if not eval else sample_eps
+        if (type(self.path_sampler) in [path.VPCPlan]):
+
+            t1 = 1 - eps if (not sde or last_step_size == 0) else 1 - last_step_size
+
+        elif (type(self.path_sampler) in [path.ICPlan, path.GVPCPlan]) \
+            and (self.model_type != ModelType.VELOCITY or sde): # avoid numerical issue by taking a first semi-implicit step
+
+            t0 = eps if (diffusion_form == "SBDM" and sde) or self.model_type != ModelType.VELOCITY else 0
+            t1 = 1 - eps if (not sde or last_step_size == 0) else 1 - last_step_size
+        
+        if reverse:
+            t0, t1 = 1 - t0, 1 - t1
+
+        return t0, t1
+
+
+    # def sample(self, x1):
+    #     """Sampling x0 & t based on shape of x1 (if needed)
+    #       Args:
+    #         x1 - data point; [batch, *dim]
+    #     """
+        
+    #     x0 = th.randn_like(x1)
+    #     t0, t1 = self.check_interval(self.train_eps, self.sample_eps)
+    #     t = th.rand((x1.shape[0],)) * (t1 - t0) + t0
+    #     t = t.to(x1)
+    #     return t, x0, x1
+
+    def sample(self, x1):
+        """Sampling x0 & t based on shape of x1 (if needed)
+        Args:
+          x1 - data point; [batch, *dim]
+        """
+        if isinstance(x1, (list, tuple)):
+            x0 = [th.randn_like(img_start) for img_start in x1]
+        else:
+            x0 = th.randn_like(x1)
+        t0, t1 = self.check_interval(self.train_eps, self.sample_eps)
+
+        if self.snr_type == SNRType.UNIFORM:
+            t = th.rand((len(x1),)) * (t1 - t0) + t0
+        elif self.snr_type == SNRType.LOGNORM:
+            u = th.normal(mean=0.0, std=1.0, size=(len(x1),))
+            t = 1 / (1 + th.exp(-u)) * (t1 - t0) + t0
+        else:
+            raise ValueError(f"Unknown snr type: {self.snr_type}")
+        t = t.to(x1[0])
+        return t, x0, x1    
+
+    def training_losses(
+        self, 
+        model,  
+        x1, 
+        model_kwargs=None
+    ):
+        """Loss for training the score model
+        Args:
+        - model: backbone model; could be score, noise, or velocity
+        - x1: datapoint
+        - model_kwargs: additional arguments for the model
+        """
+        if model_kwargs == None:
+            model_kwargs = {}
+        
+        t, x0, x1 = self.sample(x1)
+        t, xt, ut = self.path_sampler.plan(t, x0, x1)
+        model_output = model(xt, t, **model_kwargs)
+        B, *_, C = xt.shape
+        assert model_output.size() == (B, *xt.size()[1:-1], C)
+
+        terms = {}
+        terms['pred'] = model_output
+        if self.model_type == ModelType.VELOCITY:
+            terms['loss'] = mean_flat(((model_output - ut) ** 2))
+            # ! for adding rendering loss during diffusion training
+            terms['pred_xstart'], snr_t = self.path_sampler.get_xstart_from_velocity(model_output, xt, t, return_snr=True)
+            terms['snr_t'] = snr_t # for RenderDiff weighting, sigmoid(snr_t) or min(snr_t, 1)
+            st()
+            pass
+        else: 
+            _, drift_var = self.path_sampler.compute_drift(xt, t)
+            sigma_t, _ = self.path_sampler.compute_sigma_t(path.expand_t_like_x(t, xt))
+            if self.loss_type in [WeightType.VELOCITY]:
+                weight = (drift_var / sigma_t) ** 2
+            elif self.loss_type in [WeightType.LIKELIHOOD]:
+                weight = drift_var / (sigma_t ** 2)
+            elif self.loss_type in [WeightType.NONE]:
+                weight = 1
+            else:
+                raise NotImplementedError()
+            
+            if self.model_type == ModelType.NOISE:
+                terms['loss'] = mean_flat(weight * ((model_output - x0) ** 2))
+            else:
+                terms['loss'] = mean_flat(weight * ((model_output * sigma_t + x0) ** 2))
+                
+        return terms
+    
+
+    def get_drift(
+        self
+    ):
+        """member function for obtaining the drift of the probability flow ODE"""
+        def score_ode(x, t, model, **model_kwargs):
+            drift_mean, drift_var = self.path_sampler.compute_drift(x, t)
+            model_output = model(x, t, **model_kwargs)
+            return (-drift_mean + drift_var * model_output) # by change of variable
+        
+        def noise_ode(x, t, model, **model_kwargs):
+            drift_mean, drift_var = self.path_sampler.compute_drift(x, t)
+            sigma_t, _ = self.path_sampler.compute_sigma_t(path.expand_t_like_x(t, x))
+            model_output = model(x, t, **model_kwargs)
+            score = model_output / -sigma_t
+            return (-drift_mean + drift_var * score)
+        
+        def velocity_ode(x, t, model, **model_kwargs):
+            model_output = model(x, t, **model_kwargs)
+            return model_output
+
+        if self.model_type == ModelType.NOISE:
+            drift_fn = noise_ode
+        elif self.model_type == ModelType.SCORE:
+            drift_fn = score_ode
+        else:
+            drift_fn = velocity_ode
+        
+        def body_fn(x, t, model, **model_kwargs):
+            model_output = drift_fn(x, t, model, **model_kwargs)
+            assert model_output.shape == x.shape, "Output shape from ODE solver must match input shape"
+            return model_output
+
+        return body_fn
+    
+
+    def get_score(
+        self,
+    ):
+        """member function for obtaining score of 
+            x_t = alpha_t * x + sigma_t * eps"""
+        if self.model_type == ModelType.NOISE:
+            score_fn = lambda x, t, model, **kwargs: model(x, t, **kwargs) / -self.path_sampler.compute_sigma_t(path.expand_t_like_x(t, x))[0]
+        elif self.model_type == ModelType.SCORE:
+            score_fn = lambda x, t, model, **kwagrs: model(x, t, **kwagrs)
+        elif self.model_type == ModelType.VELOCITY:
+            score_fn = lambda x, t, model, **kwargs: self.path_sampler.get_score_from_velocity(model(x, t, **kwargs), x, t)
+        else:
+            raise NotImplementedError()
+        
+        return score_fn
+
+
+DEFAULT_GUIDER = {"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"}
+class Sampler:
+    """Sampler class for the transport model"""
+    def __init__(
+        self,
+        transport,
+        guider_config=None,
+    ):
+        """Constructor for a general sampler; supporting different sampling methods
+        Args:
+        - transport: an tranport object specify model prediction & interpolant type
+        """
+        
+        self.transport = transport
+        self.drift = self.transport.get_drift()
+        self.score = self.transport.get_score()
+
+        # assert guider_config is not None
+        # self.guider = instantiate_from_config(
+        #     # default(
+        #     guider_config,
+        #         # DEFAULT_GUIDER,
+        #     # )
+        # )
+
+    
+    def __get_sde_diffusion_and_drift(
+        self,
+        *,
+        diffusion_form="SBDM",
+        diffusion_norm=1.0,
+    ):
+
+        def diffusion_fn(x, t):
+            diffusion = self.transport.path_sampler.compute_diffusion(x, t, form=diffusion_form, norm=diffusion_norm)
+            return diffusion
+        
+        sde_drift = \
+            lambda x, t, model, **kwargs: \
+                self.drift(x, t, model, **kwargs) + diffusion_fn(x, t) * self.score(x, t, model, **kwargs)
+    
+        sde_diffusion = diffusion_fn
+
+        return sde_drift, sde_diffusion
+    
+    def __get_last_step(
+        self,
+        sde_drift,
+        *,
+        last_step,
+        last_step_size,
+    ):
+        """Get the last step function of the SDE solver"""
+    
+        if last_step is None:
+            last_step_fn = \
+                lambda x, t, model, **model_kwargs: \
+                    x
+        elif last_step == "Mean":
+            last_step_fn = \
+                lambda x, t, model, **model_kwargs: \
+                    x + sde_drift(x, t, model, **model_kwargs) * last_step_size
+        elif last_step == "Tweedie":
+            alpha = self.transport.path_sampler.compute_alpha_t # simple aliasing; the original name was too long
+            sigma = self.transport.path_sampler.compute_sigma_t
+            last_step_fn = \
+                lambda x, t, model, **model_kwargs: \
+                    x / alpha(t)[0][0] + (sigma(t)[0][0] ** 2) / alpha(t)[0][0] * self.score(x, t, model, **model_kwargs)
+        elif last_step == "Euler":
+            last_step_fn = \
+                lambda x, t, model, **model_kwargs: \
+                    x + self.drift(x, t, model, **model_kwargs) * last_step_size
+        else:
+            raise NotImplementedError()
+
+        return last_step_fn
+
+    def sample_sde(
+        self,
+        *,
+        sampling_method="Euler",
+        diffusion_form="SBDM",
+        diffusion_norm=1.0,
+        last_step="Mean",
+        last_step_size=0.04,
+        num_steps=250,
+    ):
+        """returns a sampling function with given SDE settings
+        Args:
+        - sampling_method: type of sampler used in solving the SDE; default to be Euler-Maruyama
+        - diffusion_form: function form of diffusion coefficient; default to be matching SBDM
+        - diffusion_norm: function magnitude of diffusion coefficient; default to 1
+        - last_step: type of the last step; default to identity
+        - last_step_size: size of the last step; default to match the stride of 250 steps over [0,1]
+        - num_steps: total integration step of SDE
+        """
+
+        if last_step is None:
+            last_step_size = 0.0
+
+        sde_drift, sde_diffusion = self.__get_sde_diffusion_and_drift(
+            diffusion_form=diffusion_form,
+            diffusion_norm=diffusion_norm,
+        )
+
+        t0, t1 = self.transport.check_interval(
+            self.transport.train_eps,
+            self.transport.sample_eps,
+            diffusion_form=diffusion_form,
+            sde=True,
+            eval=True,
+            reverse=False,
+            last_step_size=last_step_size,
+        )
+
+        _sde = sde(
+            sde_drift,
+            sde_diffusion,
+            t0=t0,
+            t1=t1,
+            num_steps=num_steps,
+            sampler_type=sampling_method
+        )
+
+        last_step_fn = self.__get_last_step(sde_drift, last_step=last_step, last_step_size=last_step_size)
+            
+
+        def _sample(init, model, **model_kwargs):
+            xs = _sde.sample(init, model, **model_kwargs)
+            ts = th.ones(init.size(0), device=init.device) * t1
+            x = last_step_fn(xs[-1], ts, model, **model_kwargs)
+            xs.append(x)
+
+            assert len(xs) == num_steps, "Samples does not match the number of steps"
+
+            return xs
+
+        return _sample
+    
+    def sample_ode(
+        self,
+        *,
+        sampling_method="dopri5",
+        num_steps=50,
+        atol=1e-6,
+        rtol=1e-3,
+        reverse=False,
+        cfg=False,
+    ):
+        """returns a sampling function with given ODE settings
+        Args:
+        - sampling_method: type of sampler used in solving the ODE; default to be Dopri5
+        - num_steps: 
+            - fixed solver (Euler, Heun): the actual number of integration steps performed
+            - adaptive solver (Dopri5): the number of datapoints saved during integration; produced by interpolation
+        - atol: absolute error tolerance for the solver
+        - rtol: relative error tolerance for the solver
+        - reverse: whether solving the ODE in reverse (data to noise); default to False
+        """
+        if reverse:
+            drift = lambda x, t, model, **kwargs: self.drift(x, th.ones_like(t) * (1 - t), model, **kwargs)
+        else:
+            drift = self.drift
+
+        t0, t1 = self.transport.check_interval(
+            self.transport.train_eps,
+            self.transport.sample_eps,
+            sde=False,
+            eval=True,
+            reverse=reverse,
+            last_step_size=0.0,
+        )
+
+        _ode = ode(
+            drift=drift,
+            t0=t0,
+            t1=t1,
+            sampler_type=sampling_method,
+            num_steps=num_steps,
+            atol=atol,
+            rtol=rtol,
+            # guider=self.guider,
+        )
+        # if cfg:
+        #     return _ode.sample_cfg
+        # else:
+        return _ode.sample
+
+    def sample_ode_likelihood(
+        self,
+        *,
+        sampling_method="dopri5",
+        num_steps=50,
+        atol=1e-6,
+        rtol=1e-3,
+    ):
+        
+        """returns a sampling function for calculating likelihood with given ODE settings
+        Args:
+        - sampling_method: type of sampler used in solving the ODE; default to be Dopri5
+        - num_steps: 
+            - fixed solver (Euler, Heun): the actual number of integration steps performed
+            - adaptive solver (Dopri5): the number of datapoints saved during integration; produced by interpolation
+        - atol: absolute error tolerance for the solver
+        - rtol: relative error tolerance for the solver
+        """
+        def _likelihood_drift(x, t, model, **model_kwargs):
+            x, _ = x
+            eps = th.randint(2, x.size(), dtype=th.float, device=x.device) * 2 - 1
+            t = th.ones_like(t) * (1 - t)
+            with th.enable_grad():
+                x.requires_grad = True
+                grad = th.autograd.grad(th.sum(self.drift(x, t, model, **model_kwargs) * eps), x)[0]
+                logp_grad = th.sum(grad * eps, dim=tuple(range(1, len(x.size()))))
+                drift = self.drift(x, t, model, **model_kwargs)
+            return (-drift, logp_grad)
+        
+        t0, t1 = self.transport.check_interval(
+            self.transport.train_eps,
+            self.transport.sample_eps,
+            sde=False,
+            eval=True,
+            reverse=False,
+            last_step_size=0.0,
+        )
+
+        _ode = ode(
+            drift=_likelihood_drift,
+            t0=t0,
+            t1=t1,
+            sampler_type=sampling_method,
+            num_steps=num_steps,
+            atol=atol,
+            rtol=rtol,
+        )
+
+        def _sample_fn(x, model, **model_kwargs):
+            init_logp = th.zeros(x.size(0)).to(x)
+            input = (x, init_logp)
+            drift, delta_logp = _ode.sample(input, model, **model_kwargs)
+            drift, delta_logp = drift[-1], delta_logp[-1]
+            prior_logp = self.transport.prior_logp(drift)
+            logp = prior_logp - delta_logp
+            return logp, drift
+
+        return _sample_fn
\ No newline at end of file
diff --git a/transport/utils.py b/transport/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..44646035531326b81883727f973900edb4eac494
--- /dev/null
+++ b/transport/utils.py
@@ -0,0 +1,29 @@
+import torch as th
+
+class EasyDict:
+
+    def __init__(self, sub_dict):
+        for k, v in sub_dict.items():
+            setattr(self, k, v)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+def mean_flat(x):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return th.mean(x, dim=list(range(1, len(x.size()))))
+
+def log_state(state):
+    result = []
+    
+    sorted_state = dict(sorted(state.items()))
+    for key, value in sorted_state.items():
+        # Check if the value is an instance of a class
+        if "<object" in str(value) or "object at" in str(value):
+            result.append(f"{key}: [{value.__class__.__name__}]")
+        else:
+            result.append(f"{key}: {value}")
+    
+    return '\n'.join(result)
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..b2513815b6af9b68cd0104d45a6eac23f2a75809
--- /dev/null
+++ b/utils/dust3r/dpt_block.py
@@ -0,0 +1,567 @@
+# Copyright (C) 2022-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+
+# --------------------------------------------------------
+# DPT head for ViTs
+# --------------------------------------------------------
+# References:
+# https://github.com/isl-org/DPT
+# https://github.com/EPFL-VILAB/MultiMAE/blob/main/multimae/output_adapters.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from typing import Union, Tuple, Iterable, List, Optional, Dict
+from einops.layers.torch import Reduce, Rearrange
+
+from pdb import set_trace as st
+
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+
+def make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand == True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0],
+        out_shape1,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1],
+        out_shape2,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2],
+        out_shape3,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3],
+        out_shape4,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+
+    scratch.layer_rn = nn.ModuleList([
+        scratch.layer1_rn,
+        scratch.layer2_rn,
+        scratch.layer3_rn,
+        scratch.layer4_rn,
+    ])
+
+    return scratch
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features, activation, bn):
+        """Init.
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups = 1
+
+        self.conv1 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        self.conv2 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: output
+        """
+
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(
+        self,
+        features,
+        activation,
+        deconv=False,
+        bn=False,
+        expand=False,
+        align_corners=True,
+        width_ratio=1,
+    ):
+        """Init.
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+        self.width_ratio = width_ratio
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups = 1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+
+        self.out_conv = nn.Conv2d(
+            features,
+            out_features,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=True,
+            groups=1,
+        )
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            if self.width_ratio != 1:
+                res = F.interpolate(res,
+                                    size=(output.shape[2], output.shape[3]),
+                                    mode='bilinear')
+
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        if self.width_ratio != 1:
+            # and output.shape[3] < self.width_ratio * output.shape[2]
+            #size=(image.shape[])
+            if (output.shape[3] /
+                    output.shape[2]) < (2 / 3) * self.width_ratio:
+                shape = 3 * output.shape[3]
+            else:
+                shape = int(self.width_ratio * 2 * output.shape[2])
+            output = F.interpolate(output,
+                                   size=(2 * output.shape[2], shape),
+                                   mode='bilinear')
+        else:
+            output = nn.functional.interpolate(
+                output,
+                scale_factor=2,
+                mode="bilinear",
+                align_corners=self.align_corners)
+        output = self.out_conv(output)
+        return output
+
+
+def make_fusion_block(features, use_bn, width_ratio=1):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+        width_ratio=width_ratio,
+    )
+
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
+
+
+class DPTOutputAdapter(nn.Module):
+    """DPT output adapter.
+
+    :param num_cahnnels: Number of output channels
+    :param stride_level: tride level compared to the full-sized image.
+        E.g. 4 for 1/4th the size of the image.
+    :param patch_size_full: Int or tuple of the patch size over the full image size.
+        Patch size for smaller inputs will be computed accordingly.
+    :param hooks: Index of intermediate layers
+    :param layer_dims: Dimension of intermediate layers
+    :param feature_dim: Feature dimension
+    :param last_dim: out_channels/in_channels for the last two Conv2d when head_type == regression
+    :param use_bn: If set to True, activates batch norm
+    :param dim_tokens_enc:  Dimension of tokens coming from encoder
+    """
+
+    def __init__(
+            self,
+            num_channels: int = 1,
+            stride_level: int = 1,
+            #  patch_size: Union[int, Tuple[int, int]] = 16,
+            patch_size: Union[int, Tuple[int, int]] = 32,
+            main_tasks: Iterable[str] = ('rgb', ),
+            hooks: List[int] = [2, 5, 8, 11],
+            layer_dims: List[int] = [96, 192, 384, 768],
+            feature_dim: int = 256,
+            last_dim: int = 32,
+            use_bn: bool = False,
+            dim_tokens_enc: Optional[int] = None,
+            head_type: str = 'regression',
+            output_width_ratio=1,
+            **kwargs):
+        super().__init__()
+        self.num_channels = num_channels
+        self.stride_level = stride_level
+        self.patch_size = pair(patch_size)
+        self.main_tasks = main_tasks
+        self.hooks = hooks
+        self.layer_dims = layer_dims
+        self.feature_dim = feature_dim
+        self.dim_tokens_enc = dim_tokens_enc * len(
+            self.main_tasks) if dim_tokens_enc is not None else None
+        self.head_type = head_type
+
+        # Actual patch height and width, taking into account stride of input
+        self.P_H = max(1, self.patch_size[0] // stride_level)
+        self.P_W = max(1, self.patch_size[1] // stride_level)
+
+        self.scratch = make_scratch(layer_dims,
+                                    feature_dim,
+                                    groups=1,
+                                    expand=False)
+
+        self.scratch.refinenet1 = make_fusion_block(feature_dim, use_bn,
+                                                    output_width_ratio)
+        self.scratch.refinenet2 = make_fusion_block(feature_dim, use_bn,
+                                                    output_width_ratio)
+        self.scratch.refinenet3 = make_fusion_block(feature_dim, use_bn,
+                                                    output_width_ratio)
+        self.scratch.refinenet4 = make_fusion_block(feature_dim, use_bn,
+                                                    output_width_ratio)
+
+        if self.head_type == 'regression':
+            # The "DPTDepthModel" head
+            self.head = nn.Sequential(
+                nn.Conv2d(feature_dim,
+                          feature_dim // 2,
+                          kernel_size=3,
+                          stride=1,
+                          padding=1),
+                Interpolate(scale_factor=2,
+                            mode="bilinear",
+                            align_corners=True),
+                nn.Conv2d(feature_dim // 2,
+                          last_dim,
+                          kernel_size=3,
+                          stride=1,
+                          padding=1), nn.ReLU(True),
+                nn.Conv2d(last_dim,
+                          self.num_channels,
+                          kernel_size=1,
+                          stride=1,
+                          padding=0))
+
+        elif self.head_type == 'regression_voxel':
+            # The "DPTDepthModel" head
+            downscale_factor = 8 # from 128 -> 32
+            c_3d = 32 * 4  # hard coded
+            self.head = nn.Sequential(
+                nn.Conv2d(feature_dim,
+                          feature_dim // 2,
+                          kernel_size=3,
+                          stride=1,
+                          padding=1),
+                #
+                nn.PixelUnshuffle(downscale_factor=downscale_factor
+                                  ),  #B 64*16 32 32
+                #
+                Rearrange(f'b (c2 z) h w -> b c2 z h w', z=32, c2=c_3d),  # B 32 32 32 32
+                nn.ReLU(True),
+                # Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+                # nn.Conv2d(feature_dim // 2 * downscale_factor**2, last_dim, kernel_size=3, stride=1, padding=1),
+                nn.Conv3d(c_3d,
+                          last_dim,
+                          kernel_size=3,
+                          stride=1,
+                          padding=1),
+                nn.ReLU(True),
+                nn.Conv3d(last_dim,
+                          self.num_channels,
+                          kernel_size=1,
+                          stride=1,
+                          padding=0))
+
+        elif self.head_type == 'regression_gs':  # avoid upsampling here
+            self.head = nn.Sequential(
+                nn.Conv2d(feature_dim,
+                          feature_dim // 2,
+                          kernel_size=3,
+                          stride=1,
+                          padding=1),
+                # Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+                nn.ReLU(True),
+                # nn.Dropout(0.1, False),
+                nn.Conv2d(feature_dim // 2,
+                          last_dim,
+                          kernel_size=3,
+                          stride=1,
+                          padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(last_dim,
+                          self.num_channels,
+                          kernel_size=1,
+                          stride=1,
+                          padding=0))
+        elif self.head_type == 'semseg':
+            # The "DPTSegmentationModel" head
+            self.head = nn.Sequential(
+                nn.Conv2d(feature_dim,
+                          feature_dim,
+                          kernel_size=3,
+                          padding=1,
+                          bias=False),
+                nn.BatchNorm2d(feature_dim) if use_bn else nn.Identity(),
+                nn.ReLU(True),
+                nn.Dropout(0.1, False),
+                nn.Conv2d(feature_dim, self.num_channels, kernel_size=1),
+                Interpolate(scale_factor=2,
+                            mode="bilinear",
+                            align_corners=True),
+            )
+        else:
+            raise ValueError('DPT head_type must be "regression" or "semseg".')
+
+        if self.dim_tokens_enc is not None:
+            self.init(dim_tokens_enc=dim_tokens_enc)
+
+    def init(self, dim_tokens_enc=768):
+        """
+        Initialize parts of decoder that are dependent on dimension of encoder tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens_enc: Dimension of tokens coming from encoder
+        """
+        #print(dim_tokens_enc)
+
+        # Set up activation postprocessing layers
+        if isinstance(dim_tokens_enc, int):
+            dim_tokens_enc = 4 * [dim_tokens_enc]
+
+        self.dim_tokens_enc = [
+            dt * len(self.main_tasks) for dt in dim_tokens_enc
+        ]
+
+        self.act_1_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc[0],
+                out_channels=self.layer_dims[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=self.layer_dims[0],
+                out_channels=self.layer_dims[0],
+                kernel_size=4,
+                stride=4,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ))
+
+        self.act_2_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc[1],
+                out_channels=self.layer_dims[1],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=self.layer_dims[1],
+                out_channels=self.layer_dims[1],
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ))
+
+        self.act_3_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc[2],
+                out_channels=self.layer_dims[2],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ))
+
+        self.act_4_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc[3],
+                out_channels=self.layer_dims[3],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.Conv2d(
+                in_channels=self.layer_dims[3],
+                out_channels=self.layer_dims[3],
+                kernel_size=3,
+                stride=2,
+                padding=1,
+            ))
+
+        self.act_postprocess = nn.ModuleList([
+            self.act_1_postprocess, self.act_2_postprocess,
+            self.act_3_postprocess, self.act_4_postprocess
+        ])
+
+    def adapt_tokens(self, encoder_tokens):
+        # Adapt tokens
+        x = []
+        x.append(encoder_tokens[:, :])
+        x = torch.cat(x, dim=-1)
+        return x
+
+    def forward(self, encoder_tokens: List[torch.Tensor], image_size):
+        #input_info: Dict):
+        assert self.dim_tokens_enc is not None, 'Need to call init(dim_tokens_enc) function first'
+        H, W = image_size
+
+        # Number of patches in height and width
+        N_H = H // (self.stride_level * self.P_H)
+        N_W = W // (self.stride_level * self.P_W)
+
+        # Hook decoder onto 4 layers from specified ViT layers
+        layers = [encoder_tokens[hook] for hook in self.hooks]
+
+        # Extract only task-relevant tokens and ignore global tokens.
+        layers = [self.adapt_tokens(l) for l in layers]
+
+        # Reshape tokens to spatial representation
+        layers = [
+            rearrange(l, 'b (nh nw) c -> b c nh nw', nh=N_H, nw=N_W)
+            for l in layers
+        ]
+
+        layers = [self.act_postprocess[idx](l) for idx, l in enumerate(layers)]
+        # Project layers to chosen feature dim
+        layers = [
+            self.scratch.layer_rn[idx](l) for idx, l in enumerate(layers)
+        ]
+
+        # Fuse layers using refinement stages
+        path_4 = self.scratch.refinenet4(layers[3])
+        path_3 = self.scratch.refinenet3(path_4, layers[2])
+        path_2 = self.scratch.refinenet2(path_3, layers[1])
+        path_1 = self.scratch.refinenet1(path_2, layers[0])
+
+        # Output head
+        out = self.head(path_1)
+
+        return out
diff --git a/utils/dust3r/heads/__init__.py b/utils/dust3r/heads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..53d0aa5610cae95f34f96bdb3ff9e835a2d6208e
--- /dev/null
+++ b/utils/dust3r/heads/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+#
+# --------------------------------------------------------
+# head factory
+# --------------------------------------------------------
+from .linear_head import LinearPts3d
+from .dpt_head import create_dpt_head
+
+
+def head_factory(head_type, output_mode, net, has_conf=False):
+    """" build a prediction head for the decoder 
+    """
+    if head_type == 'linear' and output_mode == 'pts3d':
+        return LinearPts3d(net, has_conf)
+    elif head_type == 'dpt' and output_mode == 'pts3d':
+        return create_dpt_head(net, has_conf=has_conf)
+    else:
+        raise NotImplementedError(f"unexpected {head_type=} and {output_mode=}")
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diff --git a/utils/dust3r/heads/__pycache__/postprocess.cpython-39.pyc b/utils/dust3r/heads/__pycache__/postprocess.cpython-39.pyc
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diff --git a/utils/dust3r/heads/dpt_head.py b/utils/dust3r/heads/dpt_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5780c79a5e81da02f8c7c26b3626f42c081792b
--- /dev/null
+++ b/utils/dust3r/heads/dpt_head.py
@@ -0,0 +1,178 @@
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+#
+# --------------------------------------------------------
+# dpt head implementation for DUST3R
+# Downstream heads assume inputs of size B x N x C (where N is the number of tokens) ;
+# or if it takes as input the output at every layer, the attribute return_all_layers should be set to True
+# the forward function also takes as input a dictionnary img_info with key "height" and "width"
+# for PixelwiseTask, the output will be of dimension B x num_channels x H x W
+# --------------------------------------------------------
+from einops import rearrange
+from typing import List
+import torch
+import torch.nn as nn
+from utils.dust3r.heads.postprocess import postprocess
+# import utils.dust3r.utils.path_to_croco  # noqa: F401
+from utils.dust3r.dpt_block import DPTOutputAdapter  # noqa
+
+from pdb import set_trace as st
+
+
+class DPTOutputAdapter_fix(DPTOutputAdapter):
+    """
+    Adapt croco's DPTOutputAdapter implementation for dust3r:
+    remove duplicated weigths, and fix forward for dust3r
+    """
+
+    def init(self, dim_tokens_enc=768):
+        super().init(dim_tokens_enc)
+        # these are duplicated weights
+        del self.act_1_postprocess
+        del self.act_2_postprocess
+        del self.act_3_postprocess
+        del self.act_4_postprocess
+
+    def forward(self, encoder_tokens: List[torch.Tensor], image_size=None):
+        assert self.dim_tokens_enc is not None, 'Need to call init(dim_tokens_enc) function first'
+        # H, W = input_info['image_size']
+        image_size = self.image_size if image_size is None else image_size
+        H, W = image_size
+        # Number of patches in height and width
+        N_H = H // (self.stride_level * self.P_H)
+        N_W = W // (self.stride_level * self.P_W)
+        # st()
+
+        # Hook decoder onto 4 layers from specified ViT layers
+        layers = [encoder_tokens[hook] for hook in self.hooks]
+
+        # Extract only task-relevant tokens and ignore global tokens.
+        layers = [self.adapt_tokens(l) for l in layers]
+
+        # Reshape tokens to spatial representation
+        # st()
+        layers = [
+            rearrange(l, 'b (nh nw) c -> b c nh nw', nh=N_H, nw=N_W)
+            for l in layers
+        ]
+        # st()
+
+        layers = [self.act_postprocess[idx](l) for idx, l in enumerate(layers)]
+        # Project layers to chosen feature dim
+        layers = [
+            self.scratch.layer_rn[idx](l) for idx, l in enumerate(layers)
+        ]
+
+        # Fuse layers using refinement stages
+        path_4 = self.scratch.refinenet4(
+            layers[3])[:, :, :layers[2].shape[2], :layers[2].shape[3]]
+        path_3 = self.scratch.refinenet3(path_4, layers[2])
+        path_2 = self.scratch.refinenet2(path_3, layers[1])
+        path_1 = self.scratch.refinenet1(path_2, layers[0]) # B 128 256 256
+
+        # Output head
+        # st()
+        out = self.head(path_1)
+
+        return out
+
+
+class PixelwiseTaskWithDPT(nn.Module):
+    """ DPT module for dust3r, can return 3D points + confidence for all pixels"""
+
+    def __init__(self,
+                 *,
+                 n_cls_token=0,
+                 hooks_idx=None,
+                 dim_tokens=None,
+                 output_width_ratio=1,
+                 num_channels=1,
+                 postprocess=None,
+                 depth_mode=None,
+                 conf_mode=None,
+                 **kwargs):
+        super(PixelwiseTaskWithDPT, self).__init__()
+        self.return_all_layers = True  # backbone needs to return all layers
+        self.postprocess = postprocess
+        self.depth_mode = depth_mode
+        self.conf_mode = conf_mode
+
+        assert n_cls_token == 0, "Not implemented"
+        dpt_args = dict(output_width_ratio=output_width_ratio,
+                        num_channels=num_channels,
+                        **kwargs)
+        if hooks_idx is not None:
+            dpt_args.update(hooks=hooks_idx)
+        self.dpt = DPTOutputAdapter_fix(**dpt_args)
+        dpt_init_args = {} if dim_tokens is None else {
+            'dim_tokens_enc': dim_tokens
+        }
+        self.dpt.init(**dpt_init_args)
+
+        # ! remove unused param
+        del self.dpt.scratch.refinenet4.resConfUnit1
+
+    def forward(self, x, img_info):
+        out = self.dpt(x, image_size=(img_info[0], img_info[1]))
+        if self.postprocess:
+            out = self.postprocess(out, self.depth_mode, self.conf_mode)
+        return out
+
+
+def create_dpt_head(net, has_conf=False):
+    """
+    return PixelwiseTaskWithDPT for given net params
+    """
+    assert net.dec_depth > 9
+    l2 = net.dec_depth
+    feature_dim = 256
+    last_dim = feature_dim // 2
+    out_nchan = 3
+    ed = net.enc_embed_dim
+    dd = net.dec_embed_dim
+    return PixelwiseTaskWithDPT(
+        num_channels=out_nchan + has_conf,
+        feature_dim=feature_dim,
+        last_dim=last_dim,
+        hooks_idx=[0, l2 * 2 // 4, l2 * 3 // 4, l2],
+        dim_tokens=[ed, dd, dd, dd],
+        postprocess=postprocess,
+        # postprocess=None,
+        depth_mode=net.depth_mode,
+        conf_mode=net.conf_mode,
+        head_type='regression')
+
+
+# def create_dpt_head_ln3diff(net, has_conf=False):
+def create_dpt_head_ln3diff(out_nchan,
+                            feature_dim,
+                            l2,
+                            dec_embed_dim,
+                            patch_size=2,
+                            has_conf=False,
+                            head_type='regression_gs'):
+    """
+    return PixelwiseTaskWithDPT for given net params
+    """
+    # assert net.dec_depth > 9
+    # l2 = net.dec_depth
+    # feature_dim = 256
+    last_dim = feature_dim // 2
+    # out_nchan = 3
+    # ed = net.enc_embed_dim
+    # dd = net.dec_embed_dim
+    dd = dec_embed_dim
+    return PixelwiseTaskWithDPT(
+        num_channels=out_nchan + has_conf,
+        feature_dim=feature_dim,
+        last_dim=last_dim,
+        patch_size=patch_size,
+        hooks_idx=[(l2 * 1 // 4) - 1, (l2 * 2 // 4) - 1, (l2 * 3 // 4) - 1,
+                   l2 - 1],
+        # dim_tokens=[ed, dd, dd, dd],
+        dim_tokens=[dd, dd, dd, dd],
+        # postprocess=postprocess,
+        postprocess=None,
+        # depth_mode=net.depth_mode,
+        # conf_mode=net.conf_mode,
+        head_type=head_type)
diff --git a/utils/dust3r/heads/linear_head.py b/utils/dust3r/heads/linear_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c5678d551033cc576798626b7ba59b1e7b20cc
--- /dev/null
+++ b/utils/dust3r/heads/linear_head.py
@@ -0,0 +1,41 @@
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+#
+# --------------------------------------------------------
+# linear head implementation for DUST3R
+# --------------------------------------------------------
+import torch.nn as nn
+import torch.nn.functional as F
+from .postprocess import postprocess
+
+
+class LinearPts3d (nn.Module):
+    """ 
+    Linear head for dust3r
+    Each token outputs: - 16x16 3D points (+ confidence)
+    """
+
+    def __init__(self, net, has_conf=False):
+        super().__init__()
+        self.patch_size = net.patch_embed.patch_size[0]
+        self.depth_mode = net.depth_mode
+        self.conf_mode = net.conf_mode
+        self.has_conf = has_conf
+
+        self.proj = nn.Linear(net.dec_embed_dim, (3 + has_conf)*self.patch_size**2)
+
+    def setup(self, croconet):
+        pass
+
+    def forward(self, decout, img_shape):
+        H, W = img_shape
+        tokens = decout[-1]
+        B, S, D = tokens.shape
+
+        # extract 3D points
+        feat = self.proj(tokens)  # B,S,D
+        feat = feat.transpose(-1, -2).view(B, -1, H//self.patch_size, W//self.patch_size)
+        feat = F.pixel_shuffle(feat, self.patch_size)  # B,3,H,W
+
+        # permute + norm depth
+        return postprocess(feat, self.depth_mode, self.conf_mode)
diff --git a/utils/dust3r/heads/postprocess.py b/utils/dust3r/heads/postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd68a90d89b8dcd7d8a4b4ea06ef8b17eb5da093
--- /dev/null
+++ b/utils/dust3r/heads/postprocess.py
@@ -0,0 +1,58 @@
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+#
+# --------------------------------------------------------
+# post process function for all heads: extract 3D points/confidence from output
+# --------------------------------------------------------
+import torch
+
+
+def postprocess(out, depth_mode, conf_mode):
+    """
+    extract 3D points/confidence from prediction head output
+    """
+    fmap = out.permute(0, 2, 3, 1)  # B,H,W,3
+    res = dict(pts3d=reg_dense_depth(fmap[:, :, :, 0:3], mode=depth_mode))
+
+    if conf_mode is not None:
+        res['conf'] = reg_dense_conf(fmap[:, :, :, 3], mode=conf_mode)
+    return res
+
+
+def reg_dense_depth(xyz, mode):
+    """
+    extract 3D points from prediction head output
+    """
+    mode, vmin, vmax = mode
+
+    no_bounds = (vmin == -float('inf')) and (vmax == float('inf'))
+    assert no_bounds
+
+    if mode == 'linear':
+        if no_bounds:
+            return xyz  # [-inf, +inf]
+        return xyz.clip(min=vmin, max=vmax)
+
+    # distance to origin
+    d = xyz.norm(dim=-1, keepdim=True)
+    xyz = xyz / d.clip(min=1e-8)
+
+    if mode == 'square':
+        return xyz * d.square()
+
+    if mode == 'exp':
+        return xyz * torch.expm1(d)
+
+    raise ValueError(f'bad {mode=}')
+
+
+def reg_dense_conf(x, mode):
+    """
+    extract confidence from prediction head output
+    """
+    mode, vmin, vmax = mode
+    if mode == 'exp':
+        return vmin + x.exp().clip(max=vmax-vmin)
+    if mode == 'sigmoid':
+        return (vmax - vmin) * torch.sigmoid(x) + vmin
+    raise ValueError(f'bad {mode=}')
diff --git a/utils/emd/README.md b/utils/emd/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1e864631240210444dcecdc03fbaadfe6d1cf799
--- /dev/null
+++ b/utils/emd/README.md
@@ -0,0 +1,24 @@
+## Earth Mover's Distance of point clouds
+
+![](/utils/emdls/emd/CDEMD.png)
+
+Compared to the Chamfer Distance (CD), the Earth Mover's Distance (EMD) is more reliable to distinguish the visual quality of the point clouds. See our [paper](http://cseweb.ucsd.edu/~mil070/projects/AAAI2020/paper.pdf) for more details. 
+
+We provide an EMD implementation for point cloud comparison, which only needs $O(n)$ memory and thus enables dense point clouds (with 10,000 points or over) and large batch size. It is based on an approximated algorithm (auction algorithm) and cannot guarantee a (but near) bijection assignment. It employs a parameter $\epsilon$ to balance the error rate and the speed of convergence. Smaller $\epsilon$ achieves more accurate results, but needs a longer time for convergence. The time complexity is $O(n^2k)$, where $k$ is the number of iterations. We set a $\epsilon = 0.005, k = 50$ during training and a $\epsilon = 0.002, k = 10000$ during testing. 
+
+### Compile
+Run `python3 setup.py install` to compile.
+
+### Example
+See `emd_module.py/test_emd()` for examples.
+
+### Input
+
+- **xyz1, xyz2**: float tensors with shape `[#batch, #points, 3]`. xyz1 is the predicted point cloud and xyz2 is the ground truth point cloud. Two point clouds should have same size and be normalized to [0, 1]. The number of points should be a multiple of 1024. The batch size should be no greater than 512. Since we only calculate gradients for xyz1, please do not swap xyz1 and xyz2.
+- **eps**: a float tensor, the parameter balances the error rate and the speed of convergence.
+- **iters**: a int tensor, the number of iterations.
+
+### Output
+
+- **dist**: a float tensor with shape `[#batch, #points]`. sqrt(dist) are the L2 distances between the pairs of points.
+- **assignment**: a int tensor with shape `[#batch, #points]`. The index of the matched point in the ground truth point cloud.
diff --git a/utils/emd/__pycache__/emd_module.cpython-39.pyc b/utils/emd/__pycache__/emd_module.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..57cb5851f4d396c21d280a4c5b9a22319db5f918
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diff --git a/utils/emd/clean.sh b/utils/emd/clean.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b7b55e7b54d6c89d87b5288b57696fb14dc44021
--- /dev/null
+++ b/utils/emd/clean.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+
+rm -rf __pycache__/
+rm -rf dist/
+rm -rf build/
+rm -rf emd.egg-info/
+rm -rf /mnt/lustre/chenxinyi1/.conda/envs/pt/lib/python3.7/site-packages/emd-0.0.0-py3.7-linux-x86_64.egg/
diff --git a/utils/emd/emd.cpp b/utils/emd/emd.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..5036016370aa5f148b17ec44ea7a30bc576c3a82
--- /dev/null
+++ b/utils/emd/emd.cpp
@@ -0,0 +1,31 @@
+// EMD approximation module (based on auction algorithm)
+// author: Minghua Liu
+#include <torch/extension.h>
+#include <vector>
+
+int emd_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, 
+	                 at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments,
+	                 at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters);
+
+int emd_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx);
+
+
+
+int emd_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, 
+	                 at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments,
+	                 at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters) {
+	return emd_cuda_forward(xyz1, xyz2, dist, assignment, price, assignment_inv, bid, bid_increments, max_increments, unass_idx, unass_cnt, unass_cnt_sum, cnt_tmp, max_idx, eps, iters);
+}
+
+int emd_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx) {
+
+    return emd_cuda_backward(xyz1, xyz2, gradxyz, graddist, idx);
+}
+
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &emd_forward, "emd forward (CUDA)");
+  m.def("backward", &emd_backward, "emd backward (CUDA)");
+}
\ No newline at end of file
diff --git a/utils/emd/emd_cuda.cu b/utils/emd/emd_cuda.cu
new file mode 100644
index 0000000000000000000000000000000000000000..08999bbac057bd09ed7668b71e2f691e4f06fd6b
--- /dev/null
+++ b/utils/emd/emd_cuda.cu
@@ -0,0 +1,316 @@
+// EMD approximation module (based on auction algorithm)
+// author: Minghua Liu
+#include <stdio.h>
+#include <ATen/ATen.h>
+
+#include <cuda.h>
+#include <iostream>
+#include <cuda_runtime.h>
+
+__device__ __forceinline__ float atomicMax(float *address, float val)
+{
+    int ret = __float_as_int(*address);
+    while(val > __int_as_float(ret))
+    {
+        int old = ret;
+        if((ret = atomicCAS((int *)address, old, __float_as_int(val))) == old)
+            break;
+    }
+    return __int_as_float(ret);
+}
+
+
+__global__ void clear(int b, int * cnt_tmp, int * unass_cnt) {
+	for (int i = threadIdx.x; i < b; i += blockDim.x) {
+		cnt_tmp[i] = 0;
+		unass_cnt[i] = 0;
+	}
+}
+
+__global__ void calc_unass_cnt(int b, int n, int * assignment, int * unass_cnt) { 
+	// count the number of unassigned points in each batch
+	const int BLOCK_SIZE = 1024; 
+	__shared__ int scan_array[BLOCK_SIZE];
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		scan_array[threadIdx.x] = assignment[i * n + blockIdx.y * BLOCK_SIZE + threadIdx.x] == -1 ? 1 : 0;
+		__syncthreads();
+		
+		int stride = 1;
+		while(stride <= BLOCK_SIZE / 2) {
+			int index = (threadIdx.x + 1) * stride * 2 - 1; 
+			if(index < BLOCK_SIZE)
+				scan_array[index] += scan_array[index - stride]; 
+			stride = stride * 2;
+			__syncthreads(); 
+		}
+		__syncthreads();
+		
+		if (threadIdx.x == BLOCK_SIZE - 1) {
+			atomicAdd(&unass_cnt[i], scan_array[threadIdx.x]);
+		}
+		__syncthreads();
+	}
+}
+
+__global__ void calc_unass_cnt_sum(int b, int * unass_cnt, int * unass_cnt_sum) {
+	// count the cumulative sum over over unass_cnt
+	const int BLOCK_SIZE = 512; // batch_size <= 512
+	__shared__ int scan_array[BLOCK_SIZE];
+	scan_array[threadIdx.x] = unass_cnt[threadIdx.x];
+	__syncthreads();
+	
+	int stride = 1;
+	while(stride <= BLOCK_SIZE / 2) {
+		int index = (threadIdx.x + 1) * stride * 2 - 1; 
+		if(index < BLOCK_SIZE)
+			scan_array[index] += scan_array[index - stride]; 
+		stride = stride * 2;
+		__syncthreads(); 
+	}
+	__syncthreads();
+	stride = BLOCK_SIZE / 4; 
+	while(stride > 0) {
+		int index = (threadIdx.x + 1) * stride * 2 - 1; 
+		if((index + stride) < BLOCK_SIZE)
+			scan_array[index + stride] += scan_array[index];
+		stride = stride / 2;
+		__syncthreads(); 
+	}
+	__syncthreads(); 
+	
+	//printf("%d\n", unass_cnt_sum[b - 1]);
+	unass_cnt_sum[threadIdx.x] = scan_array[threadIdx.x];
+}
+
+__global__ void calc_unass_idx(int b, int n, int * assignment, int * unass_idx, int * unass_cnt, int * unass_cnt_sum, int * cnt_tmp) {
+	// list all the unassigned points
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		if (assignment[i * n + blockIdx.y * 1024 + threadIdx.x] == -1) {
+			int idx = atomicAdd(&cnt_tmp[i], 1);
+			unass_idx[unass_cnt_sum[i] - unass_cnt[i] + idx] = blockIdx.y * 1024 + threadIdx.x;
+		} 
+	}
+}
+
+__global__ void Bid(int b, int n, const float * xyz1, const float * xyz2, float eps, int * assignment, int * assignment_inv, float * price, 
+					int * bid, float * bid_increments, float * max_increments, int * unass_cnt, int * unass_cnt_sum, int * unass_idx) {
+	const int batch = 2048, block_size = 1024, block_cnt = n / 1024;
+	__shared__ float xyz2_buf[batch * 3];
+	__shared__ float price_buf[batch];
+	__shared__ float best_buf[block_size];
+	__shared__ float better_buf[block_size];
+	__shared__ int best_i_buf[block_size];
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		int _unass_cnt = unass_cnt[i];
+		if (_unass_cnt == 0)
+			continue;
+		int _unass_cnt_sum = unass_cnt_sum[i];
+		int unass_per_block = (_unass_cnt + block_cnt - 1) / block_cnt;
+		int thread_per_unass = block_size / unass_per_block;
+		int unass_this_block = max(min(_unass_cnt - (int) blockIdx.y * unass_per_block, unass_per_block), 0);
+			
+		float x1, y1, z1, best = -1e9, better = -1e9;
+		int best_i = -1, _unass_id = -1, thread_in_unass;
+
+		if (threadIdx.x < thread_per_unass * unass_this_block) {
+			_unass_id = unass_per_block * blockIdx.y + threadIdx.x / thread_per_unass + _unass_cnt_sum - _unass_cnt;
+			_unass_id = unass_idx[_unass_id];
+			thread_in_unass = threadIdx.x % thread_per_unass;
+
+			x1 = xyz1[(i * n + _unass_id) * 3 + 0];
+			y1 = xyz1[(i * n + _unass_id) * 3 + 1];
+			z1 = xyz1[(i * n + _unass_id) * 3 + 2];
+		}
+
+		for (int k2 = 0; k2 < n; k2 += batch) {
+			int end_k = min(n, k2 + batch) - k2;
+			for (int j = threadIdx.x; j < end_k * 3; j += blockDim.x) {
+				xyz2_buf[j] = xyz2[(i * n + k2) * 3 + j];
+			}
+			for (int j = threadIdx.x; j < end_k; j += blockDim.x) {
+				price_buf[j] = price[i * n + k2 + j];
+			}
+			__syncthreads();
+
+			if (_unass_id != -1) {
+				int delta = (end_k + thread_per_unass - 1) / thread_per_unass;
+				int l = thread_in_unass * delta;
+				int r = min((thread_in_unass + 1) * delta, end_k);
+				for (int k = l; k < r; k++) 
+				//if (!last || assignment_inv[i * n + k + k2] == -1)
+				{
+					float x2 = xyz2_buf[k * 3 + 0] - x1;
+					float y2 = xyz2_buf[k * 3 + 1] - y1;
+					float z2 = xyz2_buf[k * 3 + 2] - z1;
+					// the coordinates of points should be normalized to [0, 1]
+					float d = 3.0 - sqrtf(x2 * x2 + y2 * y2 + z2 * z2) - price_buf[k];
+					if (d > best) {
+						better = best;
+						best = d;
+						best_i = k + k2;
+					}
+					else if (d > better) {
+						better = d;
+					}
+				}
+			}
+			__syncthreads();
+		}
+
+		best_buf[threadIdx.x] = best;
+		better_buf[threadIdx.x] = better;
+		best_i_buf[threadIdx.x] = best_i;
+		__syncthreads();
+		
+		if (_unass_id != -1 && thread_in_unass == 0) {
+			for (int j = threadIdx.x + 1; j < threadIdx.x + thread_per_unass; j++) {
+				if (best_buf[j] > best) {
+					better = max(best, better_buf[j]);
+					best = best_buf[j];
+					best_i = best_i_buf[j];
+				}
+				else better = max(better, best_buf[j]);
+			}
+			bid[i * n + _unass_id] = best_i;
+			bid_increments[i * n + _unass_id] = best - better + eps; 
+			atomicMax(&max_increments[i * n + best_i], best - better + eps);
+		}
+	}
+}
+
+__global__ void GetMax(int b, int n, int * assignment, int * bid, float * bid_increments, float * max_increments, int * max_idx) {
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		int j = threadIdx.x + blockIdx.y * blockDim.x;
+		if (assignment[i * n + j] == -1) {
+			int bid_id = bid[i * n + j];
+			float bid_inc = bid_increments[i * n + j];
+			float max_inc = max_increments[i * n + bid_id];
+			if (bid_inc - 1e-6 <= max_inc && max_inc <= bid_inc + 1e-6) 
+			{
+				max_idx[i * n + bid_id] = j;
+			}
+		}
+	}
+}
+
+__global__ void Assign(int b, int n, int * assignment, int * assignment_inv, float * price, int * bid, float * bid_increments, float * max_increments, int * max_idx, bool last) {
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		int j = threadIdx.x + blockIdx.y * blockDim.x;
+		if (assignment[i * n + j] == -1) {
+			int bid_id = bid[i * n + j];
+			if (last || max_idx[i * n + bid_id] == j) 
+			{
+				float bid_inc = bid_increments[i * n + j];
+				int ass_inv = assignment_inv[i * n + bid_id];
+				if (!last && ass_inv != -1) {
+					assignment[i * n + ass_inv] = -1;
+				}
+				assignment_inv[i * n + bid_id] = j;
+				assignment[i * n + j] = bid_id;
+				price[i * n + bid_id] += bid_inc;
+				max_increments[i * n + bid_id] = -1e9;
+			}
+		}
+	}
+}
+
+__global__ void CalcDist(int b, int n, float * xyz1, float * xyz2, float * dist, int * assignment) {
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		int j = threadIdx.x + blockIdx.y * blockDim.x;
+		int k = assignment[i * n + j];
+		float deltax = xyz1[(i * n + j) * 3 + 0] - xyz2[(i * n + k) * 3 + 0];
+		float deltay = xyz1[(i * n + j) * 3 + 1] - xyz2[(i * n + k) * 3 + 1];
+		float deltaz = xyz1[(i * n + j) * 3 + 2] - xyz2[(i * n + k) * 3 + 2];
+		dist[i * n + j] = deltax * deltax + deltay * deltay + deltaz * deltaz;
+	}
+}
+
+int emd_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, 
+	                 at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments,
+	                 at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters) {
+
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+	
+	if (n != m) {
+		printf("Input Error! The two point clouds should have the same size.\n");
+		return -1;
+	}
+
+	if (batch_size > 512) {
+		printf("Input Error! The batch size should be less than 512.\n");
+		return -1;
+	}
+
+	if (n % 1024 != 0) {
+		printf("Input Error! The size of the point clouds should be a multiple of 1024.\n");
+		return -1;
+	}
+
+	//cudaEvent_t start,stop;
+	//cudaEventCreate(&start);
+	//cudaEventCreate(&stop);
+	//cudaEventRecord(start);
+	//int iters = 50;
+	for (int i = 0; i < iters; i++) {
+		clear<<<1, batch_size>>>(batch_size, cnt_tmp.data<int>(), unass_cnt.data<int>());
+		calc_unass_cnt<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, assignment.data<int>(), unass_cnt.data<int>());
+		calc_unass_cnt_sum<<<1, batch_size>>>(batch_size, unass_cnt.data<int>(), unass_cnt_sum.data<int>());
+		calc_unass_idx<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, assignment.data<int>(), unass_idx.data<int>(), unass_cnt.data<int>(), 
+											 unass_cnt_sum.data<int>(), cnt_tmp.data<int>());
+		Bid<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, xyz1.data<float>(), xyz2.data<float>(), eps, assignment.data<int>(), assignment_inv.data<int>(), 
+			                          price.data<float>(), bid.data<int>(), bid_increments.data<float>(), max_increments.data<float>(),
+			                          unass_cnt.data<int>(), unass_cnt_sum.data<int>(), unass_idx.data<int>());
+		GetMax<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, assignment.data<int>(), bid.data<int>(), bid_increments.data<float>(), max_increments.data<float>(), max_idx.data<int>());
+		Assign<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, assignment.data<int>(), assignment_inv.data<int>(), price.data<float>(), bid.data<int>(),
+									  bid_increments.data<float>(), max_increments.data<float>(), max_idx.data<int>(), i == iters - 1);
+	}
+	CalcDist<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, xyz1.data<float>(), xyz2.data<float>(), dist.data<float>(), assignment.data<int>());
+	//cudaEventRecord(stop);
+	//cudaEventSynchronize(stop);
+	//float elapsedTime;
+	//cudaEventElapsedTime(&elapsedTime,start,stop);
+	//printf("%lf\n", elapsedTime);
+
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd Output: %s\n", cudaGetErrorString(err));
+	    return 0;
+	  }
+	  return 1;
+}
+
+__global__ void NmDistanceGradKernel(int b, int n, const float * xyz1, const float * xyz2, const float * grad_dist, const int * idx, float * grad_xyz){
+	for (int i = blockIdx.x; i < b; i += gridDim.x) {
+		for (int j = threadIdx.x + blockIdx.y * blockDim.x; j < n; j += blockDim.x * gridDim.y) {
+			float x1 = xyz1[(i * n + j) * 3 + 0];
+			float y1 = xyz1[(i * n + j) * 3 + 1];
+			float z1 = xyz1[(i * n + j) * 3 + 2];
+			int j2 = idx[i * n + j];
+			float x2 = xyz2[(i * n + j2) * 3 + 0];
+			float y2 = xyz2[(i * n + j2) * 3 + 1];
+			float z2 = xyz2[(i * n + j2) * 3 + 2];
+			float g = grad_dist[i * n + j] * 2;
+			atomicAdd(&(grad_xyz[(i * n + j) * 3 + 0]), g * (x1 - x2));
+			atomicAdd(&(grad_xyz[(i * n + j) * 3 + 1]), g * (y1 - y2));
+			atomicAdd(&(grad_xyz[(i * n + j) * 3 + 2]), g * (z1 - z2));
+		}
+	}
+}
+
+int emd_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx){
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); 
+	const auto m = xyz2.size(1); 
+
+	NmDistanceGradKernel<<<dim3(batch_size, n / 1024, 1), 1024>>>(batch_size, n, xyz1.data<float>(), xyz2.data<float>(), graddist.data<float>(), idx.data<int>(), gradxyz.data<float>());
+	
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd get grad: %s\n", cudaGetErrorString(err));
+	    return 0;
+	  }
+	  return 1;
+	
+}
diff --git a/utils/emd/emd_module.py b/utils/emd/emd_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cd370992cc6caea06ac8c5fd0f891e53ecc46cb
--- /dev/null
+++ b/utils/emd/emd_module.py
@@ -0,0 +1,100 @@
+# EMD approximation module (based on auction algorithm)
+# memory complexity: O(n)
+# time complexity: O(n^2 * iter) 
+# author: Minghua Liu
+
+# Input:
+# xyz1, xyz2: [#batch, #points, 3]
+# where xyz1 is the predicted point cloud and xyz2 is the ground truth point cloud 
+# two point clouds should have same size and be normalized to [0, 1]
+# #points should be a multiple of 1024
+# #batch should be no greater than 512
+# eps is a parameter which balances the error rate and the speed of convergence
+# iters is the number of iteration
+# we only calculate gradient for xyz1
+
+# Output:
+# dist: [#batch, #points],  sqrt(dist) -> L2 distance 
+# assignment: [#batch, #points], index of the matched point in the ground truth point cloud
+# the result is an approximation and the assignment is not guranteed to be a bijection
+
+import time
+import numpy as np
+import torch
+from torch import nn
+from torch.autograd import Function
+import emd
+
+
+
+
+class emdFunction(Function):
+    @staticmethod
+    def forward(ctx, xyz1, xyz2, eps, iters):
+
+        batchsize, n, _ = xyz1.size()
+        _, m, _ = xyz2.size()
+
+        assert(n == m)
+        assert(xyz1.size()[0] == xyz2.size()[0])
+        #assert(n % 1024 == 0)
+        assert(batchsize <= 512)
+
+        xyz1 = xyz1.contiguous().float().cuda()
+        xyz2 = xyz2.contiguous().float().cuda()
+        dist = torch.zeros(batchsize, n, device='cuda').contiguous()
+        assignment = torch.zeros(batchsize, n, device='cuda', dtype=torch.int32).contiguous() - 1
+        assignment_inv = torch.zeros(batchsize, m, device='cuda', dtype=torch.int32).contiguous() - 1
+        price = torch.zeros(batchsize, m, device='cuda').contiguous()
+        bid = torch.zeros(batchsize, n, device='cuda', dtype=torch.int32).contiguous()
+        bid_increments = torch.zeros(batchsize, n, device='cuda').contiguous()
+        max_increments = torch.zeros(batchsize, m, device='cuda').contiguous()
+        unass_idx = torch.zeros(batchsize * n, device='cuda', dtype=torch.int32).contiguous()
+        max_idx = torch.zeros(batchsize * m, device='cuda', dtype=torch.int32).contiguous()
+        unass_cnt = torch.zeros(512, dtype=torch.int32, device='cuda').contiguous()
+        unass_cnt_sum = torch.zeros(512, dtype=torch.int32, device='cuda').contiguous()
+        cnt_tmp = torch.zeros(512, dtype=torch.int32, device='cuda').contiguous()
+
+        emd.forward(xyz1, xyz2, dist, assignment, price, assignment_inv, bid, bid_increments, max_increments, unass_idx, unass_cnt, unass_cnt_sum, cnt_tmp, max_idx, eps, iters)
+
+        ctx.save_for_backward(xyz1, xyz2, assignment)
+        return dist, assignment
+
+    @staticmethod
+    def backward(ctx, graddist, gradidx):
+        xyz1, xyz2, assignment = ctx.saved_tensors
+        graddist = graddist.contiguous()
+
+        gradxyz1 = torch.zeros(xyz1.size(), device='cuda').contiguous()
+        gradxyz2 = torch.zeros(xyz2.size(), device='cuda').contiguous()
+
+        emd.backward(xyz1, xyz2, gradxyz1, graddist, assignment)
+        return gradxyz1, gradxyz2, None, None
+
+class emdModule(nn.Module):
+    def __init__(self):
+        super(emdModule, self).__init__()
+
+    def forward(self, input1, input2, eps, iters):
+        return emdFunction.apply(input1, input2, eps, iters)
+
+def test_emd():
+    x1 = torch.rand(20, 8192, 3).cuda()
+    x2 = torch.rand(20, 8192, 3).cuda()
+    emd = emdModule()
+    start_time = time.perf_counter()
+    dis, assigment = emd(x1, x2, 0.05, 3000)
+    # print(dis.shape) # B N
+    print("Input_size: ", x1.shape)
+    print("Runtime: %lfs" % (time.perf_counter() - start_time))
+    print("EMD: %lf" % np.sqrt(dis.cpu()).mean())
+    print("|set(assignment)|: %d" % assigment.unique().numel())
+    assigment = assigment.cpu().numpy()
+    assigment = np.expand_dims(assigment, -1)
+    x2 = np.take_along_axis(x2, assigment, axis = 1)
+    d = (x1 - x2) * (x1 - x2)
+    print("Verified EMD: %lf" % np.sqrt(d.cpu().sum(-1)).mean())
+
+# test_emd()
+    
+       
diff --git a/utils/emd/run_compile.sh b/utils/emd/run_compile.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e3ea41eb60d8cb43743342bcdb810f3d323ff40b
--- /dev/null
+++ b/utils/emd/run_compile.sh
@@ -0,0 +1,9 @@
+partition=ips_share
+job_name=compile
+gpus=1
+g=$((${gpus}<8?${gpus}:8))
+
+
+srun -u --partition=${partition} --job-name=${job_name} \
+    -n1 --gres=gpu:${gpus} --ntasks-per-node=1 -w 'SH-IDC1-10-198-6-85' \
+    python3 emd_module.py
diff --git a/utils/emd/setup.py b/utils/emd/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..8588de957bc285372be5a59a6e086af4954dc99b
--- /dev/null
+++ b/utils/emd/setup.py
@@ -0,0 +1,14 @@
+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+setup(
+    name='emd',
+    ext_modules=[
+        CUDAExtension('emd', [
+            'emd.cpp',
+            'emd_cuda.cu',
+        ]),
+    ],
+    cmdclass={
+        'build_ext': BuildExtension
+    })
\ No newline at end of file
diff --git a/utils/general_utils.py b/utils/general_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..29abf43a6635c28c66aaee897fbba6b1d778d350
--- /dev/null
+++ b/utils/general_utils.py
@@ -0,0 +1,211 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import sys
+from datetime import datetime
+import numpy as np
+import random
+
+def inverse_sigmoid(x):
+    return torch.log(x/(1-x))
+
+def PILtoTorch(pil_image, resolution):
+    resized_image_PIL = pil_image.resize(resolution)
+    resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0
+    if len(resized_image.shape) == 3:
+        return resized_image.permute(2, 0, 1)
+    else:
+        return resized_image.unsqueeze(dim=-1).permute(2, 0, 1)
+
+def get_expon_lr_func(
+    lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000
+):
+    """
+    Copied from Plenoxels
+
+    Continuous learning rate decay function. Adapted from JaxNeRF
+    The returned rate is lr_init when step=0 and lr_final when step=max_steps, and
+    is log-linearly interpolated elsewhere (equivalent to exponential decay).
+    If lr_delay_steps>0 then the learning rate will be scaled by some smooth
+    function of lr_delay_mult, such that the initial learning rate is
+    lr_init*lr_delay_mult at the beginning of optimization but will be eased back
+    to the normal learning rate when steps>lr_delay_steps.
+    :param conf: config subtree 'lr' or similar
+    :param max_steps: int, the number of steps during optimization.
+    :return HoF which takes step as input
+    """
+
+    def helper(step):
+        if step < 0 or (lr_init == 0.0 and lr_final == 0.0):
+            # Disable this parameter
+            return 0.0
+        if lr_delay_steps > 0:
+            # A kind of reverse cosine decay.
+            delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
+                0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
+            )
+        else:
+            delay_rate = 1.0
+        t = np.clip(step / max_steps, 0, 1)
+        log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
+        return delay_rate * log_lerp
+
+    return helper
+
+def strip_lowerdiag(L):
+    uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
+
+    uncertainty[:, 0] = L[:, 0, 0]
+    uncertainty[:, 1] = L[:, 0, 1]
+    uncertainty[:, 2] = L[:, 0, 2]
+    uncertainty[:, 3] = L[:, 1, 1]
+    uncertainty[:, 4] = L[:, 1, 2]
+    uncertainty[:, 5] = L[:, 2, 2]
+    return uncertainty
+
+def strip_symmetric(sym):
+    return strip_lowerdiag(sym)
+
+def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
+    """
+    From Pytorch3d
+    Convert a unit quaternion to a standard form: one in which the real
+    part is non negative.
+
+    Args:
+        quaternions: Quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Standardized quaternions as tensor of shape (..., 4).
+    """
+    return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
+
+def quaternion_raw_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    """
+    From Pytorch3d
+    Multiply two quaternions.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+
+    Returns:
+        The product of a and b, a tensor of quaternions shape (..., 4).
+    """
+    aw, ax, ay, az = torch.unbind(a, -1)
+    bw, bx, by, bz = torch.unbind(b, -1)
+    ow = aw * bw - ax * bx - ay * by - az * bz
+    ox = aw * bx + ax * bw + ay * bz - az * by
+    oy = aw * by - ax * bz + ay * bw + az * bx
+    oz = aw * bz + ax * by - ay * bx + az * bw
+    return torch.stack((ow, ox, oy, oz), -1)
+
+# Matrix to quaternion does not come under NVIDIA Copyright
+# Written by Stan Szymanowicz 2023
+def matrix_to_quaternion(M: torch.Tensor) -> torch.Tensor:
+    """
+    Matrix-to-quaternion conversion method. Equation taken from 
+    https://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/index.htm
+    Args:
+        M: rotation matrices, (3 x 3)
+    Returns:
+        q: quaternion of shape (4)
+    """
+    tr = 1 + M[ 0, 0] + M[ 1, 1] + M[ 2, 2]
+
+    if tr > 0:
+        r = torch.sqrt(tr) / 2.0
+        x = ( M[ 2, 1] - M[ 1, 2] ) / ( 4 * r )
+        y = ( M[ 0, 2] - M[ 2, 0] ) / ( 4 * r )
+        z = ( M[ 1, 0] - M[ 0, 1] ) / ( 4 * r )
+    elif ( M[ 0, 0] > M[ 1, 1]) and (M[ 0, 0] > M[ 2, 2]):
+        S = torch.sqrt(1.0 + M[ 0, 0] - M[ 1, 1] - M[ 2, 2]) * 2 # S=4*qx 
+        r = (M[ 2, 1] - M[ 1, 2]) / S
+        x = 0.25 * S
+        y = (M[ 0, 1] + M[ 1, 0]) / S 
+        z = (M[ 0, 2] + M[ 2, 0]) / S 
+    elif M[ 1, 1] > M[ 2, 2]: 
+        S = torch.sqrt(1.0 + M[ 1, 1] - M[ 0, 0] - M[ 2, 2]) * 2 # S=4*qy
+        r = (M[ 0, 2] - M[ 2, 0]) / S
+        x = (M[ 0, 1] + M[ 1, 0]) / S
+        y = 0.25 * S
+        z = (M[ 1, 2] + M[ 2, 1]) / S
+    else:
+        S = torch.sqrt(1.0 + M[ 2, 2] - M[ 0, 0] -  M[ 1, 1]) * 2 # S=4*qz
+        r = (M[ 1, 0] - M[ 0, 1]) / S
+        x = (M[ 0, 2] + M[ 2, 0]) / S
+        y = (M[ 1, 2] + M[ 2, 1]) / S
+        z = 0.25 * S
+
+    return torch.stack([r, x, y, z], dim=-1)
+
+def build_rotation(r):
+    norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])
+
+    q = r / norm[:, None]
+
+    R = torch.zeros((q.size(0), 3, 3), device='cuda')
+
+    r = q[:, 0]
+    x = q[:, 1]
+    y = q[:, 2]
+    z = q[:, 3]
+
+    R[:, 0, 0] = 1 - 2 * (y*y + z*z)
+    R[:, 0, 1] = 2 * (x*y - r*z)
+    R[:, 0, 2] = 2 * (x*z + r*y)
+    R[:, 1, 0] = 2 * (x*y + r*z)
+    R[:, 1, 1] = 1 - 2 * (x*x + z*z)
+    R[:, 1, 2] = 2 * (y*z - r*x)
+    R[:, 2, 0] = 2 * (x*z - r*y)
+    R[:, 2, 1] = 2 * (y*z + r*x)
+    R[:, 2, 2] = 1 - 2 * (x*x + y*y)
+    return R
+
+def build_scaling_rotation(s, r):
+    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
+    R = build_rotation(r)
+
+    L[:,0,0] = s[:,0]
+    L[:,1,1] = s[:,1]
+    L[:,2,2] = s[:,2]
+
+    L = R @ L
+    return L
+
+def safe_state(cfg, silent=False):
+    old_f = sys.stdout
+    class F:
+        def __init__(self, silent):
+            self.silent = silent
+
+        def write(self, x):
+            if not self.silent:
+                if x.endswith("\n"):
+                    old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S")))))
+                else:
+                    old_f.write(x)
+
+        def flush(self):
+            old_f.flush()
+
+    sys.stdout = F(silent)
+
+    random.seed(cfg.general.random_seed)
+    np.random.seed(cfg.general.random_seed)
+    torch.manual_seed(cfg.general.random_seed)
+    device = torch.device("cuda:{}".format(cfg.general.device))
+    torch.cuda.set_device(device)
+
+    return device
diff --git a/utils/geometry.py b/utils/geometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e88b38602ae00d9c20343f21efb019b8fba1cc0
--- /dev/null
+++ b/utils/geometry.py
@@ -0,0 +1,55 @@
+import torch
+
+
+def index(feat, uv):
+    '''
+
+    :param feat: [B, C, H, W] image features
+    :param uv: [B, 2, N] uv coordinates in the image plane, range [-1, 1]
+    :return: [B, C, N] image features at the uv coordinates
+    '''
+    uv = uv.transpose(1, 2)  # [B, N, 2]
+    uv = uv.unsqueeze(2)  # [B, N, 1, 2]
+    # NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample
+    # for old versions, simply remove the aligned_corners argument.
+    samples = torch.nn.functional.grid_sample(feat, uv, align_corners=True)  # [B, C, N, 1]
+    return samples[:, :, :, 0]  # [B, C, N]
+
+
+def orthogonal(points, calibrations, transforms=None):
+    '''
+    Compute the orthogonal projections of 3D points into the image plane by given projection matrix
+    :param points: [B, 3, N] Tensor of 3D points
+    :param calibrations: [B, 4, 4] Tensor of projection matrix
+    :param transforms: [B, 2, 3] Tensor of image transform matrix
+    :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
+    '''
+    rot = calibrations[:, :3, :3]
+    trans = calibrations[:, :3, 3:4]
+    pts = torch.baddbmm(trans, rot, points)  # [B, 3, N]
+    if transforms is not None:
+        scale = transforms[:2, :2]
+        shift = transforms[:2, 2:3]
+        pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
+    return pts
+
+
+def perspective(points, calibrations, transforms=None):
+    '''
+    Compute the perspective projections of 3D points into the image plane by given projection matrix
+    :param points: [Bx3xN] Tensor of 3D points
+    :param calibrations: [Bx4x4] Tensor of projection matrix
+    :param transforms: [Bx2x3] Tensor of image transform matrix
+    :return: xy: [Bx2xN] Tensor of xy coordinates in the image plane
+    '''
+    rot = calibrations[:, :3, :3]
+    trans = calibrations[:, :3, 3:4]
+    homo = torch.baddbmm(trans, rot, points)  # [B, 3, N]
+    xy = homo[:, :2, :] / homo[:, 2:3, :]
+    if transforms is not None:
+        scale = transforms[:2, :2]
+        shift = transforms[:2, 2:3]
+        xy = torch.baddbmm(shift, scale, xy)
+
+    xyz = torch.cat([xy, homo[:, 2:3, :]], 1)
+    return xyz
diff --git a/utils/grid_sample_gradfix.py b/utils/grid_sample_gradfix.py
new file mode 100644
index 0000000000000000000000000000000000000000..23930166b872bb425c232fdfa35c96ef5c2286a9
--- /dev/null
+++ b/utils/grid_sample_gradfix.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+"""Custom replacement for `torch.nn.functional.grid_sample` that
+supports arbitrarily high order gradients between the input and output.
+Only works on 2D images and assumes
+`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
+
+import torch
+from pkg_resources import parse_version
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False  # Enable the custom op by setting this to true.
+_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version(
+    '1.11.0a')  # Allow prerelease builds of 1.11
+
+#----------------------------------------------------------------------------
+
+
+def grid_sample(input, grid):
+    if _should_use_custom_op():
+        return _GridSample2dForward.apply(input, grid)
+    return torch.nn.functional.grid_sample(input=input,
+                                           grid=grid,
+                                           mode='bilinear',
+                                           padding_mode='zeros',
+                                           align_corners=False)
+
+
+#----------------------------------------------------------------------------
+
+
+def _should_use_custom_op():
+    return enabled
+
+
+#----------------------------------------------------------------------------
+
+
+class _GridSample2dForward(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, input, grid):
+        assert input.ndim == 4
+        assert grid.ndim == 4
+        output = torch.nn.functional.grid_sample(input=input,
+                                                 grid=grid,
+                                                 mode='bilinear',
+                                                 padding_mode='zeros',
+                                                 align_corners=False)
+        ctx.save_for_backward(input, grid)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, grid = ctx.saved_tensors
+        grad_input, grad_grid = _GridSample2dBackward.apply(
+            grad_output, input, grid)
+        return grad_input, grad_grid
+
+
+#----------------------------------------------------------------------------
+
+
+class _GridSample2dBackward(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, grad_output, input, grid):
+        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
+        if _use_pytorch_1_11_api:
+            output_mask = (ctx.needs_input_grad[1], ctx.needs_input_grad[2])
+            grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False,
+                                       output_mask)
+        else:
+            grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
+        ctx.save_for_backward(grid)
+        return grad_input, grad_grid
+
+    @staticmethod
+    def backward(ctx, grad2_grad_input, grad2_grad_grid):
+        _ = grad2_grad_grid  # unused
+        grid, = ctx.saved_tensors
+        grad2_grad_output = None
+        grad2_input = None
+        grad2_grid = None
+
+        if ctx.needs_input_grad[0]:
+            grad2_grad_output = _GridSample2dForward.apply(
+                grad2_grad_input, grid)
+
+        # st()
+        # ? why
+        # assert not ctx.needs_input_grad[2]
+
+        return grad2_grad_output, grad2_input, grad2_grid
diff --git a/utils/gs_utils/__pycache__/graphics_utils.cpython-39.pyc b/utils/gs_utils/__pycache__/graphics_utils.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1e8ccc71f3a814de8a49a4966b8fc61397aff835
Binary files /dev/null and b/utils/gs_utils/__pycache__/graphics_utils.cpython-39.pyc differ
diff --git a/utils/gs_utils/camera_utils.py b/utils/gs_utils/camera_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a54d0ada0361997109c462cde1e088ea5da9ff2
--- /dev/null
+++ b/utils/gs_utils/camera_utils.py
@@ -0,0 +1,82 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+from scene.cameras import Camera
+import numpy as np
+from utils.general_utils import PILtoTorch
+from utils.graphics_utils import fov2focal
+
+WARNED = False
+
+def loadCam(args, id, cam_info, resolution_scale):
+    orig_w, orig_h = cam_info.image.size
+
+    if args.resolution in [1, 2, 4, 8]:
+        resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution))
+    else:  # should be a type that converts to float
+        if args.resolution == -1:
+            if orig_w > 1600:
+                global WARNED
+                if not WARNED:
+                    print("[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n "
+                        "If this is not desired, please explicitly specify '--resolution/-r' as 1")
+                    WARNED = True
+                global_down = orig_w / 1600
+            else:
+                global_down = 1
+        else:
+            global_down = orig_w / args.resolution
+
+        scale = float(global_down) * float(resolution_scale)
+        resolution = (int(orig_w / scale), int(orig_h / scale))
+
+    resized_image_rgb = PILtoTorch(cam_info.image, resolution)
+
+    gt_image = resized_image_rgb[:3, ...]
+    loaded_mask = None
+
+    if resized_image_rgb.shape[1] == 4:
+        loaded_mask = resized_image_rgb[3:4, ...]
+
+    return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, 
+                  FoVx=cam_info.FovX, FoVy=cam_info.FovY, 
+                  image=gt_image, gt_alpha_mask=loaded_mask,
+                  image_name=cam_info.image_name, uid=id, data_device=args.data_device)
+
+def cameraList_from_camInfos(cam_infos, resolution_scale, args):
+    camera_list = []
+
+    for id, c in enumerate(cam_infos):
+        camera_list.append(loadCam(args, id, c, resolution_scale))
+
+    return camera_list
+
+def camera_to_JSON(id, camera : Camera):
+    Rt = np.zeros((4, 4))
+    Rt[:3, :3] = camera.R.transpose()
+    Rt[:3, 3] = camera.T
+    Rt[3, 3] = 1.0
+
+    W2C = np.linalg.inv(Rt)
+    pos = W2C[:3, 3]
+    rot = W2C[:3, :3]
+    serializable_array_2d = [x.tolist() for x in rot]
+    camera_entry = {
+        'id' : id,
+        'img_name' : camera.image_name,
+        'width' : camera.width,
+        'height' : camera.height,
+        'position': pos.tolist(),
+        'rotation': serializable_array_2d,
+        'fy' : fov2focal(camera.FovY, camera.height),
+        'fx' : fov2focal(camera.FovX, camera.width)
+    }
+    return camera_entry
diff --git a/utils/gs_utils/general_utils.py b/utils/gs_utils/general_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..541c0825229a2d86e84460b765879f86f724a59d
--- /dev/null
+++ b/utils/gs_utils/general_utils.py
@@ -0,0 +1,133 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import sys
+from datetime import datetime
+import numpy as np
+import random
+
+def inverse_sigmoid(x):
+    return torch.log(x/(1-x))
+
+def PILtoTorch(pil_image, resolution):
+    resized_image_PIL = pil_image.resize(resolution)
+    resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0
+    if len(resized_image.shape) == 3:
+        return resized_image.permute(2, 0, 1)
+    else:
+        return resized_image.unsqueeze(dim=-1).permute(2, 0, 1)
+
+def get_expon_lr_func(
+    lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000
+):
+    """
+    Copied from Plenoxels
+
+    Continuous learning rate decay function. Adapted from JaxNeRF
+    The returned rate is lr_init when step=0 and lr_final when step=max_steps, and
+    is log-linearly interpolated elsewhere (equivalent to exponential decay).
+    If lr_delay_steps>0 then the learning rate will be scaled by some smooth
+    function of lr_delay_mult, such that the initial learning rate is
+    lr_init*lr_delay_mult at the beginning of optimization but will be eased back
+    to the normal learning rate when steps>lr_delay_steps.
+    :param conf: config subtree 'lr' or similar
+    :param max_steps: int, the number of steps during optimization.
+    :return HoF which takes step as input
+    """
+
+    def helper(step):
+        if step < 0 or (lr_init == 0.0 and lr_final == 0.0):
+            # Disable this parameter
+            return 0.0
+        if lr_delay_steps > 0:
+            # A kind of reverse cosine decay.
+            delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
+                0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
+            )
+        else:
+            delay_rate = 1.0
+        t = np.clip(step / max_steps, 0, 1)
+        log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
+        return delay_rate * log_lerp
+
+    return helper
+
+def strip_lowerdiag(L):
+    uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
+
+    uncertainty[:, 0] = L[:, 0, 0]
+    uncertainty[:, 1] = L[:, 0, 1]
+    uncertainty[:, 2] = L[:, 0, 2]
+    uncertainty[:, 3] = L[:, 1, 1]
+    uncertainty[:, 4] = L[:, 1, 2]
+    uncertainty[:, 5] = L[:, 2, 2]
+    return uncertainty
+
+def strip_symmetric(sym):
+    return strip_lowerdiag(sym)
+
+def build_rotation(r):
+    norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])
+
+    q = r / norm[:, None]
+
+    R = torch.zeros((q.size(0), 3, 3), device='cuda')
+
+    r = q[:, 0]
+    x = q[:, 1]
+    y = q[:, 2]
+    z = q[:, 3]
+
+    R[:, 0, 0] = 1 - 2 * (y*y + z*z)
+    R[:, 0, 1] = 2 * (x*y - r*z)
+    R[:, 0, 2] = 2 * (x*z + r*y)
+    R[:, 1, 0] = 2 * (x*y + r*z)
+    R[:, 1, 1] = 1 - 2 * (x*x + z*z)
+    R[:, 1, 2] = 2 * (y*z - r*x)
+    R[:, 2, 0] = 2 * (x*z - r*y)
+    R[:, 2, 1] = 2 * (y*z + r*x)
+    R[:, 2, 2] = 1 - 2 * (x*x + y*y)
+    return R
+
+def build_scaling_rotation(s, r):
+    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
+    R = build_rotation(r)
+
+    L[:,0,0] = s[:,0]
+    L[:,1,1] = s[:,1]
+    L[:,2,2] = s[:,2]
+
+    L = R @ L
+    return L
+
+def safe_state(silent):
+    old_f = sys.stdout
+    class F:
+        def __init__(self, silent):
+            self.silent = silent
+
+        def write(self, x):
+            if not self.silent:
+                if x.endswith("\n"):
+                    old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S")))))
+                else:
+                    old_f.write(x)
+
+        def flush(self):
+            old_f.flush()
+
+    sys.stdout = F(silent)
+
+    random.seed(0)
+    np.random.seed(0)
+    torch.manual_seed(0)
+    torch.cuda.set_device(torch.device("cuda:0"))
diff --git a/utils/gs_utils/graphics_utils.py b/utils/gs_utils/graphics_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb2659360987236de470acda9ec581cd8d53456
--- /dev/null
+++ b/utils/gs_utils/graphics_utils.py
@@ -0,0 +1,91 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import math
+import numpy as np
+from typing import NamedTuple
+
+class BasicPointCloud(NamedTuple):
+    points : np.array
+    colors : np.array
+    normals : np.array
+
+def geom_transform_points(points, transf_matrix):
+    P, _ = points.shape
+    ones = torch.ones(P, 1, dtype=points.dtype, device=points.device)
+    points_hom = torch.cat([points, ones], dim=1)
+    points_out = torch.matmul(points_hom, transf_matrix.unsqueeze(0))
+
+    denom = points_out[..., 3:] + 0.0000001
+    return (points_out[..., :3] / denom).squeeze(dim=0)
+
+def getWorld2View(R, t):
+    Rt = np.zeros((4, 4))
+    Rt[:3, :3] = R.transpose()
+    Rt[:3, 3] = t
+    Rt[3, 3] = 1.0
+    return np.float32(Rt)
+
+def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):
+    Rt = np.zeros((4, 4))
+    Rt[:3, :3] = R.transpose()
+    Rt[:3, 3] = t
+    Rt[3, 3] = 1.0
+
+    C2W = np.linalg.inv(Rt)
+    cam_center = C2W[:3, 3]
+    cam_center = (cam_center + translate) * scale
+    C2W[:3, 3] = cam_center
+    Rt = np.linalg.inv(C2W)
+    return np.float32(Rt)
+
+def getView2World(R, t, translate=np.array([.0, .0, .0]), scale=1.0):
+    Rt = np.zeros((4, 4))
+    Rt[:3, :3] = R.transpose()
+    Rt[:3, 3] = t
+    Rt[3, 3] = 1.0
+
+    C2W = np.linalg.inv(Rt)
+    cam_center = C2W[:3, 3]
+    cam_center = (cam_center + translate) * scale
+    C2W[:3, 3] = cam_center
+    Rt = C2W
+    return np.float32(Rt)
+
+
+def getProjectionMatrix(znear, zfar, fovX, fovY):
+    tanHalfFovY = math.tan((fovY / 2))
+    tanHalfFovX = math.tan((fovX / 2))
+
+    top = tanHalfFovY * znear
+    bottom = -top
+    right = tanHalfFovX * znear
+    left = -right
+
+    P = torch.zeros(4, 4)
+
+    z_sign = 1.0
+
+    P[0, 0] = 2.0 * znear / (right - left)
+    P[1, 1] = 2.0 * znear / (top - bottom)
+    P[0, 2] = (right + left) / (right - left)
+    P[1, 2] = (top + bottom) / (top - bottom)
+    P[3, 2] = z_sign
+    P[2, 2] = z_sign * zfar / (zfar - znear)
+    P[2, 3] = -(zfar * znear) / (zfar - znear)
+    return P
+
+def fov2focal(fov, pixels):
+    return pixels / (2 * math.tan(fov / 2))
+
+def focal2fov(focal, pixels):
+    return 2*math.atan(pixels/(2*focal))
\ No newline at end of file
diff --git a/utils/gs_utils/image_utils.py b/utils/gs_utils/image_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdeaa1b6d250e549181ab165070f82ccd31b3eb9
--- /dev/null
+++ b/utils/gs_utils/image_utils.py
@@ -0,0 +1,19 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+
+def mse(img1, img2):
+    return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
+
+def psnr(img1, img2):
+    mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
+    return 20 * torch.log10(1.0 / torch.sqrt(mse))
diff --git a/utils/gs_utils/loss_utils.py b/utils/gs_utils/loss_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9defc23a913e5d861aa5adc63270050884923094
--- /dev/null
+++ b/utils/gs_utils/loss_utils.py
@@ -0,0 +1,64 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+from math import exp
+
+def l1_loss(network_output, gt):
+    return torch.abs((network_output - gt)).mean()
+
+def l2_loss(network_output, gt):
+    return ((network_output - gt) ** 2).mean()
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+def ssim(img1, img2, window_size=11, size_average=True):
+    channel = img1.size(-3)
+    window = create_window(window_size, channel)
+
+    if img1.is_cuda:
+        window = window.cuda(img1.get_device())
+    window = window.type_as(img1)
+
+    return _ssim(img1, img2, window, window_size, channel, size_average)
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    C1 = 0.01 ** 2
+    C2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1).mean(1).mean(1)
+
diff --git a/utils/gs_utils/sh_utils.py b/utils/gs_utils/sh_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbca7d192aa3a7edf8c5b2d24dee535eac765785
--- /dev/null
+++ b/utils/gs_utils/sh_utils.py
@@ -0,0 +1,118 @@
+#  Copyright 2021 The PlenOctree Authors.
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#  this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#  this list of conditions and the following disclaimer in the documentation
+#  and/or other materials provided with the distribution.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+
+import torch
+
+C0 = 0.28209479177387814
+C1 = 0.4886025119029199
+C2 = [
+    1.0925484305920792,
+    -1.0925484305920792,
+    0.31539156525252005,
+    -1.0925484305920792,
+    0.5462742152960396
+]
+C3 = [
+    -0.5900435899266435,
+    2.890611442640554,
+    -0.4570457994644658,
+    0.3731763325901154,
+    -0.4570457994644658,
+    1.445305721320277,
+    -0.5900435899266435
+]
+C4 = [
+    2.5033429417967046,
+    -1.7701307697799304,
+    0.9461746957575601,
+    -0.6690465435572892,
+    0.10578554691520431,
+    -0.6690465435572892,
+    0.47308734787878004,
+    -1.7701307697799304,
+    0.6258357354491761,
+]   
+
+
+def eval_sh(deg, sh, dirs):
+    """
+    Evaluate spherical harmonics at unit directions
+    using hardcoded SH polynomials.
+    Works with torch/np/jnp.
+    ... Can be 0 or more batch dimensions.
+    Args:
+        deg: int SH deg. Currently, 0-3 supported
+        sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2]
+        dirs: jnp.ndarray unit directions [..., 3]
+    Returns:
+        [..., C]
+    """
+    assert deg <= 4 and deg >= 0
+    coeff = (deg + 1) ** 2
+    assert sh.shape[-1] >= coeff
+
+    result = C0 * sh[..., 0]
+    if deg > 0:
+        x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3]
+        result = (result -
+                C1 * y * sh[..., 1] +
+                C1 * z * sh[..., 2] -
+                C1 * x * sh[..., 3])
+
+        if deg > 1:
+            xx, yy, zz = x * x, y * y, z * z
+            xy, yz, xz = x * y, y * z, x * z
+            result = (result +
+                    C2[0] * xy * sh[..., 4] +
+                    C2[1] * yz * sh[..., 5] +
+                    C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] +
+                    C2[3] * xz * sh[..., 7] +
+                    C2[4] * (xx - yy) * sh[..., 8])
+
+            if deg > 2:
+                result = (result +
+                C3[0] * y * (3 * xx - yy) * sh[..., 9] +
+                C3[1] * xy * z * sh[..., 10] +
+                C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] +
+                C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] +
+                C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] +
+                C3[5] * z * (xx - yy) * sh[..., 14] +
+                C3[6] * x * (xx - 3 * yy) * sh[..., 15])
+
+                if deg > 3:
+                    result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] +
+                            C4[1] * yz * (3 * xx - yy) * sh[..., 17] +
+                            C4[2] * xy * (7 * zz - 1) * sh[..., 18] +
+                            C4[3] * yz * (7 * zz - 3) * sh[..., 19] +
+                            C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] +
+                            C4[5] * xz * (7 * zz - 3) * sh[..., 21] +
+                            C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] +
+                            C4[7] * xz * (xx - 3 * yy) * sh[..., 23] +
+                            C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24])
+    return result
+
+def RGB2SH(rgb):
+    return (rgb - 0.5) / C0
+
+def SH2RGB(sh):
+    return sh * C0 + 0.5
\ No newline at end of file
diff --git a/utils/gs_utils/system_utils.py b/utils/gs_utils/system_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..90ca6d7f77610c967affe313398777cd86920e8e
--- /dev/null
+++ b/utils/gs_utils/system_utils.py
@@ -0,0 +1,28 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+from errno import EEXIST
+from os import makedirs, path
+import os
+
+def mkdir_p(folder_path):
+    # Creates a directory. equivalent to using mkdir -p on the command line
+    try:
+        makedirs(folder_path)
+    except OSError as exc: # Python >2.5
+        if exc.errno == EEXIST and path.isdir(folder_path):
+            pass
+        else:
+            raise
+
+def searchForMaxIteration(folder):
+    saved_iters = [int(fname.split("_")[-1]) for fname in os.listdir(folder)]
+    return max(saved_iters)
diff --git a/utils/infer_utils.py b/utils/infer_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7022e6ac355bbfdb969f64d933b6d020cdec2f1
--- /dev/null
+++ b/utils/infer_utils.py
@@ -0,0 +1,70 @@
+# copied from InstantMesh
+import os
+import imageio
+import rembg
+import torch
+import numpy as np
+import PIL.Image
+from PIL import Image
+from typing import Any
+
+
+def resize_to_224(img):
+    img = transforms.functional.resize(img, 224,
+        interpolation=transforms.InterpolationMode.LANCZOS)
+    return img
+
+def remove_background(image: PIL.Image.Image,
+    rembg_session: Any = None,
+    force: bool = False,
+    **rembg_kwargs,
+) -> PIL.Image.Image:
+    do_remove = True
+    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+        do_remove = False
+    do_remove = do_remove or force
+    if do_remove:
+        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+    return image
+
+
+def resize_foreground(
+    image: PIL.Image.Image,
+    ratio: float,
+) -> PIL.Image.Image:
+    image = np.array(image)
+    assert image.shape[-1] == 4
+    alpha = np.where(image[..., 3] > 0)
+    y1, y2, x1, x2 = (
+        alpha[0].min(),
+        alpha[0].max(),
+        alpha[1].min(),
+        alpha[1].max(),
+    )
+    # crop the foreground
+    fg = image[y1:y2, x1:x2]
+    # pad to square
+    size = max(fg.shape[0], fg.shape[1])
+    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
+    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
+    new_image = np.pad(
+        fg,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+
+    # compute padding according to the ratio
+    new_size = int(new_image.shape[0] / ratio)
+    # pad to size, double side
+    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
+    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
+    new_image = np.pad(
+        new_image,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+    new_image = PIL.Image.fromarray(new_image)
+    return new_image
+
diff --git a/utils/mesh_util.py b/utils/mesh_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..a998c0578ab8e4f483d6080637578cf59b3a33d7
--- /dev/null
+++ b/utils/mesh_util.py
@@ -0,0 +1,279 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+import xatlas
+import trimesh
+import cv2
+import numpy as np
+from PIL import Image
+from functools import partial
+import open3d as o3d
+import trimesh
+
+
+# https://github.com/hbb1/2d-gaussian-splatting/blob/19eb5f1e091a582e911b4282fe2832bac4c89f0f/utils/mesh_utils.py#L22C1-L43C18
+# def post_process_mesh(mesh, cluster_to_keep=1000):
+def post_process_mesh(mesh, cluster_to_keep=None):
+    """
+    Post-process a mesh to filter out floaters and disconnected parts
+    """
+    import copy
+    mesh_0 = copy.deepcopy(mesh)
+    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
+            triangle_clusters, cluster_n_triangles, cluster_area = (mesh_0.cluster_connected_triangles())
+
+    cluster_to_keep = min(len(cluster_n_triangles),10)
+
+    triangle_clusters = np.asarray(triangle_clusters)
+    cluster_n_triangles = np.asarray(cluster_n_triangles)
+    cluster_area = np.asarray(cluster_area)
+    n_cluster = np.sort(cluster_n_triangles.copy())[-cluster_to_keep]
+    n_cluster = max(n_cluster, 50) # filter meshes smaller than 50
+    triangles_to_remove = cluster_n_triangles[triangle_clusters] < n_cluster
+    mesh_0.remove_triangles_by_mask(triangles_to_remove)
+    mesh_0.remove_unreferenced_vertices()
+    mesh_0.remove_degenerate_triangles()
+    # print("num vertices raw {}".format(len(mesh.vertices)))
+    # print("num vertices post {}".format(len(mesh_0.vertices)))
+    return mesh_0
+
+def smooth_mesh(mesh):
+    import copy
+    mesh_0 = copy.deepcopy(mesh)
+    mesh_0 = mesh_0.filter_smooth_taubin(12)
+    return mesh_0
+
+
+def to_cam_open3d(viewpoint_stack):
+    camera_traj = []
+    for i, viewpoint_cam in enumerate(viewpoint_stack):
+        W = viewpoint_cam.image_width
+        H = viewpoint_cam.image_height
+        ndc2pix = torch.tensor([
+            [W / 2, 0, 0, (W-1) / 2],
+            [0, H / 2, 0, (H-1) / 2],
+            [0, 0, 0, 1]]).float().cuda().T
+        intrins =  (viewpoint_cam.projection_matrix @ ndc2pix)[:3,:3].T
+        intrinsic=o3d.camera.PinholeCameraIntrinsic(
+            width=viewpoint_cam.image_width,
+            height=viewpoint_cam.image_height,
+            cx = intrins[0,2].item(),
+            cy = intrins[1,2].item(), 
+            fx = intrins[0,0].item(), 
+            fy = intrins[1,1].item()
+        )
+
+        extrinsic=np.asarray((viewpoint_cam.world_view_transform.T).cpu().numpy())
+        camera = o3d.camera.PinholeCameraParameters()
+        camera.extrinsic = extrinsic
+        camera.intrinsic = intrinsic
+        camera_traj.append(camera)
+
+    return camera_traj
+
+def to_cam_open3d_compat(gs_foramt_c):
+    W = H = image_width = image_height = 512
+    projection_matrix = gs_foramt_c['projection_matrix']
+    world_view_transform = gs_foramt_c['cam_view']
+
+    # camera_traj = []
+
+    # for i, viewpoint_cam in enumerate(viewpoint_stack):
+        # W = viewpoint_cam.image_width
+        # H = viewpoint_cam.image_height
+    ndc2pix = torch.tensor([
+        [W / 2, 0, 0, (W-1) / 2],
+        [0, H / 2, 0, (H-1) / 2],
+        [0, 0, 0, 1]]).float().T
+    intrins =  (projection_matrix @ ndc2pix)[:3,:3].T
+    intrinsic=o3d.camera.PinholeCameraIntrinsic(
+        width=image_width,
+        height=image_height,
+        cx = intrins[0,2].item(),
+        cy = intrins[1,2].item(), 
+        fx = intrins[0,0].item(), 
+        fy = intrins[1,1].item()
+    )
+
+    extrinsic=np.asarray((world_view_transform.T).cpu().numpy())
+    camera = o3d.camera.PinholeCameraParameters()
+    camera.extrinsic = extrinsic
+    camera.intrinsic = intrinsic
+
+    # camera_traj.append(camera)
+    return camera
+
+    # return camera_traj
+
+def save_obj(pointnp_px3, facenp_fx3, colornp_px3, fpath):
+
+    pointnp_px3 = pointnp_px3 @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]])
+    facenp_fx3 = facenp_fx3[:, [2, 1, 0]]
+
+    mesh = trimesh.Trimesh(
+        vertices=pointnp_px3, 
+        faces=facenp_fx3, 
+        vertex_colors=colornp_px3,
+    )
+    mesh.export(fpath, 'obj')
+
+
+def save_glb(pointnp_px3, facenp_fx3, colornp_px3, fpath):
+
+    pointnp_px3 = pointnp_px3 @ np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]])
+
+    mesh = trimesh.Trimesh(
+        vertices=pointnp_px3, 
+        faces=facenp_fx3, 
+        vertex_colors=colornp_px3,
+    )
+    mesh.export(fpath, 'glb')
+
+
+def save_obj_with_mtl(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, texmap_hxwx3, fname):
+    import os
+    fol, na = os.path.split(fname)
+    na, _ = os.path.splitext(na)
+
+    matname = '%s/%s.mtl' % (fol, na)
+    fid = open(matname, 'w')
+    fid.write('newmtl material_0\n')
+    fid.write('Kd 1 1 1\n')
+    fid.write('Ka 0 0 0\n')
+    fid.write('Ks 0.4 0.4 0.4\n')
+    fid.write('Ns 10\n')
+    fid.write('illum 2\n')
+    fid.write('map_Kd %s.png\n' % na)
+    fid.close()
+    ####
+
+    fid = open(fname, 'w')
+    fid.write('mtllib %s.mtl\n' % na)
+
+    for pidx, p in enumerate(pointnp_px3):
+        pp = p
+        fid.write('v %f %f %f\n' % (pp[0], pp[1], pp[2]))
+
+    for pidx, p in enumerate(tcoords_px2):
+        pp = p
+        fid.write('vt %f %f\n' % (pp[0], pp[1]))
+
+    fid.write('usemtl material_0\n')
+    for i, f in enumerate(facenp_fx3):
+        f1 = f + 1
+        f2 = facetex_fx3[i] + 1
+        fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
+    fid.close()
+
+    # save texture map
+    lo, hi = 0, 1
+    img = np.asarray(texmap_hxwx3, dtype=np.float32)
+    img = (img - lo) * (255 / (hi - lo))
+    img = img.clip(0, 255)
+    mask = np.sum(img.astype(np.float32), axis=-1, keepdims=True)
+    mask = (mask <= 3.0).astype(np.float32)
+    kernel = np.ones((3, 3), 'uint8')
+    dilate_img = cv2.dilate(img, kernel, iterations=1)
+    img = img * (1 - mask) + dilate_img * mask
+    img = img.clip(0, 255).astype(np.uint8)
+    Image.fromarray(np.ascontiguousarray(img[::-1, :, :]), 'RGB').save(f'{fol}/{na}.png')
+
+
+def loadobj(meshfile):
+    v = []
+    f = []
+    meshfp = open(meshfile, 'r')
+    for line in meshfp.readlines():
+        data = line.strip().split(' ')
+        data = [da for da in data if len(da) > 0]
+        if len(data) != 4:
+            continue
+        if data[0] == 'v':
+            v.append([float(d) for d in data[1:]])
+        if data[0] == 'f':
+            data = [da.split('/')[0] for da in data]
+            f.append([int(d) for d in data[1:]])
+    meshfp.close()
+
+    # torch need int64
+    facenp_fx3 = np.array(f, dtype=np.int64) - 1
+    pointnp_px3 = np.array(v, dtype=np.float32)
+    return pointnp_px3, facenp_fx3
+
+
+def loadobjtex(meshfile):
+    v = []
+    vt = []
+    f = []
+    ft = []
+    meshfp = open(meshfile, 'r')
+    for line in meshfp.readlines():
+        data = line.strip().split(' ')
+        data = [da for da in data if len(da) > 0]
+        if not ((len(data) == 3) or (len(data) == 4) or (len(data) == 5)):
+            continue
+        if data[0] == 'v':
+            assert len(data) == 4
+
+            v.append([float(d) for d in data[1:]])
+        if data[0] == 'vt':
+            if len(data) == 3 or len(data) == 4:
+                vt.append([float(d) for d in data[1:3]])
+        if data[0] == 'f':
+            data = [da.split('/') for da in data]
+            if len(data) == 4:
+                f.append([int(d[0]) for d in data[1:]])
+                ft.append([int(d[1]) for d in data[1:]])
+            elif len(data) == 5:
+                idx1 = [1, 2, 3]
+                data1 = [data[i] for i in idx1]
+                f.append([int(d[0]) for d in data1])
+                ft.append([int(d[1]) for d in data1])
+                idx2 = [1, 3, 4]
+                data2 = [data[i] for i in idx2]
+                f.append([int(d[0]) for d in data2])
+                ft.append([int(d[1]) for d in data2])
+    meshfp.close()
+
+    # torch need int64
+    facenp_fx3 = np.array(f, dtype=np.int64) - 1
+    ftnp_fx3 = np.array(ft, dtype=np.int64) - 1
+    pointnp_px3 = np.array(v, dtype=np.float32)
+    uvs = np.array(vt, dtype=np.float32)
+    return pointnp_px3, facenp_fx3, uvs, ftnp_fx3
+
+
+# ==============================================================================================
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    import nvdiffrast.torch as dr
+    return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
+
+
+def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution):
+    import nvdiffrast.torch as dr
+    vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy())
+
+    # Convert to tensors
+    indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
+
+    uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
+    mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
+    # mesh_v_tex. ture
+    uv_clip = uvs[None, ...] * 2.0 - 1.0
+
+    # pad to four component coordinate
+    uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
+
+    # rasterize
+    rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution))
+
+    # Interpolate world space position
+    gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
+    mask = rast[..., 3:4] > 0
+    return uvs, mesh_tex_idx, gb_pos, mask
diff --git a/utils/nerf_utils.py b/utils/nerf_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f80bb2f63f43ac7b6ad3c8664061350efe9477d5
--- /dev/null
+++ b/utils/nerf_utils.py
@@ -0,0 +1,66 @@
+# https://github.com/yenchenlin/nerf-pytorch/blob/63a5a630c9abd62b0f21c08703d0ac2ea7d4b9dd/run_nerf_helpers.py#L14
+
+import torch
+# torch.autograd.set_detect_anomaly(True)
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+# Misc
+img2mse = lambda x, y : torch.mean((x - y) ** 2)
+mse2psnr = lambda x : -10. * torch.log(x) / torch.log(torch.Tensor([10.]))
+to8b = lambda x : (255*np.clip(x,0,1)).astype(np.uint8)
+
+
+# Positional encoding (section 5.1)
+class Embedder:
+    def __init__(self, **kwargs):
+        self.kwargs = kwargs
+        self.create_embedding_fn()
+        
+    def create_embedding_fn(self):
+        embed_fns = []
+        d = self.kwargs['input_dims']
+        out_dim = 0
+        if self.kwargs['include_input']:
+            embed_fns.append(lambda x : x)
+            out_dim += d
+            
+        max_freq = self.kwargs['max_freq_log2']
+        N_freqs = self.kwargs['num_freqs']
+        
+        if self.kwargs['log_sampling']:
+            freq_bands = 2.**torch.linspace(0., max_freq, steps=N_freqs)
+        else:
+            freq_bands = torch.linspace(2.**0., 2.**max_freq, steps=N_freqs)
+            
+        for freq in freq_bands:
+            for p_fn in self.kwargs['periodic_fns']:
+                embed_fns.append(lambda x, p_fn=p_fn, freq=freq : p_fn(x * freq))
+                out_dim += d
+                    
+        self.embed_fns = embed_fns
+        self.out_dim = out_dim
+        
+    def embed(self, inputs):
+        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
+
+
+def get_embedder(multires=10, i=0):
+    if i == -1:
+        return nn.Identity(), 3
+    
+    embed_kwargs = {
+                'include_input' : True,
+                'input_dims' : 3,
+                'max_freq_log2' : multires-1,
+                'num_freqs' : multires,
+                'log_sampling' : True,
+                'periodic_fns' : [torch.sin, torch.cos],
+    }
+    
+    embedder_obj = Embedder(**embed_kwargs)
+    embed = lambda x, eo=embedder_obj : eo.embed(x)
+    return embed, embedder_obj.out_dim
+
diff --git a/utils/point_utils.py b/utils/point_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..219eb1b2f729bc54049f4792eba822e0b98b10b2
--- /dev/null
+++ b/utils/point_utils.py
@@ -0,0 +1,78 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import os, cv2
+import matplotlib.pyplot as plt
+import math
+import point_cloud_utils as pcu
+from pdb import set_trace as st
+
+def depths_to_points_2(world_view_transform, tanfov, W, H, depthmap):
+    c2w = (world_view_transform).inverse()
+    # W, H = view.image_width, view.image_height
+
+    # fx = W / (2 * math.tan(view.FoVx / 2.))
+    # fy = H / (2 * math.tan(view.FoVy / 2.))
+
+    fx = W / (2 * tanfov)
+    fy = H / (2 * tanfov)
+
+    intrins = torch.tensor(
+        [[fx, 0., W/2.],
+        [0., fy, H/2.],
+        [0., 0., 1.0]]
+    ).float().cuda()
+    grid_x, grid_y = torch.meshgrid(torch.arange(W, device='cuda').float(), torch.arange(H, device='cuda').float(), indexing='xy')
+    points = torch.stack([grid_x, grid_y, torch.ones_like(grid_x)], dim=-1).reshape(-1, 3)
+    rays_d = points @ intrins.inverse().T @ c2w[:3,:3].T
+    rays_o = c2w[:3,3]
+    points = depthmap.reshape(-1, 1) * rays_d + rays_o
+    return points
+
+
+
+def depths_to_points(view, depthmap):
+    c2w = (view.world_view_transform.T).inverse()
+    W, H = view.image_width, view.image_height
+    fx = W / (2 * math.tan(view.FoVx / 2.))
+    fy = H / (2 * math.tan(view.FoVy / 2.))
+    intrins = torch.tensor(
+        [[fx, 0., W/2.],
+        [0., fy, H/2.],
+        [0., 0., 1.0]]
+    ).float().cuda()
+    grid_x, grid_y = torch.meshgrid(torch.arange(W, device='cuda').float(), torch.arange(H, device='cuda').float(), indexing='xy')
+    points = torch.stack([grid_x, grid_y, torch.ones_like(grid_x)], dim=-1).reshape(-1, 3)
+    rays_d = points @ intrins.inverse().T @ c2w[:3,:3].T
+    rays_o = c2w[:3,3]
+    points = depthmap.reshape(-1, 1) * rays_d + rays_o
+    return points
+
+def depth_to_normal(view, depth):
+    """
+        view: view camera
+        depth: depthmap 
+    """
+    points = depths_to_points(view, depth).reshape(*depth.shape[1:], 3)
+    output = torch.zeros_like(points)
+    dx = torch.cat([points[2:, 1:-1] - points[:-2, 1:-1]], dim=0)
+    dy = torch.cat([points[1:-1, 2:] - points[1:-1, :-2]], dim=1)
+    normal_map = torch.nn.functional.normalize(torch.cross(dx, dy, dim=-1), dim=-1)
+    output[1:-1, 1:-1, :] = normal_map
+    return output
+
+def depth_to_normal_2(world_view_transform, tanfov, W, H, depth):
+    """
+        view: view camera
+        depth: depthmap 
+    """
+    points = depths_to_points_2(world_view_transform, tanfov, W, H, depth).reshape(*depth.shape[1:], 3)
+    # st()
+    # pcu.save_mesh_v( f'tmp/depth2pts.ply', points.detach().reshape(-1,3).cpu().numpy(),)
+    output = torch.zeros_like(points)
+    dx = torch.cat([points[2:, 1:-1] - points[:-2, 1:-1]], dim=0)
+    dy = torch.cat([points[1:-1, 2:] - points[1:-1, :-2]], dim=1)
+    normal_map = torch.nn.functional.normalize(torch.cross(dx, dy, dim=-1), dim=-1)
+    output[1:-1, 1:-1, :] = normal_map
+    return output
\ No newline at end of file
diff --git a/utils/profile_frombuffer.py b/utils/profile_frombuffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6049bfb723813cb3e505f5f077555c5f79ad0590
--- /dev/null
+++ b/utils/profile_frombuffer.py
@@ -0,0 +1,26 @@
+import numpy as np
+from numba import jit
+import time
+
+# Numba-optimized function without reshape
+@jit(nopython=True)
+def from_buffer_fast(buf):
+    return np.frombuffer(buf, dtype=np.uint8)
+
+# Benchmarking function
+def benchmark(func, buf):
+    start_time = time.time()
+    func(buf)
+    end_time = time.time()
+    return end_time - start_time
+
+# Generate random buffer and dtype
+buf = np.random.randint(0, 255, size=10**6, dtype=np.uint8).tobytes()
+
+# Benchmark np.frombuffer
+original_time = benchmark(np.frombuffer, buf)
+print("np.frombuffer time:", original_time)
+
+# Benchmark Numba-optimized function without reshape
+numba_time = benchmark(from_buffer_fast, buf)
+print("Numba-optimized function time:", numba_time)
diff --git a/vit/__init__.py b/vit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d23d4dd3f4db3ac37eaaea955baa09659b60172c
--- /dev/null
+++ b/vit/__init__.py
@@ -0,0 +1,3 @@
+# modified from DINO, CLIP, ViTGAN, etc.
+
+# todo, load pretrained model for triplane encoder init
\ No newline at end of file
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diff --git a/vit/utils.py b/vit/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e7418c22e4a315216c5185f189d039a33923b3f
--- /dev/null
+++ b/vit/utils.py
@@ -0,0 +1,881 @@
+# https://raw.githubusercontent.com/facebookresearch/dino/main/utils.py
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Misc functions.
+
+Mostly copy-paste from torchvision references or other public repos like DETR:
+https://github.com/facebookresearch/detr/blob/master/util/misc.py
+"""
+import os
+import sys
+import time
+import math
+import random
+import datetime
+import subprocess
+from collections import defaultdict, deque
+
+import numpy as np
+import torch
+from torch import nn
+import torch.distributed as dist
+from PIL import ImageFilter, ImageOps
+
+
+class GaussianBlur(object):
+    """
+    Apply Gaussian Blur to the PIL image.
+    """
+    def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
+        self.prob = p
+        self.radius_min = radius_min
+        self.radius_max = radius_max
+
+    def __call__(self, img):
+        do_it = random.random() <= self.prob
+        if not do_it:
+            return img
+
+        return img.filter(
+            ImageFilter.GaussianBlur(
+                radius=random.uniform(self.radius_min, self.radius_max)))
+
+
+class Solarization(object):
+    """
+    Apply Solarization to the PIL image.
+    """
+    def __init__(self, p):
+        self.p = p
+
+    def __call__(self, img):
+        if random.random() < self.p:
+            return ImageOps.solarize(img)
+        else:
+            return img
+
+
+def load_pretrained_weights(model, pretrained_weights, checkpoint_key,
+                            model_name, patch_size):
+    if os.path.isfile(pretrained_weights):
+        state_dict = torch.load(pretrained_weights, map_location="cpu")
+        if checkpoint_key is not None and checkpoint_key in state_dict:
+            print(f"Take key {checkpoint_key} in provided checkpoint dict")
+            state_dict = state_dict[checkpoint_key]
+        # remove `module.` prefix
+        state_dict = {
+            k.replace("module.", ""): v
+            for k, v in state_dict.items()
+        }
+        # remove `backbone.` prefix induced by multicrop wrapper
+        state_dict = {
+            k.replace("backbone.", ""): v
+            for k, v in state_dict.items()
+        }
+        msg = model.load_state_dict(state_dict, strict=False)
+        print('Pretrained weights found at {} and loaded with msg: {}'.format(
+            pretrained_weights, msg))
+    else:
+        print(
+            "Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate."
+        )
+        url = None
+        if model_name == "vit_small" and patch_size == 16:
+            url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth"
+        elif model_name == "vit_small" and patch_size == 8:
+            url = "dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth"
+        elif model_name == "vit_base" and patch_size == 16:
+            url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth"
+        elif model_name == "vit_base" and patch_size == 8:
+            url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth"
+        elif model_name == "xcit_small_12_p16":
+            url = "dino_xcit_small_12_p16_pretrain/dino_xcit_small_12_p16_pretrain.pth"
+        elif model_name == "xcit_small_12_p8":
+            url = "dino_xcit_small_12_p8_pretrain/dino_xcit_small_12_p8_pretrain.pth"
+        elif model_name == "xcit_medium_24_p16":
+            url = "dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth"
+        elif model_name == "xcit_medium_24_p8":
+            url = "dino_xcit_medium_24_p8_pretrain/dino_xcit_medium_24_p8_pretrain.pth"
+        elif model_name == "resnet50":
+            url = "dino_resnet50_pretrain/dino_resnet50_pretrain.pth"
+        if url is not None:
+            print(
+                "Since no pretrained weights have been provided, we load the reference pretrained DINO weights."
+            )
+            state_dict = torch.hub.load_state_dict_from_url(
+                url="https://dl.fbaipublicfiles.com/dino/" + url)
+            model.load_state_dict(state_dict, strict=True)
+        else:
+            print(
+                "There is no reference weights available for this model => We use random weights."
+            )
+
+
+def load_pretrained_linear_weights(linear_classifier, model_name, patch_size):
+    url = None
+    if model_name == "vit_small" and patch_size == 16:
+        url = "dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth"
+    elif model_name == "vit_small" and patch_size == 8:
+        url = "dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth"
+    elif model_name == "vit_base" and patch_size == 16:
+        url = "dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth"
+    elif model_name == "vit_base" and patch_size == 8:
+        url = "dino_vitbase8_pretrain/dino_vitbase8_linearweights.pth"
+    elif model_name == "resnet50":
+        url = "dino_resnet50_pretrain/dino_resnet50_linearweights.pth"
+    if url is not None:
+        print("We load the reference pretrained linear weights.")
+        state_dict = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/dino/" + url)["state_dict"]
+        linear_classifier.load_state_dict(state_dict, strict=True)
+    else:
+        print("We use random linear weights.")
+
+
+def clip_gradients(model, clip):
+    norms = []
+    for name, p in model.named_parameters():
+        if p.grad is not None:
+            param_norm = p.grad.data.norm(2)
+            norms.append(param_norm.item())
+            clip_coef = clip / (param_norm + 1e-6)
+            if clip_coef < 1:
+                p.grad.data.mul_(clip_coef)
+    return norms
+
+
+def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
+    if epoch >= freeze_last_layer:
+        return
+    for n, p in model.named_parameters():
+        if "last_layer" in n:
+            p.grad = None
+
+
+def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
+    """
+    Re-start from checkpoint
+    """
+    if not os.path.isfile(ckp_path):
+        return
+    print("Found checkpoint at {}".format(ckp_path))
+
+    # open checkpoint file
+    checkpoint = torch.load(ckp_path, map_location="cpu")
+
+    # key is what to look for in the checkpoint file
+    # value is the object to load
+    # example: {'state_dict': model}
+    for key, value in kwargs.items():
+        if key in checkpoint and value is not None:
+            try:
+                msg = value.load_state_dict(checkpoint[key], strict=False)
+                print("=> loaded '{}' from checkpoint '{}' with msg {}".format(
+                    key, ckp_path, msg))
+            except TypeError:
+                try:
+                    msg = value.load_state_dict(checkpoint[key])
+                    print("=> loaded '{}' from checkpoint: '{}'".format(
+                        key, ckp_path))
+                except ValueError:
+                    print(
+                        "=> failed to load '{}' from checkpoint: '{}'".format(
+                            key, ckp_path))
+        else:
+            print("=> key '{}' not found in checkpoint: '{}'".format(
+                key, ckp_path))
+
+    # re load variable important for the run
+    if run_variables is not None:
+        for var_name in run_variables:
+            if var_name in checkpoint:
+                run_variables[var_name] = checkpoint[var_name]
+
+
+def cosine_scheduler(base_value,
+                     final_value,
+                     epochs,
+                     niter_per_ep,
+                     warmup_epochs=0,
+                     start_warmup_value=0):
+    warmup_schedule = np.array([])
+    warmup_iters = warmup_epochs * niter_per_ep
+    if warmup_epochs > 0:
+        warmup_schedule = np.linspace(start_warmup_value, base_value,
+                                      warmup_iters)
+
+    iters = np.arange(epochs * niter_per_ep - warmup_iters)
+    schedule = final_value + 0.5 * (base_value - final_value) * (
+        1 + np.cos(np.pi * iters / len(iters)))
+
+    schedule = np.concatenate((warmup_schedule, schedule))
+    assert len(schedule) == epochs * niter_per_ep
+    return schedule
+
+
+def bool_flag(s):
+    """
+    Parse boolean arguments from the command line.
+    """
+    FALSY_STRINGS = {"off", "false", "0"}
+    TRUTHY_STRINGS = {"on", "true", "1"}
+    if s.lower() in FALSY_STRINGS:
+        return False
+    elif s.lower() in TRUTHY_STRINGS:
+        return True
+    else:
+        raise argparse.ArgumentTypeError("invalid value for a boolean flag")
+
+
+def fix_random_seeds(seed=31):
+    """
+    Fix random seeds.
+    """
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.6f} ({global_avg:.6f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total],
+                         dtype=torch.float64,
+                         device='cuda')
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(median=self.median,
+                               avg=self.avg,
+                               global_avg=self.global_avg,
+                               max=self.max,
+                               value=self.value)
+
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that all processes
+    have the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.all_reduce(values)
+        if average:
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(
+            type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append("{}: {}".format(name, str(meter)))
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None):
+        i = 0
+        if not header:
+            header = ''
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt='{avg:.6f}')
+        data_time = SmoothedValue(fmt='{avg:.6f}')
+        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join([
+                header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}',
+                'time: {time}', 'data: {data}', 'max mem: {memory:.0f}'
+            ])
+        else:
+            log_msg = self.delimiter.join([
+                header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}',
+                'time: {time}', 'data: {data}'
+            ])
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(
+                        log_msg.format(i,
+                                       len(iterable),
+                                       eta=eta_string,
+                                       meters=str(self),
+                                       time=str(iter_time),
+                                       data=str(data_time)))
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print('{} Total time: {} ({:.6f} s / it)'.format(
+            header, total_time_str, total_time / len(iterable)))
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command,
+                                       cwd=cwd).decode('ascii').strip()
+
+    sha = 'N/A'
+    diff = "clean"
+    branch = 'N/A'
+    try:
+        sha = _run(['git', 'rev-parse', 'HEAD'])
+        subprocess.check_output(['git', 'diff'], cwd=cwd)
+        diff = _run(['git', 'diff-index', 'HEAD'])
+        diff = "has uncommited changes" if diff else "clean"
+        branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop('force', False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def init_distributed_mode(args):
+    # launched with torch.distributed.launch
+    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.gpu = int(os.environ['LOCAL_RANK'])
+    # launched with submitit on a slurm cluster
+    elif 'SLURM_PROCID' in os.environ:
+        args.rank = int(os.environ['SLURM_PROCID'])
+        args.gpu = args.rank % torch.cuda.device_count()
+    # launched naively with `python main_dino.py`
+    # we manually add MASTER_ADDR and MASTER_PORT to env variables
+    elif torch.cuda.is_available():
+        print('Will run the code on one GPU.')
+        args.rank, args.gpu, args.world_size = 0, 0, 1
+        os.environ['MASTER_ADDR'] = '127.0.0.1'
+        os.environ['MASTER_PORT'] = '29500'
+    else:
+        print('Does not support training without GPU.')
+        sys.exit(1)
+
+    dist.init_process_group(
+        backend="nccl",
+        init_method=args.dist_url,
+        world_size=args.world_size,
+        rank=args.rank,
+    )
+
+    torch.cuda.set_device(args.gpu)
+    print('| distributed init (rank {}): {}'.format(args.rank, args.dist_url),
+          flush=True)
+    dist.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+def accuracy(output, target, topk=(1, )):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    maxk = max(topk)
+    batch_size = target.size(0)
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.reshape(1, -1).expand_as(pred))
+    return [
+        correct[:k].reshape(-1).float().sum(0) * 100. / batch_size
+        for k in topk
+    ]
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+class LARS(torch.optim.Optimizer):
+    """
+    Almost copy-paste from https://github.com/facebookresearch/barlowtwins/blob/main/main.py
+    """
+    def __init__(self,
+                 params,
+                 lr=0,
+                 weight_decay=0,
+                 momentum=0.9,
+                 eta=0.001,
+                 weight_decay_filter=None,
+                 lars_adaptation_filter=None):
+        defaults = dict(lr=lr,
+                        weight_decay=weight_decay,
+                        momentum=momentum,
+                        eta=eta,
+                        weight_decay_filter=weight_decay_filter,
+                        lars_adaptation_filter=lars_adaptation_filter)
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self):
+        for g in self.param_groups:
+            for p in g['params']:
+                dp = p.grad
+
+                if dp is None:
+                    continue
+
+                if p.ndim != 1:
+                    dp = dp.add(p, alpha=g['weight_decay'])
+
+                if p.ndim != 1:
+                    param_norm = torch.norm(p)
+                    update_norm = torch.norm(dp)
+                    one = torch.ones_like(param_norm)
+                    q = torch.where(
+                        param_norm > 0.,
+                        torch.where(update_norm > 0,
+                                    (g['eta'] * param_norm / update_norm),
+                                    one), one)
+                    dp = dp.mul(q)
+
+                param_state = self.state[p]
+                if 'mu' not in param_state:
+                    param_state['mu'] = torch.zeros_like(p)
+                mu = param_state['mu']
+                mu.mul_(g['momentum']).add_(dp)
+
+                p.add_(mu, alpha=-g['lr'])
+
+
+class MultiCropWrapper(nn.Module):
+    """
+    Perform forward pass separately on each resolution input.
+    The inputs corresponding to a single resolution are clubbed and single
+    forward is run on the same resolution inputs. Hence we do several
+    forward passes = number of different resolutions used. We then
+    concatenate all the output features and run the head forward on these
+    concatenated features.
+    """
+    def __init__(self, backbone, head):
+        super(MultiCropWrapper, self).__init__()
+        # disable layers dedicated to ImageNet labels classification
+        backbone.fc, backbone.head = nn.Identity(), nn.Identity()
+        self.backbone = backbone
+        self.head = head
+
+    def forward(self, x):
+        # convert to list
+        if not isinstance(x, list):
+            x = [x]
+        idx_crops = torch.cumsum(
+            torch.unique_consecutive(
+                torch.tensor([inp.shape[-1] for inp in x]),
+                return_counts=True,
+            )[1], 0)
+        start_idx, output = 0, torch.empty(0).to(x[0].device)
+        for end_idx in idx_crops:
+            _out = self.backbone(torch.cat(x[start_idx:end_idx]))
+            # The output is a tuple with XCiT model. See:
+            # https://github.com/facebookresearch/xcit/blob/master/xcit.py#L404-L405
+            if isinstance(_out, tuple):
+                _out = _out[0]
+            # accumulate outputs
+            output = torch.cat((output, _out))
+            start_idx = end_idx
+        # Run the head forward on the concatenated features.
+        return self.head(output)
+
+
+def get_params_groups(model):
+    regularized = []
+    not_regularized = []
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue
+        # we do not regularize biases nor Norm parameters
+        if name.endswith(".bias") or len(param.shape) == 1:
+            not_regularized.append(param)
+        else:
+            regularized.append(param)
+    return [{
+        'params': regularized
+    }, {
+        'params': not_regularized,
+        'weight_decay': 0.
+    }]
+
+
+def has_batchnorms(model):
+    bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d,
+                nn.SyncBatchNorm)
+    for name, module in model.named_modules():
+        if isinstance(module, bn_types):
+            return True
+    return False
+
+
+class PCA():
+    """
+    Class to  compute and apply PCA.
+    """
+    def __init__(self, dim=256, whit=0.5):
+        self.dim = dim
+        self.whit = whit
+        self.mean = None
+
+    def train_pca(self, cov):
+        """
+        Takes a covariance matrix (np.ndarray) as input.
+        """
+        d, v = np.linalg.eigh(cov)
+        eps = d.max() * 1e-5
+        n_0 = (d < eps).sum()
+        if n_0 > 0:
+            d[d < eps] = eps
+
+        # total energy
+        totenergy = d.sum()
+
+        # sort eigenvectors with eigenvalues order
+        idx = np.argsort(d)[::-1][:self.dim]
+        d = d[idx]
+        v = v[:, idx]
+
+        print("keeping %.2f %% of the energy" % (d.sum() / totenergy * 100.0))
+
+        # for the whitening
+        d = np.diag(1. / d**self.whit)
+
+        # principal components
+        self.dvt = np.dot(d, v.T)
+
+    def apply(self, x):
+        # input is from numpy
+        if isinstance(x, np.ndarray):
+            if self.mean is not None:
+                x -= self.mean
+            return np.dot(self.dvt, x.T).T
+
+        # input is from torch and is on GPU
+        if x.is_cuda:
+            if self.mean is not None:
+                x -= torch.cuda.FloatTensor(self.mean)
+            return torch.mm(torch.cuda.FloatTensor(self.dvt),
+                            x.transpose(0, 1)).transpose(0, 1)
+
+        # input if from torch, on CPU
+        if self.mean is not None:
+            x -= torch.FloatTensor(self.mean)
+        return torch.mm(torch.FloatTensor(self.dvt),
+                        x.transpose(0, 1)).transpose(0, 1)
+
+
+def compute_ap(ranks, nres):
+    """
+    Computes average precision for given ranked indexes.
+    Arguments
+    ---------
+    ranks : zerro-based ranks of positive images
+    nres  : number of positive images
+    Returns
+    -------
+    ap    : average precision
+    """
+
+    # number of images ranked by the system
+    nimgranks = len(ranks)
+
+    # accumulate trapezoids in PR-plot
+    ap = 0
+
+    recall_step = 1. / nres
+
+    for j in np.arange(nimgranks):
+        rank = ranks[j]
+
+        if rank == 0:
+            precision_0 = 1.
+        else:
+            precision_0 = float(j) / rank
+
+        precision_1 = float(j + 1) / (rank + 1)
+
+        ap += (precision_0 + precision_1) * recall_step / 2.
+
+    return ap
+
+
+def compute_map(ranks, gnd, kappas=[]):
+    """
+    Computes the mAP for a given set of returned results.
+         Usage:
+           map = compute_map (ranks, gnd)
+                 computes mean average precsion (map) only
+           map, aps, pr, prs = compute_map (ranks, gnd, kappas)
+                 computes mean average precision (map), average precision (aps) for each query
+                 computes mean precision at kappas (pr), precision at kappas (prs) for each query
+         Notes:
+         1) ranks starts from 0, ranks.shape = db_size X #queries
+         2) The junk results (e.g., the query itself) should be declared in the gnd stuct array
+         3) If there are no positive images for some query, that query is excluded from the evaluation
+    """
+
+    map = 0.
+    nq = len(gnd)  # number of queries
+    aps = np.zeros(nq)
+    pr = np.zeros(len(kappas))
+    prs = np.zeros((nq, len(kappas)))
+    nempty = 0
+
+    for i in np.arange(nq):
+        qgnd = np.array(gnd[i]['ok'])
+
+        # no positive images, skip from the average
+        if qgnd.shape[0] == 0:
+            aps[i] = float('nan')
+            prs[i, :] = float('nan')
+            nempty += 1
+            continue
+
+        try:
+            qgndj = np.array(gnd[i]['junk'])
+        except:
+            qgndj = np.empty(0)
+
+        # sorted positions of positive and junk images (0 based)
+        pos = np.arange(ranks.shape[0])[np.in1d(ranks[:, i], qgnd)]
+        junk = np.arange(ranks.shape[0])[np.in1d(ranks[:, i], qgndj)]
+
+        k = 0
+        ij = 0
+        if len(junk):
+            # decrease positions of positives based on the number of
+            # junk images appearing before them
+            ip = 0
+            while (ip < len(pos)):
+                while (ij < len(junk) and pos[ip] > junk[ij]):
+                    k += 1
+                    ij += 1
+                pos[ip] = pos[ip] - k
+                ip += 1
+
+        # compute ap
+        ap = compute_ap(pos, len(qgnd))
+        map = map + ap
+        aps[i] = ap
+
+        # compute precision @ k
+        pos += 1  # get it to 1-based
+        for j in np.arange(len(kappas)):
+            kq = min(max(pos), kappas[j])
+            prs[i, j] = (pos <= kq).sum() / kq
+        pr = pr + prs[i, :]
+
+    map = map / (nq - nempty)
+    pr = pr / (nq - nempty)
+
+    return map, aps, pr, prs
+
+
+def multi_scale(samples, model):
+    v = None
+    for s in [1, 1 / 2**(1 / 2), 1 / 2]:  # we use 3 different scales
+        if s == 1:
+            inp = samples.clone()
+        else:
+            inp = nn.functional.interpolate(samples,
+                                            scale_factor=s,
+                                            mode='bilinear',
+                                            align_corners=False)
+        feats = model(inp).clone()
+        if v is None:
+            v = feats
+        else:
+            v += feats
+    v /= 3
+    v /= v.norm()
+    return v
\ No newline at end of file
diff --git a/vit/vision_transformer.py b/vit/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d359fdff56639363694058b31bf960a551aa7bcb
--- /dev/null
+++ b/vit/vision_transformer.py
@@ -0,0 +1,2988 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Mostly copy-paste from timm library.
+https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+"""
+from copy import deepcopy
+from typing import List, Optional, Tuple
+import math
+from functools import partial
+from sympy import flatten
+
+import torch
+import torch.nn as nn
+from torch import Tensor, pixel_shuffle
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+from torch.nn.modules import GELU
+
+import torch.utils.benchmark as benchmark
+def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
+    t0 = benchmark.Timer(
+        stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
+    )
+    return t0.blocked_autorange().mean * 1e6
+
+# from vit.vision_transformer import Conv3DCrossAttentionBlock
+
+from .utils import trunc_normal_
+
+from pdb import set_trace as st
+# import apex
+# from apex.normalization import FusedRMSNorm as RMSNorm
+try:
+    from apex.normalization import FusedRMSNorm as RMSNorm
+except:
+    from dit.norm import RMSNorm
+
+from torch.nn import LayerNorm
+
+try:
+    import xformers
+    import xformers.ops
+    from xformers.ops import memory_efficient_attention, unbind, fmha
+    from xformers.ops import MemoryEfficientAttentionFlashAttentionOp, MemoryEfficientAttentionCutlassOp
+    # from xformers.ops import RMSNorm
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    # logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+from packaging import version
+assert version.parse(torch.__version__) >= version.parse("2.0.0")
+SDP_IS_AVAILABLE = True
+# from torch.backends.cuda import SDPBackend, sdp_kernel
+from torch.nn.attention import sdpa_kernel, SDPBackend
+
+
+class Attention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0., 
+                 enable_rmsnorm=False,
+                 qk_norm=False,
+                 no_flash_op=False,
+                 enable_rope=False,):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        # https://github.com/huggingface/pytorch-image-models/blob/5dce71010174ad6599653da4e8ba37fd5f9fa572/timm/models/vision_transformer.py#L79C1-L80C78
+
+        self.enable_rope = enable_rope
+        # st()
+
+        if enable_rope:
+            self.q_norm = RMSNorm(dim, elementwise_affine=True) if qk_norm else nn.Identity()
+            self.k_norm = RMSNorm(dim, elementwise_affine=True) if qk_norm else nn.Identity()
+        else:
+            self.q_norm = RMSNorm(head_dim, elementwise_affine=True) if qk_norm else nn.Identity()
+            self.k_norm = RMSNorm(head_dim, elementwise_affine=True) if qk_norm else nn.Identity()
+
+        # if qk_norm:
+        #     self.q_norm = LayerNorm(dim, eps=1e-5)
+        #     self.k_norm = LayerNorm(dim, eps=1e-5)
+        self.qk_norm = qk_norm
+        self.no_flash_op = no_flash_op
+        self.attn_mode = "torch"
+         
+        self.backend = SDPBackend.FLASH_ATTENTION # FA implemented by torch.
+
+    @staticmethod
+    def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+        """
+        Reshape frequency tensor for broadcasting it with another tensor.
+
+        This function reshapes the frequency tensor to have the same shape as
+        the target tensor 'x' for the purpose of broadcasting the frequency
+        tensor during element-wise operations.
+
+        Args:
+            freqs_cis (torch.Tensor): Frequency tensor to be reshaped.
+            x (torch.Tensor): Target tensor for broadcasting compatibility.
+
+        Returns:
+            torch.Tensor: Reshaped frequency tensor.
+
+        Raises:
+            AssertionError: If the frequency tensor doesn't match the expected
+                shape.
+            AssertionError: If the target tensor 'x' doesn't have the expected
+                number of dimensions.
+        """
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert freqs_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis.view(*shape)
+
+    @staticmethod
+    def apply_rotary_emb(
+        xq: torch.Tensor,
+        xk: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Apply rotary embeddings to input tensors using the given frequency
+        tensor.
+
+        This function applies rotary embeddings to the given query 'xq' and
+        key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
+        input tensors are reshaped as complex numbers, and the frequency tensor
+        is reshaped for broadcasting compatibility. The resulting tensors
+        contain rotary embeddings and are returned as real tensors.
+
+        Args:
+            xq (torch.Tensor): Query tensor to apply rotary embeddings.
+            xk (torch.Tensor): Key tensor to apply rotary embeddings.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
+                exponentials.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
+                and key tensor with rotary embeddings.
+        """
+        with torch.cuda.amp.autocast(enabled=False):
+            xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+            xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+            freqs_cis = Attention.reshape_for_broadcast(freqs_cis, xq_)
+            xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+            xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+            return xq_out.type_as(xq), xk_out.type_as(xk)
+
+    def forward(self, x):
+        # B, N, C = x.shape
+        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
+        #                           C // self.num_heads).permute(2, 0, 3, 1, 4)
+        # q, k, v = qkv[0], qkv[1], qkv[2]
+
+        # attn = (q @ k.transpose(-2, -1)) * self.scale
+        # attn = attn.softmax(dim=-1)
+        # attn = self.attn_drop(attn)
+
+        # x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        # return x, attn
+
+        # https://github.com/Stability-AI/generative-models/blob/863665548f95ff827273948766a3f732ab01bc49/sgm/modules/attention.py#L179
+        B, L, C = x.shape
+
+        qkv = self.qkv(x)
+        if self.attn_mode == "torch":
+            qkv = rearrange(
+                qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads
+            ).float()
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            q, k = self.q_norm(q), self.k_norm(k)
+
+            with sdpa_kernel([self.backend]): # new signature
+
+                x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+                 
+            del q, k, v
+            x = rearrange(x, "B H L D -> B L (H D)")
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        return x
+
+
+class MemEffAttention(Attention):
+
+    def forward(self, x: Tensor, attn_bias=None, freqs_cis=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x)
+
+        dtype = qkv.dtype
+
+        if self.enable_rope:
+            assert freqs_cis is not None
+
+            qkv = qkv.reshape(B, N, 3, C)
+            q, k, v = unbind(qkv, 2)
+            q, k = self.q_norm(q), self.k_norm(k) # do q-k norm on the full seq instead.
+
+            st()
+            q, k = Attention.apply_rotary_emb(q, k, freqs_cis=freqs_cis)
+
+            q = q.reshape(B, N, self.num_heads, C // self.num_heads)
+            k = k.reshape(B, N, self.num_heads, C // self.num_heads)
+
+            q, k, v = map(
+                lambda t: t.reshape(b, N, self.num_heads, C // self.num_heads)
+                (q, k, v),
+            )
+            q, k = q.to(dtype), k.to(dtype)
+
+
+
+        else:
+            qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads)
+            q, k, v = unbind(qkv, 2)
+            q, k = self.q_norm(q), self.k_norm(k)
+
+        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) # if not bf16, no flash-attn here.
+        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp) # force flash attention
+
+        if self.no_flash_op: # F-A does not support large batch size? force cutlas?
+            # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionCutlassOp) # force flash attention
+
+            if version.parse(xformers.__version__) >= version.parse("0.0.21"):
+                # NOTE: workaround for
+                # https://github.com/facebookresearch/xformers/issues/845
+                # def attn(max_bs, op):
+
+                max_bs = 32768
+                L = q.shape[0]
+                n_batches = math.ceil(L / max_bs)
+                x = list()
+                for i_batch in range(n_batches):
+                    batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs)
+                    x.append(
+                        xformers.ops.memory_efficient_attention(
+                            q[batch],
+                            k[batch],
+                            v[batch],
+                            attn_bias=None,
+                            # op=MemoryEfficientAttentionFlashAttentionOp,
+                            # op=op,
+                            op=MemoryEfficientAttentionCutlassOp,
+                        )
+                    )
+                x = torch.cat(x, 0)
+                # return x
+                 
+                # The cutlas implementation runs in 8396.681 microseconds
+                # The Flash implementation runs in 19473.491 microseconds
+
+                # max_bs = 32768
+                # math_time = benchmark_torch_function_in_microseconds(attn, max_bs, MemoryEfficientAttentionCutlassOp)
+                # print(f"The cutlas implementation runs in {math_time:.3f} microseconds")
+
+                # max_bs = 32768 // 2 # works for flash attention
+                # math_time = benchmark_torch_function_in_microseconds(attn, max_bs, MemoryEfficientAttentionFlashAttentionOp)
+                # print(f"The Flash implementation runs in {math_time:.3f} microseconds")
+                # st()
+                # pass
+
+        else: # will enable flash attention by default.
+            # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp) # force flash attention
+            x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) # force flash attention
+
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+class MemEffCrossAttention(MemEffAttention):
+    # for cross attention, where context serves as k and v
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0, proj_drop=0):
+        super().__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop, proj_drop)
+        del self.qkv
+        self.q = nn.Linear(dim, dim * 1, bias=qkv_bias)
+        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+
+    def forward(self, x: Tensor, context: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            return super().forward(x)
+
+        B, N, C = x.shape
+        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q = self.q(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+        kv = self.kv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        k, v = unbind(kv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+# https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
+class CrossAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.wq = nn.Linear(dim, dim, bias=qkv_bias)
+        self.wk = nn.Linear(dim, dim, bias=qkv_bias)
+        self.wv = nn.Linear(dim, dim, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+
+        B, N, C = x.shape
+        q = self.wq(x[:,
+                      0:1, ...]).reshape(B, 1, self.num_heads,
+                                         C // self.num_heads).permute(
+                                             0, 2, 1,
+                                             3)  # B1C -> B1H(C/H) -> BH1(C/H)
+        k = self.wk(x).reshape(B, N,
+                               self.num_heads, C // self.num_heads).permute(
+                                   0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)
+        v = self.wv(x).reshape(B, N,
+                               self.num_heads, C // self.num_heads).permute(
+                                   0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)
+
+        attn = (q @ k.transpose(
+            -2, -1)) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(
+            B, 1, C)  # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Conv3D_Aware_CrossAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.wq = nn.Linear(dim, dim, bias=qkv_bias)
+        self.wk = nn.Linear(dim, dim, bias=qkv_bias)
+        self.wv = nn.Linear(dim, dim, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+
+        B, group_size, N, C = x.shape  # B 3 N C
+        p = int(N**0.5)  # patch size
+        assert p**2 == N, 'check input dim, no [cls] needed here'
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.reshape(B, group_size, p, p, C)  # expand patch token dim
+
+        # * init qkv
+        # q = torch.empty(B * group_size * N,
+        #                 1,
+        #                 self.num_heads,
+        #                 C // self.num_heads,
+        #                 device=x.device).permute(0, 2, 1, 3)
+        # k = torch.empty(B * group_size * N,
+        #                 2 * p,
+        #                 self.num_heads,
+        #                 C // self.num_heads,
+        #                 device=x.device).permute(0, 2, 1, 3)
+        # v = torch.empty_like(k)
+
+        q_x = torch.empty(
+            B * group_size * N,
+            1,
+            # self.num_heads,
+            # C // self.num_heads,
+            C,
+            device=x.device)
+        k_x = torch.empty(
+            B * group_size * N,
+            2 * p,
+            # self.num_heads,
+            # C // self.num_heads,
+            C,
+            device=x.device)
+        v_x = torch.empty_like(k_x)
+
+        # ! refer to the following plane order
+        # N, M, _ = coordinates.shape
+        # xy_coords = coordinates[..., [0, 1]]
+        # yz_coords = coordinates[..., [1, 2]]
+        # zx_coords = coordinates[..., [2, 0]]
+        # return torch.stack([xy_coords, yz_coords, zx_coords],
+        #                 dim=1).reshape(N * 3, M, 2)
+
+        index_i, index_j = torch.meshgrid(torch.arange(0, p),
+                                          torch.arange(0, p),
+                                          indexing='ij')  # 16*16
+        index_mesh_grid = torch.stack([index_i, index_j], 0).to(
+            x.device).unsqueeze(0).repeat_interleave(B,
+                                                     0).reshape(B, 2, p,
+                                                                p)  # B 2 p p.
+
+        for i in range(group_size):
+            q_x[B * i * N:B * (i + 1) * N] = x[:, i:i + 1].permute(
+                0, 2, 3, 1, 4).reshape(B * N, 1, C)  # B 1 p p C -> B*N, 1, C
+
+            # TODO, how to batchify gather ops?
+            plane_yz = x[:, (i + 1) % group_size:(i + 1) % group_size +
+                         1]  # B 1 p p C
+            plane_zx = x[:, (i + 2) % group_size:(i + 2) % group_size + 1]
+
+            assert plane_yz.shape == plane_zx.shape == (
+                B, 1, p, p, C), 'check sub plane dimensions'
+
+            pooling_plane_yz = torch.gather(
+                plane_yz,
+                dim=2,
+                index=index_mesh_grid[:, 0:1].reshape(B, 1, N, 1, 1).expand(
+                    -1, -1, -1, p,
+                    C)).permute(0, 2, 1, 3, 4)  # B 1 256 16 C => B 256 1 16 C
+            pooling_plane_zx = torch.gather(
+                plane_zx,
+                dim=3,
+                index=index_mesh_grid[:, 1:2].reshape(B, 1, 1, N, 1).expand(
+                    -1, -1, p, -1,
+                    C)).permute(0, 3, 1, 2, 4)  # B 1 16 256 C => B 256 1 16 C
+
+            k_x[B * i * N:B * (i + 1) *
+                N] = v_x[B * i * N:B * (i + 1) * N] = torch.cat(
+                    [pooling_plane_yz, pooling_plane_zx],
+                    dim=2).reshape(B * N, 2 * p,
+                                   C)  # B 256 2 16 C => (B*256) 2*16 C
+
+            # q[B * i * N: B * (i+1) * N] = self.wq(q_x).reshape(B*N, 1, self.num_heads, C // self.num_heads).permute( 0, 2, 1, 3)
+            # k[B * i * N: B * (i+1) * N] = self.wk(k_x).reshape(B*N, 2*p, self.num_heads, C // self.num_heads).permute( 0, 2, 1, 3)
+            # v[B * i * N: B * (i+1) * N] = self.wv(v_x).reshape(B*N, 2*p, self.num_heads, C // self.num_heads).permute( 0, 2, 1, 3)
+
+        q = self.wq(q_x).reshape(B * group_size * N, 1,
+                                 self.num_heads, C // self.num_heads).permute(
+                                     0, 2, 1,
+                                     3)  # merge num_heads into Batch dimention
+        k = self.wk(k_x).reshape(B * group_size * N, 2 * p, self.num_heads,
+                                 C // self.num_heads).permute(0, 2, 1, 3)
+        v = self.wv(v_x).reshape(B * group_size * N, 2 * p, self.num_heads,
+                                 C // self.num_heads).permute(0, 2, 1, 3)
+
+        attn = (q @ k.transpose(
+            -2, -1)) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N, N=2p here
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(
+            B * 3 * N, 1,
+            C)  # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        # reshape x back
+        x = x.reshape(B, 3, N, C)
+
+        return x
+
+
+class xformer_Conv3D_Aware_CrossAttention(nn.Module):
+    # https://github.dev/facebookresearch/dinov2
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+        super().__init__()
+
+        # https://pytorch.org/blog/accelerated-generative-diffusion-models/
+
+        self.num_heads = num_heads
+        self.wq = nn.Linear(dim, dim * 1, bias=qkv_bias)
+        self.w_kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        self.index_mesh_grid = None
+
+    def forward(self, x, attn_bias=None):
+
+        B, group_size, N, C = x.shape  # B 3 N C
+        p = int(N**0.5)  # patch size
+        assert p**2 == N, 'check input dim, no [cls] needed here'
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.reshape(B, group_size, p, p, C)  # expand patch token dim
+
+        q_x = torch.empty(B * group_size * N, 1, C, device=x.device)
+        context = torch.empty(B * group_size * N, 2 * p, C,
+                              device=x.device)  # k_x=v_x
+
+        if self.index_mesh_grid is None:  # further accelerate
+            index_i, index_j = torch.meshgrid(torch.arange(0, p),
+                                              torch.arange(0, p),
+                                              indexing='ij')  # 16*16
+            index_mesh_grid = torch.stack([index_i, index_j], 0).to(
+                x.device).unsqueeze(0).repeat_interleave(B, 0).reshape(
+                    B, 2, p, p)  # B 2 p p.
+            self.index_mesh_grid = index_mesh_grid[0:1]
+        else:
+            index_mesh_grid = self.index_mesh_grid.clone().repeat_interleave(
+                B, 0)
+            assert index_mesh_grid.shape == (
+                B, 2, p, p), 'check index_mesh_grid dimension'
+
+        for i in range(group_size):
+            q_x[B * i * N:B * (i + 1) * N] = x[:, i:i + 1].permute(
+                0, 2, 3, 1, 4).reshape(B * N, 1, C)  # B 1 p p C -> B*N, 1, C
+
+            # TODO, how to batchify gather ops?
+            plane_yz = x[:, (i + 1) % group_size:(i + 1) % group_size +
+                         1]  # B 1 p p C
+            plane_zx = x[:, (i + 2) % group_size:(i + 2) % group_size + 1]
+
+            assert plane_yz.shape == plane_zx.shape == (
+                B, 1, p, p, C), 'check sub plane dimensions'
+
+            pooling_plane_yz = torch.gather(
+                plane_yz,
+                dim=2,
+                index=index_mesh_grid[:, 0:1].reshape(B, 1, N, 1, 1).expand(
+                    -1, -1, -1, p,
+                    C)).permute(0, 2, 1, 3, 4)  # B 1 256 16 C => B 256 1 16 C
+            pooling_plane_zx = torch.gather(
+                plane_zx,
+                dim=3,
+                index=index_mesh_grid[:, 1:2].reshape(B, 1, 1, N, 1).expand(
+                    -1, -1, p, -1,
+                    C)).permute(0, 3, 1, 2, 4)  # B 1 16 256 C => B 256 1 16 C
+
+            context[B * i * N:B * (i + 1) * N] = torch.cat(
+                [pooling_plane_yz, pooling_plane_zx],
+                dim=2).reshape(B * N, 2 * p,
+                               C)  # B 256 2 16 C => (B*256) 2*16 C
+
+        # B, N, C = x.shape
+
+        q = self.wq(q_x).reshape(B * group_size * N, 1, self.num_heads,
+                                 C // self.num_heads)
+
+        kv = self.w_kv(context).reshape(B * group_size * N, 2 * p, 2,
+                                        self.num_heads, C // self.num_heads)
+        k, v = unbind(kv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp)
+        x = x.transpose(1, 2).reshape([B * 3 * N, 1, C]).reshape(B, 3, N, C)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        return x
+
+
+class xformer_Conv3D_Aware_CrossAttention_xygrid(
+        xformer_Conv3D_Aware_CrossAttention):
+    """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
+    """
+
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop=0.0,
+                 proj_drop=0.0):
+        super().__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop,
+                         proj_drop)
+
+    def forward(self, x, attn_bias=None):
+
+        B, group_size, N, C = x.shape  # B 3 N C
+        p = int(N**0.5)  # patch size
+        assert p**2 == N, 'check input dim, no [cls] needed here'
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.reshape(B, group_size, p, p, C)  # expand patch token dim
+
+        q_x = torch.empty(B * group_size * N, 1, C, device=x.device)
+        context = torch.empty(B * group_size * N, 2 * p, C,
+                              device=x.device)  # k_x=v_x
+
+        if self.index_mesh_grid is None:  # further accelerate
+            index_u, index_v = torch.meshgrid(
+                torch.arange(0, p), torch.arange(0, p),
+                indexing='xy')  # ! switch to 'xy' here to match uv coordinate
+            index_mesh_grid = torch.stack([index_u, index_v], 0).to(
+                x.device).unsqueeze(0).repeat_interleave(B, 0).reshape(
+                    B, 2, p, p)  # B 2 p p.
+            self.index_mesh_grid = index_mesh_grid[0:1]
+        else:
+            index_mesh_grid = self.index_mesh_grid.clone().repeat_interleave(
+                B, 0)
+            assert index_mesh_grid.shape == (
+                B, 2, p, p), 'check index_mesh_grid dimension'
+
+        for i in range(group_size):
+            q_x[B * i * N:B * (i + 1) * N] = x[:, i:i + 1].permute(
+                0, 2, 3, 1, 4).reshape(B * N, 1, C)  # B 1 p p C -> B*N, 1, C
+
+            # TODO, how to batchify gather ops?
+            plane_yz = x[:, (i + 1) % group_size:(i + 1) % group_size +
+                         1]  # B 1 p p C
+            plane_zx = x[:, (i + 2) % group_size:(i + 2) % group_size + 1]
+
+            assert plane_yz.shape == plane_zx.shape == (
+                B, 1, p, p, C), 'check sub plane dimensions'
+
+            pooling_plane_yz = torch.gather(
+                plane_yz,
+                dim=2,
+                index=index_mesh_grid[:, 1:2].reshape(B, 1, N, 1, 1).expand(
+                    -1, -1, -1, p,
+                    C)).permute(0, 2, 1, 3, 4)  # B 1 256 16 C => B 256 1 16 C
+            pooling_plane_zx = torch.gather(
+                plane_zx,
+                dim=3,
+                index=index_mesh_grid[:, 0:1].reshape(B, 1, 1, N, 1).expand(
+                    -1, -1, p, -1,
+                    C)).permute(0, 3, 1, 2, 4)  # B 1 16 256 C => B 256 1 16 C
+
+            context[B * i * N:B * (i + 1) * N] = torch.cat(
+                [pooling_plane_yz, pooling_plane_zx],
+                dim=2).reshape(B * N, 2 * p,
+                               C)  # B 256 2 16 C => (B*256) 2*16 C
+
+        # B, N, C = x.shape
+        q = self.wq(q_x).reshape(B * group_size * N, 1, self.num_heads,
+                                 C // self.num_heads)
+
+        kv = self.w_kv(context).reshape(B * group_size * N, 2 * p, 2,
+                                        self.num_heads, C // self.num_heads)
+        k, v = unbind(kv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp)
+        x = x.transpose(1, 2).reshape([B * 3 * N, 1, C]).reshape(B, 3, N, C)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        return x
+
+
+class xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(
+        xformer_Conv3D_Aware_CrossAttention_xygrid):
+
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop=0,
+                 proj_drop=0):
+        super().__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop,
+                         proj_drop)
+
+    def forward(self, x, attn_bias=None):
+        # ! split x: B N C into B 3 N C//3
+        B, N, C = x.shape
+        x = x.reshape(B, N, C // 3, 3).permute(0, 3, 1,
+                                               2)  # B N C 3 -> B 3 N C
+        x_out = super().forward(x, attn_bias)  # B 3 N C
+        x_out = x_out.permute(0, 2, 3, 1)# B 3 N C -> B N C 3
+        x_out = x_out.reshape(*x_out.shape[:2], -1) # B N C 3 -> B N C3
+        return x_out.contiguous()
+
+class self_cross_attn(nn.Module):
+    def __init__(self, dino_attn, cross_attn, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.dino_attn = dino_attn
+        self.cross_attn = cross_attn
+    
+    def forward(self, x_norm):
+        y = self.dino_attn(x_norm) + x_norm
+        return self.cross_attn(y) # will add x in the original code
+
+# class RodinRollOutConv(nn.Module):
+#     """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
+#     Use Group Conv
+#     """
+
+#     def __init__(self, in_chans, out_chans=None):
+#         super().__init__()
+#         # input: B 3C H W
+#         if out_chans is None:
+#             out_chans = in_chans
+
+#         self.roll_out_convs = nn.Conv2d(in_chans,
+#                                         out_chans,
+#                                         kernel_size=3,
+#                                         groups=3,
+#                                         padding=1)
+
+#     def forward(self, x):
+#         return self.roll_out_convs(x)
+
+
+class RodinRollOutConv3D(nn.Module):
+    """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
+    """
+
+    def __init__(self, in_chans, out_chans=None):
+        super().__init__()
+        if out_chans is None:
+            out_chans = in_chans
+
+        self.out_chans = out_chans // 3
+
+        self.roll_out_convs = nn.Conv2d(in_chans,
+                                        self.out_chans,
+                                        kernel_size=3,
+                                        padding=1)
+
+    def forward(self, x):
+        # todo, reshape before input?
+
+        B, C3, p, p = x.shape  # B 3C H W
+        C = C3 // 3
+        group_size = C3 // C
+        assert group_size == 3
+
+        x = x.reshape(B, 3, C, p, p)
+
+        roll_out_x = torch.empty(B, group_size * C, p, 3 * p,
+                                 device=x.device)  # B, 3C, H, 3W
+
+        for i in range(group_size):
+            plane_xy = x[:, i]  # B C H W
+
+            # TODO, simply do the average pooling?
+            plane_yz_pooling = x[:, (i + 1) % group_size].mean(
+                dim=-1, keepdim=True).repeat_interleave(
+                    p, dim=-1)  # B C H W -> B C H 1 -> B C H W, reduce z dim
+            plane_zx_pooling = x[:, (i + 2) % group_size].mean(
+                dim=-2, keepdim=True).repeat_interleave(
+                    p, dim=-2)  # B C H W -> B C 1 W -> B C H W, reduce z dim
+
+            roll_out_x[..., i * p:(i + 1) * p] = torch.cat(
+                [plane_xy, plane_yz_pooling, plane_zx_pooling],
+                1)  # fill in the 3W dim
+
+        x = self.roll_out_convs(roll_out_x)  # B C H 3W
+
+        x = x.reshape(B, self.out_chans, p, 3, p)
+        x = x.permute(0, 3, 1, 2, 4).reshape(B, 3 * self.out_chans, p,
+                                             p)  # B 3C H W
+
+        return x
+
+
+class RodinRollOutConv3D_GroupConv(nn.Module):
+    """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
+    """
+
+    def __init__(self,
+                 in_chans,
+                 out_chans=None,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1):
+        super().__init__()
+        if out_chans is None:
+            out_chans = in_chans
+
+        self.roll_out_convs = nn.Conv2d(
+            in_chans * 3,
+            out_chans,
+            kernel_size=kernel_size,
+            groups=3,  # B 9C H W
+            stride=stride,
+            padding=padding)
+
+    # @torch.autocast(device_type='cuda')
+    def forward(self, x):
+        # todo, reshape before input?
+
+        B, C3, p, p = x.shape  # B 3C H W
+        C = C3 // 3
+        group_size = C3 // C
+        assert group_size == 3
+
+        x = x.reshape(B, 3, C, p, p)
+
+        roll_out_x = torch.empty(B, group_size * C * 3, p, p,
+                                 device=x.device)  # B, 3C, H, 3W
+
+        for i in range(group_size):
+            plane_xy = x[:, i]  # B C H W
+
+            # # TODO, simply do the average pooling?
+            plane_yz_pooling = x[:, (i + 1) % group_size].mean(
+                dim=-1, keepdim=True).repeat_interleave(
+                    p, dim=-1)  # B C H W -> B C H 1 -> B C H W, reduce z dim
+            plane_zx_pooling = x[:, (i + 2) % group_size].mean(
+                dim=-2, keepdim=True).repeat_interleave(
+                    p, dim=-2)  # B C H W -> B C 1 W -> B C H W, reduce z dim
+
+            roll_out_x[:, i * 3 * C:(i + 1) * 3 * C] = torch.cat(
+                [plane_xy, plane_yz_pooling, plane_zx_pooling],
+                1)  # fill in the 3W dim
+
+            # ! directly cat, avoid intermediate vars
+            # ? why OOM
+            # roll_out_x[:, i * 3 * C:(i + 1) * 3 * C] = torch.cat(
+            #     [
+            #         x[:, i],
+            #         x[:, (i + 1) % group_size].mean(
+            #             dim=-1, keepdim=True).repeat_interleave(p, dim=-1),
+            #         x[:, (i + 2) % group_size].mean(
+            #             dim=-2, keepdim=True).repeat_interleave(
+            #                 p, dim=-2
+            #             )  # B C H W -> B C 1 W -> B C H W, reduce z dim
+            #     ],
+            #     1)  # fill in the 3C dim
+
+        x = self.roll_out_convs(roll_out_x)  # B 3C H W
+
+        return x
+
+
+class RodinRollOut_GroupConv_noConv3D(nn.Module):
+    """only roll out and do Conv on individual planes
+    """
+
+    def __init__(self,
+                 in_chans,
+                 out_chans=None,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1):
+        super().__init__()
+        if out_chans is None:
+            out_chans = in_chans
+
+        self.roll_out_inplane_conv = nn.Conv2d(
+            in_chans,
+            out_chans,
+            kernel_size=kernel_size,
+            groups=3,  # B 3C H W
+            stride=stride,
+            padding=padding)
+
+    def forward(self, x):
+        x = self.roll_out_inplane_conv(x)  # B 3C H W
+        return x
+
+
+# class RodinConv3D_SynthesisLayer_withact(nn.Module):
+#     def __init__(self, in_chans, out_chans) -> None:
+#         super().__init__()
+
+#         self.act = nn.LeakyReLU(inplace=True)
+#         self.conv = nn.Sequential(
+#             RodinRollOutConv3D_GroupConv(in_chans, out_chans),
+#             nn.LeakyReLU(inplace=True),
+#         )
+
+#         if in_chans != out_chans:
+#             self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
+#         else:
+#             self.short_cut = None
+
+# def forward(self, feats):
+
+#     if self.short_cut is not None:
+#         res_feats = self.short_cut(feats)
+#     else:
+#         res_feats = feats
+
+#     # return res_feats + self.conv(feats)
+#     feats = res_feats + self.conv(feats)
+#     return self.act(feats) # as in resnet, add an act before return
+
+
+class RodinConv3D_SynthesisLayer_mlp_unshuffle_as_residual(nn.Module):
+
+    def __init__(self, in_chans, out_chans) -> None:
+        super().__init__()
+
+        self.act = nn.LeakyReLU(inplace=True)
+        self.conv = nn.Sequential(
+            RodinRollOutConv3D_GroupConv(in_chans, out_chans),
+            nn.LeakyReLU(inplace=True),
+        )
+
+        self.out_chans = out_chans
+        if in_chans != out_chans:
+            # self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
+            self.short_cut = nn.Linear(  # B 3C H W -> B 3C 4H 4W
+                in_chans // 3,  # 144 / 3 = 48
+                out_chans // 3 * 4 * 4,  # 32 * 16
+                bias=True)  # decoder to pat
+
+            # RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
+        else:
+            self.short_cut = None
+
+    def shortcut_unpatchify_triplane(self,
+                                     x,
+                                     p=None,
+                                     unpatchify_out_chans=None):
+        """separate triplane version; x shape: B (3*257) 768
+        """
+
+        assert self.short_cut is not None
+
+        # B, L, C = x.shape
+        B, C3, h, w = x.shape
+        assert h == w
+        L = h * w
+        x = x.reshape(B, C3 // 3, 3, L).permute(0, 2, 3,
+                                                1)  # (B, 3, L // 3, C)
+
+        x = self.short_cut(x)
+
+        p = h * 4
+
+        x = x.reshape(shape=(B, 3, h, w, p, p, unpatchify_out_chans))
+        x = torch.einsum('ndhwpqc->ndchpwq',
+                         x)  # nplanes, C order in the renderer.py
+        x = x.reshape(shape=(B, 3 * self.out_chans, h * p, h * p))
+        return x
+
+    def forward(self, feats):
+
+        if self.short_cut is not None:
+            res_feats = self.shortcut_unpatchify_triplane(feats)
+        else:
+            res_feats = feats
+
+        # return res_feats + self.conv(feats)
+
+        feats = res_feats + self.conv(feats)
+        return self.act(feats)  # as in resnet, add an act before return
+
+
+# class RodinConv3D_SynthesisLayer(nn.Module):
+#     def __init__(self, in_chans, out_chans) -> None:
+#         super().__init__()
+
+#         self.act = nn.LeakyReLU(inplace=True)
+#         self.conv = nn.Sequential(
+#             RodinRollOutConv3D_GroupConv(in_chans, out_chans),
+#             nn.LeakyReLU(inplace=True),
+#         )
+
+#         if in_chans != out_chans:
+#             self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
+#         else:
+#             self.short_cut = None
+
+#     def forward(self, feats):
+
+#         if self.short_cut is not None:
+#             res_feats = self.short_cut(feats)
+#         else:
+#             res_feats = feats
+
+#         # return res_feats + self.conv(feats)
+
+#         feats = res_feats + self.conv(feats)
+#         # return self.act(feats) # as in resnet, add an act before return
+#         return feats # ! old behaviour, no act
+
+
+# previous worked version
+class RodinConv3D_SynthesisLayer(nn.Module):
+
+    def __init__(self, in_chans, out_chans) -> None:
+        super().__init__()
+        # x2 SR + 1x1 Conv Residual BLK
+        # self.conv3D = RodinRollOutConv3D(in_chans, out_chans)
+
+        self.act = nn.LeakyReLU(inplace=True)
+        self.conv = nn.Sequential(
+            RodinRollOutConv3D_GroupConv(in_chans, out_chans),
+            nn.LeakyReLU(inplace=True),
+        )
+
+        if in_chans != out_chans:
+            self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans)
+        else:
+            self.short_cut = None
+
+    def forward(self, feats):
+        feats_out = self.conv(feats)
+        if self.short_cut is not None:
+            # ! failed below
+            feats_out = self.short_cut(
+                feats
+            ) + feats_out  # ! only difference here, no act() compared with baseline
+            # feats_out = self.act(self.short_cut(feats)) + feats_out # ! only difference here, no act() compared with baseline
+        else:
+            feats_out = feats_out + feats
+        return feats_out
+
+
+class RodinRollOutConv3DSR2X(nn.Module):
+
+    def __init__(self, in_chans, **kwargs) -> None:
+        super().__init__()
+        self.conv3D = RodinRollOutConv3D_GroupConv(in_chans)
+        # self.conv3D = RodinRollOutConv3D(in_chans)
+        self.act = nn.LeakyReLU(inplace=True)
+        self.input_resolution = 224
+
+    def forward(self, x):
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        group_size = C3 // C
+
+        assert group_size == 3
+        # p = int(N**0.5)  # patch size
+        # assert p**2 == N, 'check input dim, no [cls] needed here'
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if x.shape[-1] != self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=True)
+
+        x = x + self.conv3D(x)
+
+        return x
+
+
+class RodinRollOutConv3DSR4X_lite(nn.Module):
+
+    def __init__(self, in_chans, input_resolutiopn=256, **kwargs) -> None:
+        super().__init__()
+        self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans)
+        self.conv3D_1 = RodinRollOutConv3D_GroupConv(in_chans)
+
+        self.act = nn.LeakyReLU(inplace=True)
+        self.input_resolution = input_resolutiopn
+
+    def forward(self, x):
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        group_size = C3 // C
+
+        assert group_size == 3
+        # p = int(N**0.5)  # patch size
+        # assert p**2 == N, 'check input dim, no [cls] needed here'
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if x.shape[-1] != self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=True)
+
+        # ! still not convering, not bug here?
+        # x = x + self.conv3D_0(x)
+        # x = x + self.conv3D_1(x)
+
+        x = x + self.act(self.conv3D_0(x))
+        x = x + self.act(self.conv3D_1(x))
+
+        # TODO: which is better, bilinear + conv or PixelUnshuffle?
+
+        return x
+
+
+# class RodinConv3D2X_lite_mlp_as_residual(nn.Module):
+#     """lite 4X version, with MLP unshuffle to change the dimention
+#     """
+#     def __init__(self, in_chans, out_chans, input_resolution=256) -> None:
+#         super().__init__()
+
+#         self.act = nn.LeakyReLU(inplace=True)
+
+#         self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans, out_chans)
+#         self.conv3D_1 = RodinRollOutConv3D_GroupConv(out_chans, out_chans)
+
+#         self.act = nn.LeakyReLU(inplace=True)
+#         self.input_resolution = input_resolution
+
+#         self.out_chans = out_chans
+#         if in_chans != out_chans: # ! only change the dimension
+#             self.short_cut = nn.Linear( # B 3C H W -> B 3C 4H 4W
+#                 in_chans//3, # 144 / 3 = 48
+#                 out_chans//3, # 32 * 16
+#                 bias=True)  # decoder to pat
+#         else:
+#             self.short_cut = None
+
+#     def shortcut_unpatchify_triplane(self, x, p=None):
+#         """separate triplane version; x shape: B (3*257) 768
+#         """
+
+#         assert self.short_cut is not None
+
+#         # B, L, C = x.shape
+#         B, C3, h, w = x.shape
+#         assert h == w
+#         L = h*w
+#         x = x.reshape(B, C3//3, 3, L).permute(0,2,3,1) # (B, 3, L // 3, C_in)
+
+#         x = self.short_cut(x) # B 3 L//3 C_out
+
+#         x = x.permute(0,1,3,2) # B 3 C_out L//3
+#         x = x.reshape(shape=(B, self.out_chans, h, w))
+
+#         # directly resize to the target, no unpatchify here since no 3D ViT is included here
+#         if w != self.input_resolution:
+#             x = torch.nn.functional.interpolate(x, # 4X SR
+#                                                 size=(self.input_resolution,
+#                                                       self.input_resolution),
+#                                                 mode='bilinear',
+#                                                 align_corners=False,
+#                                                 antialias=True)
+
+#         return x
+
+#     def forward(self, x):
+
+#         # x: B 3 112*112 C
+#         B, C3, p, p = x.shape  # after unpachify triplane
+#         C = C3 // 3
+
+#         if self.short_cut is not None:
+#             res_feats = self.shortcut_unpatchify_triplane(x)
+#         else:
+#             res_feats = x
+
+#         """following forward code copied from lite4x version
+#         """
+#         x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+#                                           p)  # B 3 C N -> B 3C h W
+
+#         if x.shape[-1] != self.input_resolution:
+#             x = torch.nn.functional.interpolate(x, # 4X SR
+#                                                 size=(self.input_resolution,
+#                                                       self.input_resolution),
+#                                                 mode='bilinear',
+#                                                 align_corners=False,
+#                                                 antialias=True)
+
+#         x = res_feats + self.act(self.conv3D_0(x))
+#         x = x + self.act(self.conv3D_1(x))
+
+#         return x
+
+
+class RodinConv3D4X_lite_mlp_as_residual(nn.Module):
+    """lite 4X version, with MLP unshuffle to change the dimention
+    """
+
+    def __init__(self,
+                 in_chans,
+                 out_chans,
+                 input_resolution=256,
+                 interp_mode='bilinear',
+                 bcg_triplane=False) -> None:
+        super().__init__()
+
+        self.interp_mode = interp_mode
+
+        self.act = nn.LeakyReLU(inplace=True)
+
+        self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans, out_chans)
+        self.conv3D_1 = RodinRollOutConv3D_GroupConv(out_chans, out_chans)
+        self.bcg_triplane = bcg_triplane
+        if bcg_triplane:
+            self.conv3D_1_bg = RodinRollOutConv3D_GroupConv(
+                out_chans, out_chans)
+
+        self.act = nn.LeakyReLU(inplace=True)
+        self.input_resolution = input_resolution
+
+        self.out_chans = out_chans
+        if in_chans != out_chans:  # ! only change the dimension
+            self.short_cut = nn.Linear(  # B 3C H W -> B 3C 4H 4W
+                in_chans // 3,  # 144 / 3 = 48
+                out_chans // 3,  # 32 * 16
+                bias=True)  # decoder to pat
+        else:
+            self.short_cut = None
+
+    def shortcut_unpatchify_triplane(self, x, p=None):
+        """separate triplane version; x shape: B (3*257) 768
+        """
+
+        assert self.short_cut is not None
+
+        B, C3, h, w = x.shape
+        assert h == w
+        L = h * w
+        x = x.reshape(B, C3 // 3, 3, L).permute(0, 2, 3,
+                                                1)  # (B, 3, L // 3, C_in)
+
+        x = self.short_cut(x)  # B 3 L//3 C_out
+
+        x = x.permute(0, 1, 3, 2)  # B 3 C_out L//3
+        x = x.reshape(shape=(B, self.out_chans, h, w))
+
+        # directly resize to the target, no unpatchify here since no 3D ViT is included here
+        if w != self.input_resolution:
+            x = torch.nn.functional.interpolate(
+                x,  # 4X SR
+                size=(self.input_resolution, self.input_resolution),
+                mode='bilinear',
+                align_corners=False,
+                antialias=True)
+
+        return x
+
+    def interpolate(self, feats):
+        if self.interp_mode == 'bilinear':
+            return torch.nn.functional.interpolate(
+                feats,  # 4X SR
+                size=(self.input_resolution, self.input_resolution),
+                mode='bilinear',
+                align_corners=False,
+                antialias=True)
+        else:
+            return torch.nn.functional.interpolate(
+                feats,  # 4X SR
+                size=(self.input_resolution, self.input_resolution),
+                mode='nearest',
+            )
+
+    def forward(self, x):
+
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+
+        if self.short_cut is not None:
+            res_feats = self.shortcut_unpatchify_triplane(x)
+        else:
+            res_feats = x
+            if res_feats.shape[-1] != self.input_resolution:
+                res_feats = self.interpolate(res_feats)
+        """following forward code copied from lite4x version
+        """
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if x.shape[-1] != self.input_resolution:
+            x = self.interpolate(x)
+
+        x0 = res_feats + self.act(self.conv3D_0(x))  # the base feature
+        x = x0 + self.act(self.conv3D_1(x0))
+        if self.bcg_triplane:
+            x_bcg = x0 + self.act(self.conv3D_1_bg(x0))
+            return torch.cat([x, x_bcg], 1)
+        else:
+            return x
+
+
+class RodinConv3D4X_lite_mlp_as_residual_litev2(
+        RodinConv3D4X_lite_mlp_as_residual):
+
+    def __init__(self,
+                 in_chans,
+                 out_chans,
+                 num_feat=128,
+                 input_resolution=256,
+                 interp_mode='bilinear',
+                 bcg_triplane=False) -> None:
+        super().__init__(in_chans, out_chans, input_resolution, interp_mode,
+                         bcg_triplane)
+
+        self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans, in_chans)
+        self.conv_before_upsample = RodinRollOut_GroupConv_noConv3D(
+            in_chans, num_feat * 3)
+        self.conv3D_1 = RodinRollOut_GroupConv_noConv3D(
+            num_feat * 3, num_feat * 3)
+        self.conv_last = RodinRollOut_GroupConv_noConv3D(
+            num_feat * 3, out_chans)
+        self.short_cut = None
+
+    def forward(self, x):
+
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+
+        # if self.short_cut is not None:
+        #     res_feats = self.shortcut_unpatchify_triplane(x)
+        # else:
+        #     res_feats = x
+        #     if res_feats.shape[-1] != self.input_resolution:
+        #         res_feats = self.interpolate(res_feats)
+        """following forward code copied from lite4x version
+        """
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        x = x + self.conv3D_0(x)  # the base feature
+        x = self.act(self.conv_before_upsample(x))
+
+        # if x.shape[-1] != self.input_resolution:
+        x = self.conv_last(self.act(self.conv3D_1(self.interpolate(x))))
+
+        return x
+
+
+class RodinConv3D4X_lite_mlp_as_residual_lite(
+        RodinConv3D4X_lite_mlp_as_residual):
+
+    def __init__(self,
+                 in_chans,
+                 out_chans,
+                 input_resolution=256,
+                 interp_mode='bilinear') -> None:
+        super().__init__(in_chans, out_chans, input_resolution, interp_mode)
+        """replace the first Rodin Conv 3D with ordinary rollout conv to save memory
+        """
+        self.conv3D_0 = RodinRollOut_GroupConv_noConv3D(in_chans, out_chans)
+
+
+class SR3D(nn.Module):
+    # https://github.com/SeanChenxy/Mimic3D/blob/77d313656df3cd5536d2c4c5766db3a56208eea6/training/networks_stylegan2.py#L629
+    # roll-out and apply two deconv/pixelUnshuffle layer
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class RodinConv3D4X_lite_mlp_as_residual_improved(nn.Module):
+
+    def __init__(self,
+                 in_chans,
+                 num_feat,
+                 out_chans,
+                 input_resolution=256) -> None:
+        super().__init__()
+
+        assert in_chans == 4 * out_chans
+        assert num_feat == 2 * out_chans
+        self.input_resolution = input_resolution
+
+        # refer to https://github.com/JingyunLiang/SwinIR/blob/6545850fbf8df298df73d81f3e8cba638787c8bd/models/network_swinir.py#L750
+        self.upscale = 4
+
+        self.conv_after_body = RodinRollOutConv3D_GroupConv(
+            in_chans, in_chans, 3, 1, 1)
+        self.conv_before_upsample = nn.Sequential(
+            RodinRollOutConv3D_GroupConv(in_chans, num_feat, 3, 1, 1),
+            nn.LeakyReLU(inplace=True))
+        self.conv_up1 = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
+                                                     1)
+        if self.upscale == 4:
+            self.conv_up2 = RodinRollOutConv3D_GroupConv(
+                num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
+                                                    1)
+        self.conv_last = RodinRollOutConv3D_GroupConv(num_feat, out_chans, 3,
+                                                      1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        """following forward code copied from lite4x version
+        """
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        # ? nearest or bilinear
+        x = self.conv_after_body(x) + x
+        x = self.conv_before_upsample(x)
+        x = self.lrelu(
+            self.conv_up1(
+                torch.nn.functional.interpolate(
+                    x,
+                    scale_factor=2,
+                    mode='nearest',
+                    # align_corners=False,
+                    # antialias=True
+                )))
+        if self.upscale == 4:
+            x = self.lrelu(
+                self.conv_up2(
+                    torch.nn.functional.interpolate(
+                        x,
+                        scale_factor=2,
+                        mode='nearest',
+                        # align_corners=False,
+                        # antialias=True
+                    )))
+        x = self.conv_last(self.lrelu(self.conv_hr(x)))
+
+        assert x.shape[-1] == self.input_resolution
+
+        return x
+
+
+class RodinConv3D4X_lite_improved_lint_withresidual(nn.Module):
+
+    def __init__(self,
+                 in_chans,
+                 num_feat,
+                 out_chans,
+                 input_resolution=256) -> None:
+        super().__init__()
+
+        assert in_chans == 4 * out_chans
+        assert num_feat == 2 * out_chans
+        self.input_resolution = input_resolution
+
+        # refer to https://github.com/JingyunLiang/SwinIR/blob/6545850fbf8df298df73d81f3e8cba638787c8bd/models/network_swinir.py#L750
+        self.upscale = 4
+
+        self.conv_after_body = RodinRollOutConv3D_GroupConv(
+            in_chans, in_chans, 3, 1, 1)
+        self.conv_before_upsample = nn.Sequential(
+            RodinRollOutConv3D_GroupConv(in_chans, num_feat, 3, 1, 1),
+            nn.LeakyReLU(inplace=True))
+        self.conv_up1 = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
+                                                     1)
+        if self.upscale == 4:
+            self.conv_up2 = RodinRollOutConv3D_GroupConv(
+                num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
+                                                    1)
+        self.conv_last = RodinRollOutConv3D_GroupConv(num_feat, out_chans, 3,
+                                                      1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        """following forward code copied from lite4x version
+        """
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        # ? nearest or bilinear
+        x = self.conv_after_body(x) + x
+        x = self.conv_before_upsample(x)
+        x = self.lrelu(
+            self.conv_up1(
+                torch.nn.functional.interpolate(
+                    x,
+                    scale_factor=2,
+                    mode='nearest',
+                    # align_corners=False,
+                    # antialias=True
+                )))
+        if self.upscale == 4:
+            x = self.lrelu(
+                self.conv_up2(
+                    torch.nn.functional.interpolate(
+                        x,
+                        scale_factor=2,
+                        mode='nearest',
+                        # align_corners=False,
+                        # antialias=True
+                    )))
+        x = self.conv_last(self.lrelu(self.conv_hr(x) + x))
+
+        assert x.shape[-1] == self.input_resolution
+
+        return x
+
+
+class RodinRollOutConv3DSR_FlexibleChannels(nn.Module):
+
+    def __init__(self,
+                 in_chans,
+                 num_out_ch=96,
+                 input_resolution=256,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.block0 = RodinConv3D_SynthesisLayer(in_chans,
+                                                 num_out_ch)  # in_chans=48
+        self.block1 = RodinConv3D_SynthesisLayer(num_out_ch, num_out_ch)
+
+        self.input_resolution = input_resolution  # 64 -> 256 SR
+
+    def forward(self, x):
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        # group_size = C3 // C
+
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if x.shape[-1] != self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=True)
+
+        x = self.block0(x)
+        x = self.block1(x)
+
+        return x
+
+
+# previous worked version
+class RodinRollOutConv3DSR4X(nn.Module):
+    # follow PixelUnshuffleUpsample
+
+    def __init__(self, in_chans, **kwargs) -> None:
+        super().__init__()
+        # self.block0 = RodinConv3D_SynthesisLayer(in_chans, 96 * 2) # TODO, match the old behaviour now.
+        # self.block1 = RodinConv3D_SynthesisLayer(96 * 2, 96)
+
+        self.block0 = RodinConv3D_SynthesisLayer(in_chans, 96)
+        self.block1 = RodinConv3D_SynthesisLayer(
+            96, 96)  # baseline choice, validate with no LPIPS loss here
+
+        self.input_resolution = 64  # 64 -> 256
+
+    def forward(self, x):
+        # x: B 3 112*112 C
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        # group_size = C3 // C
+
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if x.shape[-1] != self.input_resolution:
+            x = torch.nn.functional.interpolate(x,
+                                                size=(self.input_resolution,
+                                                      self.input_resolution),
+                                                mode='bilinear',
+                                                align_corners=False,
+                                                antialias=True)
+
+        x = self.block0(x)
+        x = self.block1(x)
+
+        return x
+
+
+class Upsample3D(nn.Module):
+    """Upsample module.
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        super().__init__()
+
+        m_convs = []
+        m_pixelshuffle = []
+
+        assert (scale & (scale - 1)) == 0, 'scale = 2^n'
+        self.scale = scale
+
+        for _ in range(int(math.log(scale, 2))):
+            m_convs.append(
+                RodinRollOutConv3D_GroupConv(num_feat, 4 * num_feat, 3, 1, 1))
+            m_pixelshuffle.append(nn.PixelShuffle(2))
+
+        self.m_convs = nn.ModuleList(m_convs)
+        self.m_pixelshuffle = nn.ModuleList(m_pixelshuffle)
+
+    # @torch.autocast(device_type='cuda')
+    def forward(self, x):
+        for scale_idx in range(int(math.log(self.scale, 2))):
+            x = self.m_convs[scale_idx](x)  # B 3C H W
+            # x =
+            # B, C3, H, W = x.shape
+            x = x.reshape(x.shape[0] * 3, x.shape[1] // 3, *x.shape[2:])
+            x = self.m_pixelshuffle[scale_idx](x)
+            x = x.reshape(x.shape[0] // 3, x.shape[1] * 3, *x.shape[2:])
+
+        return x
+
+
+class RodinConv3DPixelUnshuffleUpsample(nn.Module):
+
+    def __init__(self,
+                 output_dim,
+                 num_feat=32 * 6,
+                 num_out_ch=32 * 3,
+                 sr_ratio=4,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.conv_after_body = RodinRollOutConv3D_GroupConv(
+            output_dim, output_dim, 3, 1, 1)
+        self.conv_before_upsample = nn.Sequential(
+            RodinRollOutConv3D_GroupConv(output_dim, num_feat, 3, 1, 1),
+            nn.LeakyReLU(inplace=True))
+        self.upsample = Upsample3D(sr_ratio, num_feat)  # 4 time SR
+        self.conv_last = RodinRollOutConv3D_GroupConv(num_feat, num_out_ch, 3,
+                                                      1, 1)
+
+    # @torch.autocast(device_type='cuda')
+    def forward(self, x, input_skip_connection=True, *args, **kwargs):
+        # x = self.conv_first(x)
+        if input_skip_connection:
+            x = self.conv_after_body(x) + x
+        else:
+            x = self.conv_after_body(x)
+
+        x = self.conv_before_upsample(x)
+        x = self.upsample(x)
+        x = self.conv_last(x)
+        return x
+
+
+class RodinConv3DPixelUnshuffleUpsample_improvedVersion(nn.Module):
+
+    def __init__(
+        self,
+        output_dim,
+        num_out_ch=32 * 3,
+        sr_ratio=4,
+        input_resolution=256,
+    ) -> None:
+        super().__init__()
+
+        self.input_resolution = input_resolution
+
+        # self.conv_first = RodinRollOutConv3D_GroupConv(output_dim, num_out_ch,
+        #                                               3, 1, 1)
+        self.upsample = Upsample3D(sr_ratio, output_dim)  # 4 time SR
+        self.conv_last = RodinRollOutConv3D_GroupConv(output_dim, num_out_ch,
+                                                      3, 1, 1)
+
+    def forward(self, x, bilinear_upsample=True):
+
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        group_size = C3 // C
+
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if bilinear_upsample and x.shape[-1] != self.input_resolution:
+            x_bilinear_upsample = torch.nn.functional.interpolate(
+                x,
+                size=(self.input_resolution, self.input_resolution),
+                mode='bilinear',
+                align_corners=False,
+                antialias=True)
+            x = self.upsample(x) + x_bilinear_upsample
+        else:
+            # x_bilinear_upsample = x
+            x = self.upsample(x)
+
+        x = self.conv_last(x)
+
+        return x
+
+
+class RodinConv3DPixelUnshuffleUpsample_improvedVersion2(nn.Module):
+    """removed nearest neighbour residual conenctions, add a conv layer residual conenction
+    """
+
+    def __init__(
+        self,
+        output_dim,
+        num_out_ch=32 * 3,
+        sr_ratio=4,
+        input_resolution=256,
+    ) -> None:
+        super().__init__()
+
+        self.input_resolution = input_resolution
+
+        self.conv_after_body = RodinRollOutConv3D_GroupConv(
+            output_dim, num_out_ch, 3, 1, 1)
+        self.upsample = Upsample3D(sr_ratio, output_dim)  # 4 time SR
+        self.conv_last = RodinRollOutConv3D_GroupConv(output_dim, num_out_ch,
+                                                      3, 1, 1)
+
+    def forward(self, x, input_skip_connection=True):
+
+        B, C3, p, p = x.shape  # after unpachify triplane
+        C = C3 // 3
+        group_size = C3 // C
+
+        assert group_size == 3, 'designed for triplane here'
+
+        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
+                                          p)  # B 3 C N -> B 3C h W
+
+        if input_skip_connection:
+            x = self.conv_after_body(x) + x
+        else:
+            x = self.conv_after_body(x)
+
+        x = self.upsample(x)
+        x = self.conv_last(x)
+
+        return x
+
+
+class CLSCrossAttentionBlock(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=4.,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 has_mlp=False):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = CrossAttention(dim,
+                                   num_heads=num_heads,
+                                   qkv_bias=qkv_bias,
+                                   qk_scale=qk_scale,
+                                   attn_drop=attn_drop,
+                                   proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.has_mlp = has_mlp
+        if has_mlp:
+            self.norm2 = norm_layer(dim)
+            mlp_hidden_dim = int(dim * mlp_ratio)
+            self.mlp = Mlp(in_features=dim,
+                           hidden_features=mlp_hidden_dim,
+                           act_layer=act_layer,
+                           drop=drop)
+
+    def forward(self, x):
+        x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
+        if self.has_mlp:
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class Conv3DCrossAttentionBlock(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=4.,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 has_mlp=False):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Conv3D_Aware_CrossAttention(dim,
+                                                num_heads=num_heads,
+                                                qkv_bias=qkv_bias,
+                                                qk_scale=qk_scale,
+                                                attn_drop=attn_drop,
+                                                proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.has_mlp = has_mlp
+        if has_mlp:
+            self.norm2 = norm_layer(dim)
+            mlp_hidden_dim = int(dim * mlp_ratio)
+            self.mlp = Mlp(in_features=dim,
+                           hidden_features=mlp_hidden_dim,
+                           act_layer=act_layer,
+                           drop=drop)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        if self.has_mlp:
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class Conv3DCrossAttentionBlockXformerMHA(Conv3DCrossAttentionBlock):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=4,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0,
+                 attn_drop=0,
+                 drop_path=0,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 has_mlp=False):
+        super().__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop,
+                         attn_drop, drop_path, act_layer, norm_layer, has_mlp)
+        # self.attn = xformer_Conv3D_Aware_CrossAttention(dim,
+        self.attn = xformer_Conv3D_Aware_CrossAttention_xygrid(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop)
+
+
+class Conv3DCrossAttentionBlockXformerMHANested(
+        Conv3DCrossAttentionBlockXformerMHA):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=4,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 has_mlp=False):
+        super().__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop,
+                         attn_drop, drop_path, act_layer, norm_layer, has_mlp)
+        """for in-place replaing the internal attn in Dino ViT.
+        """
+
+    def forward(self, x):
+        Bx3, N, C = x.shape
+        B, group_size = Bx3 // 3, 3
+        x = x.reshape(B, group_size, N, C)  # in plane vit
+        x = super().forward(x)
+        return x.reshape(B * group_size, N,
+                         C)  # to match the original attn size
+
+
+class Conv3DCrossAttentionBlockXformerMHANested_withinC(
+        Conv3DCrossAttentionBlockXformerMHANested):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=4,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0,
+                 attn_drop=0,
+                 drop_path=0,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 has_mlp=False):
+        super().__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop,
+                         attn_drop, drop_path, act_layer, norm_layer, has_mlp)
+        self.attn = xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop)
+
+    def forward(self, x):
+        # basic TX attention forward function
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        if self.has_mlp:
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class TriplaneFusionBlock(nn.Module):
+    """4 ViT blocks + 1 CrossAttentionBlock
+    """
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 cross_attention_blk=CLSCrossAttentionBlock,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        if use_fusion_blk:
+            self.fusion = nn.ModuleList()
+
+            # copied vit settings from https://github.dev/facebookresearch/dinov2
+            nh = num_heads
+            dim = embed_dim
+
+            mlp_ratio = 4  # defined for all dino2 model
+            qkv_bias = True
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+            drop_path_rate = 0.3  # default setting
+            attn_drop = proj_drop = 0.0
+            qk_scale = None  # TODO, double check
+
+            for d in range(self.num_branches):
+                self.fusion.append(
+                    cross_attention_blk(
+                        dim=dim,
+                        num_heads=nh,
+                        mlp_ratio=mlp_ratio,
+                        qkv_bias=qkv_bias,
+                        qk_scale=qk_scale,
+                        # drop=drop,
+                        drop=proj_drop,
+                        attn_drop=attn_drop,
+                        drop_path=drop_path_rate,
+                        norm_layer=norm_layer,  # type: ignore
+                        has_mlp=False))
+        else:
+            self.fusion = None
+
+    def forward(self, x):
+        # modified from https://github.com/IBM/CrossViT/blob/main/models/crossvit.py#L132
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+        x = x.view(B * group_size, N, C)
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B 3 N C
+
+        if self.fusion is None:
+            return x.view(B, group_size, N, C)
+
+        # outs_b = x.view(B, group_size, N,
+        #                 C).chunk(chunks=3,
+        #                          dim=1)  # 3 * [B, 1, N//3, C] Tensors, for fusion
+
+        outs_b = x.chunk(chunks=3,
+                         dim=0)  # 3 * [B, N//3, C] Tensors, for fusion
+
+        # only take the cls token out
+        proj_cls_token = [x[:, 0:1] for x in outs_b]
+        # cross attention
+        outs = []
+        for i in range(self.num_branches):
+            tmp = torch.cat(
+                (proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:,
+                                                                        ...]),
+                dim=1)
+            tmp = self.fusion[i](tmp)
+            # reverted_proj_cls_token = self.revert_projs[i](tmp[:, 0:1, ...])
+            reverted_proj_cls_token = tmp[:, 0:1, ...]
+            tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]),
+                            dim=1)
+            outs.append(tmp)
+        # outs = ? needs to merge back?
+        outs = torch.stack(outs, 1)  # B 3 N C
+        return outs
+
+
+class TriplaneFusionBlockv2(nn.Module):
+    """4 ViT blocks + 1 CrossAttentionBlock
+    """
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlock,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        if use_fusion_blk:
+            # self.fusion = nn.ModuleList()
+
+            # copied vit settings from https://github.dev/facebookresearch/dinov2
+            nh = num_heads
+            dim = embed_dim
+
+            mlp_ratio = 4  # defined for all dino2 model
+            qkv_bias = True
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+            drop_path_rate = 0.3  # default setting
+            attn_drop = proj_drop = 0.0
+            qk_scale = None  # TODO, double check
+
+            # for d in range(self.num_branches):
+            self.fusion = fusion_ca_blk(  # one fusion is enough
+                dim=dim,
+                num_heads=nh,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                # drop=drop,
+                drop=proj_drop,
+                attn_drop=attn_drop,
+                drop_path=drop_path_rate,
+                norm_layer=norm_layer,  # type: ignore
+                has_mlp=False)
+        else:
+            self.fusion = None
+
+    def forward(self, x):
+        # modified from https://github.com/IBM/CrossViT/blob/main/models/crossvit.py#L132
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+        x = x.reshape(B * group_size, N, C)
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B 3 N C
+
+        if self.fusion is None:
+            return x.reshape(B, group_size, N, C)
+
+        x = x.reshape(B, group_size, N, C)  # .chunk(chunks=3,
+        #  dim=1)  # 3 * [B, N//3, C] Tensors, for fusion
+        return self.fusion(x)
+
+
+class TriplaneFusionBlockv3(TriplaneFusionBlockv2):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHA,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk,
+                         fusion_ca_blk, *args, **kwargs)
+
+
+class TriplaneFusionBlockv4(TriplaneFusionBlockv3):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHA,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk,
+                         fusion_ca_blk, *args, **kwargs)
+        """OOM? directly replace the atten here
+        """
+
+        assert len(vit_blks) == 2
+        # del self.vit_blks[1].attn
+        del self.vit_blks[1].attn, self.vit_blks[1].ls1, self.vit_blks[1].norm1
+
+    def ffn_residual_func(self, tx_blk, x: Tensor) -> Tensor:
+        return tx_blk.ls2(
+            tx_blk.mlp(tx_blk.norm2(x))
+        )  # https://github.com/facebookresearch/dinov2/blob/c3c2683a13cde94d4d99f523cf4170384b00c34c/dinov2/layers/block.py#L86C1-L87C53
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+        assert self.fusion is not None
+
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        x = x.reshape(B * group_size, N, C)  # in plane vit
+
+        # in plane self attention
+        x = self.vit_blks[0](x)
+
+        # 3D cross attention blk + ffn
+        x = x + self.fusion(x.reshape(B, group_size, N, C)).reshape(
+            B * group_size, N, C)
+        x = x + self.ffn_residual_func(self.vit_blks[1], x)
+        return x.reshape(B, group_size, N, C)
+
+
+class TriplaneFusionBlockv4_nested(nn.Module):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        assert use_fusion_blk
+
+        assert len(vit_blks) == 2
+
+        # ! replace vit_blks[1] attn layer with 3D aware attention
+        del self.vit_blks[
+            1].attn  # , self.vit_blks[1].ls1, self.vit_blks[1].norm1
+
+        # copied vit settings from https://github.dev/facebookresearch/dinov2
+        nh = num_heads
+        dim = embed_dim
+
+        mlp_ratio = 4  # defined for all dino2 model
+        qkv_bias = True
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        drop_path_rate = 0.3  # default setting
+        attn_drop = proj_drop = 0.0
+        qk_scale = None  # TODO, double check
+
+        self.vit_blks[1].attn = fusion_ca_blk(  # one fusion is enough
+            dim=dim,
+            num_heads=nh,
+            mlp_ratio=mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            # drop=drop,
+            drop=proj_drop,
+            attn_drop=attn_drop,
+            drop_path=drop_path_rate,
+            norm_layer=norm_layer,  # type: ignore
+            has_mlp=False)
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+        x = x.reshape(B * group_size, N, C)
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B 3 N C
+
+        # TODO, avoid the reshape overhead?
+        return x.reshape(B, group_size, N, C)
+
+
+class TriplaneFusionBlockv4_nested_init_from_dino(nn.Module):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
+                 init_from_dino=True,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        assert use_fusion_blk
+
+        assert len(vit_blks) == 2
+
+        # copied vit settings from https://github.dev/facebookresearch/dinov2
+        nh = num_heads
+        dim = embed_dim
+
+        mlp_ratio = 4  # defined for all dino2 model
+        qkv_bias = True
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        drop_path_rate = 0.3  # default setting
+        attn_drop = proj_drop = 0.0
+        qk_scale = None  # TODO, double check
+
+        attn_3d = fusion_ca_blk(  # one fusion is enough
+            dim=dim,
+            num_heads=nh,
+            mlp_ratio=mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            # drop=drop,
+            drop=proj_drop,
+            attn_drop=attn_drop,
+            drop_path=drop_path_rate,
+            norm_layer=norm_layer,  # type: ignore
+            has_mlp=False)
+
+        # ! initialize 3dattn from dino attn
+        if init_from_dino:
+            merged_qkv_linear = self.vit_blks[1].attn.qkv
+            attn_3d.attn.proj.load_state_dict(
+                self.vit_blks[1].attn.proj.state_dict())
+
+            # Initialize the Q, K, and V linear layers using the weights of the merged QKV linear layer
+            attn_3d.attn.wq.weight.data = merged_qkv_linear.weight.data[:
+                                                                        dim, :]
+            attn_3d.attn.w_kv.weight.data = merged_qkv_linear.weight.data[
+                dim:, :]
+
+            # Optionally, you can initialize the biases as well (if your QKV linear layer has biases)
+            if qkv_bias:
+                attn_3d.attn.wq.bias.data = merged_qkv_linear.bias.data[:dim]
+                attn_3d.attn.w_kv.bias.data = merged_qkv_linear.bias.data[dim:]
+
+        del self.vit_blks[1].attn
+        # ! assign
+        self.vit_blks[1].attn = attn_3d
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+        x = x.reshape(B * group_size, N, C)
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B 3 N C
+
+        # TODO, avoid the reshape overhead?
+        return x.reshape(B, group_size, N, C)
+
+
+class TriplaneFusionBlockv4_nested_init_from_dino_lite(nn.Module):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=None,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        assert use_fusion_blk
+
+        assert len(vit_blks) == 2
+
+        # copied vit settings from https://github.dev/facebookresearch/dinov2
+        nh = num_heads
+        dim = embed_dim
+
+        mlp_ratio = 4  # defined for all dino2 model
+        qkv_bias = True
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        drop_path_rate = 0.3  # default setting
+        attn_drop = proj_drop = 0.0
+        qk_scale = None  # TODO, double check
+
+        attn_3d = xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(  # ! raw 3D attn layer
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop)
+
+        del self.vit_blks[1].attn
+        # ! assign
+        self.vit_blks[1].attn = attn_3d
+
+    def forward(self, x):
+        """x: B N C, where N = H*W tokens. Just raw ViT forward pass
+        """
+
+        # ! move the below to the front of the first call
+        B, N, C = x.shape  # has [cls] token in N
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B N C
+
+        return x
+
+class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge(nn.Module):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=None,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.vit_blks = vit_blks
+
+        assert use_fusion_blk
+        assert len(vit_blks) == 2
+
+        # copied vit settings from https://github.dev/facebookresearch/dinov2
+        nh = num_heads
+        dim = embed_dim
+        qkv_bias = True
+        attn_drop = proj_drop = 0.0
+        qk_scale = None  # TODO, double check
+
+        if False: # abla
+            for blk in self.vit_blks:
+                attn_3d = xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(  # ! raw 3D attn layer
+                    dim,
+                    num_heads=num_heads,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    attn_drop=attn_drop,
+                    proj_drop=proj_drop)
+                blk.attn = self_cross_attn(blk.attn, attn_3d)
+
+    def forward(self, x):
+        """x: B N C, where N = H*W tokens. Just raw ViT forward pass
+        """
+
+        # ! move the below to the front of the first call
+        B, N, C = x.shape  # has [cls] token in N
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B N C
+
+        return x
+
+class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_B_3L_C(TriplaneFusionBlockv4_nested_init_from_dino_lite_merge):
+    # on roll out + B 3L C
+    def __init__(self, vit_blks, num_heads, embed_dim, use_fusion_blk=True, fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested, init_from_dino=True, *args, **kwargs) -> None:
+        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk, fusion_ca_blk, init_from_dino, *args, **kwargs)
+
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # ! move the below to the front of the first call
+
+        # B, N, C = x.shape  # has [cls] token in N
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        x = x.reshape(B, group_size*N, C)
+
+        for blk in self.vit_blks:
+            x = blk(x)  # B N C
+
+        x = x.reshape(B, group_size, N, C) # outer loop tradition
+
+        return x
+
+class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_B_3L_C_withrollout(TriplaneFusionBlockv4_nested_init_from_dino_lite_merge):
+    # roll out + B 3L C
+    def __init__(self, vit_blks, num_heads, embed_dim, use_fusion_blk=True, fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested, init_from_dino=True, *args, **kwargs) -> None:
+        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk, fusion_ca_blk, init_from_dino, *args, **kwargs)
+
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # ! move the below to the front of the first call
+
+        # B, N, C = x.shape  # has [cls] token in N
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        x = x.reshape(B*group_size, N, C)
+        x = self.vit_blks[0](x)
+
+        x = x.reshape(B,group_size*N, C)
+        x = self.vit_blks[1](x)
+
+        x = x.reshape(B, group_size, N, C) # outer loop tradition
+
+        return x
+
+
+class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_add3DAttn(TriplaneFusionBlockv4_nested_init_from_dino):
+    # no roll out + 3D Attention
+    def __init__(self, vit_blks, num_heads, embed_dim, use_fusion_blk=True, fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested, init_from_dino=True, *args, **kwargs) -> None:
+        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk, fusion_ca_blk, init_from_dino, *args, **kwargs)
+
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        x = x.reshape(B, group_size*N, C)
+        x = self.vit_blks[0](x) # B 3 L C
+
+        # ! move the below to the front of the first call
+        x = x.reshape(B, group_size, N, C).reshape(B*group_size, N, C)
+        x = self.vit_blks[1](x) # has 3D attention
+        return x.reshape(B, group_size, N, C)
+
+        return x
+
+
+class TriplaneFusionBlockv5_ldm_addCA(nn.Module):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        assert use_fusion_blk
+
+        assert len(vit_blks) == 2
+
+        # ! rather than replacing, add a 3D attention block after.
+        # del self.vit_blks[
+        #     1].attn  # , self.vit_blks[1].ls1, self.vit_blks[1].norm1
+        self.norm_for_atten_3d = deepcopy(self.vit_blks[1].norm1)
+
+        # copied vit settings from https://github.dev/facebookresearch/dinov2
+        nh = num_heads
+        dim = embed_dim
+
+        mlp_ratio = 4  # defined for all dino2 model
+        qkv_bias = True
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        drop_path_rate = 0.3  # default setting
+        attn_drop = proj_drop = 0.0
+        qk_scale = None  # TODO, double check
+
+        self.attn_3d = xformer_Conv3D_Aware_CrossAttention_xygrid(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop)
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+
+        flatten_token = lambda x: x.reshape(B * group_size, N, C)
+        unflatten_token = lambda x: x.reshape(B, group_size, N, C)
+
+        x = flatten_token(x)
+        x = self.vit_blks[0](x)
+
+        x = unflatten_token(x)
+        x = self.attn_3d(self.norm_for_atten_3d(x)) + x
+
+        x = flatten_token(x)
+        x = self.vit_blks[1](x)
+
+        return unflatten_token(x)
+
+
+class TriplaneFusionBlockv6_ldm_addCA_Init3DAttnfrom2D(
+        TriplaneFusionBlockv5_ldm_addCA):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk,
+                         fusion_ca_blk, *args, **kwargs)
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+
+        flatten_token = lambda x: x.reshape(B * group_size, N, C)
+        unflatten_token = lambda x: x.reshape(B, group_size, N, C)
+
+        x = flatten_token(x)
+        x = self.vit_blks[0](x)
+
+        x = unflatten_token(x)
+        x = self.attn_3d(self.norm_for_atten_3d(x)) + x
+
+        x = flatten_token(x)
+        x = self.vit_blks[1](x)
+
+        return unflatten_token(x)
+
+
+class TriplaneFusionBlockv5_ldm_add_dualCA(nn.Module):
+
+    def __init__(self,
+                 vit_blks,
+                 num_heads,
+                 embed_dim,
+                 use_fusion_blk=True,
+                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
+                 *args,
+                 **kwargs) -> None:
+        super().__init__()
+
+        self.num_branches = 3  # triplane
+        self.vit_blks = vit_blks
+
+        assert use_fusion_blk
+
+        assert len(vit_blks) == 2
+
+        # ! rather than replacing, add a 3D attention block after.
+        # del self.vit_blks[
+        #     1].attn  # , self.vit_blks[1].ls1, self.vit_blks[1].norm1
+        self.norm_for_atten_3d_0 = deepcopy(self.vit_blks[0].norm1)
+        self.norm_for_atten_3d_1 = deepcopy(self.vit_blks[1].norm1)
+
+        # copied vit settings from https://github.dev/facebookresearch/dinov2
+        nh = num_heads
+        dim = embed_dim
+
+        mlp_ratio = 4  # defined for all dino2 model
+        qkv_bias = True
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        drop_path_rate = 0.3  # default setting
+        attn_drop = proj_drop = 0.0
+        qk_scale = None  # TODO, double check
+
+        self.attn_3d_0 = xformer_Conv3D_Aware_CrossAttention_xygrid(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop)
+
+        self.attn_3d_1 = deepcopy(self.attn_3d_0)
+
+    def forward(self, x):
+        """x: B 3 N C, where N = H*W tokens
+        """
+
+        # self attention, by merging the triplane channel into B for parallel computation
+
+        # ! move the below to the front of the first call
+        B, group_size, N, C = x.shape  # has [cls] token in N
+        assert group_size == 3, 'triplane'
+
+        flatten_token = lambda x: x.reshape(B * group_size, N, C)
+        unflatten_token = lambda x: x.reshape(B, group_size, N, C)
+
+        x = flatten_token(x)
+        x = self.vit_blks[0](x)
+
+        x = unflatten_token(x)
+        x = self.attn_3d_0(self.norm_for_atten_3d_0(x)) + x
+
+        x = flatten_token(x)
+        x = self.vit_blks[1](x)
+
+        x = unflatten_token(x)
+        x = self.attn_3d_1(self.norm_for_atten_3d_1(x)) + x
+
+        return unflatten_token(x)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0], ) + (1, ) * (
+        x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(
+        shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+
+class Mlp(nn.Module):
+
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=4.,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        # self.attn = Attention(dim,
+        self.attn = MemEffAttention(dim,
+                              num_heads=num_heads,
+                              qkv_bias=qkv_bias,
+                              qk_scale=qk_scale,
+                              attn_drop=attn_drop,
+                              proj_drop=drop)
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer,
+                       drop=drop)
+
+    def forward(self, x, return_attention=False):
+        y, attn = self.attn(self.norm1(x))
+        if return_attention:
+            return attn
+        x = x + self.drop_path(y)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        num_patches = (img_size // patch_size) * (img_size // patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B, C, L -> B, L, C
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """ Vision Transformer """
+
+    def __init__(self,
+                 img_size=[224],
+                 patch_size=16,
+                 in_chans=3,
+                 num_classes=0,
+                 embed_dim=768,
+                 depth=12,
+                 num_heads=12,
+                 mlp_ratio=4.,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 norm_layer='nn.LayerNorm',
+                 patch_embedding=True,
+                 cls_token=True,
+                 pixel_unshuffle=False,
+                 **kwargs):
+        super().__init__()
+        self.num_features = self.embed_dim = embed_dim
+        self.patch_size = patch_size
+
+        # if norm_layer == 'nn.LayerNorm':
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        if patch_embedding:
+            self.patch_embed = PatchEmbed(img_size=img_size[0],
+                                          patch_size=patch_size,
+                                          in_chans=in_chans,
+                                          embed_dim=embed_dim)
+            num_patches = self.patch_embed.num_patches
+            self.img_size = self.patch_embed.img_size
+        else:
+            self.patch_embed = None
+            self.img_size = img_size[0]
+            num_patches = (img_size[0] // patch_size) * (img_size[0] //
+                                                         patch_size)
+
+        if cls_token:
+            self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, num_patches + 1, embed_dim))
+        else:
+            self.cls_token = None
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, num_patches, embed_dim))
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
+               ]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(dim=embed_dim,
+                  num_heads=num_heads,
+                  mlp_ratio=mlp_ratio,
+                  qkv_bias=qkv_bias,
+                  qk_scale=qk_scale,
+                  drop=drop_rate,
+                  attn_drop=attn_drop_rate,
+                  drop_path=dpr[i],
+                  norm_layer=norm_layer) for i in range(depth)
+        ])
+        self.norm = norm_layer(embed_dim)
+
+        # Classifier head
+        self.head = nn.Linear(
+            embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        trunc_normal_(self.pos_embed, std=.02)
+        if cls_token:
+            trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+
+        # if pixel_unshuffle:
+        #     self.decoder_pred = nn.Linear(embed_dim,
+        #                                 patch_size**2 * out_chans,
+        #                                 bias=True)  # decoder to patch
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        patch_pos_embed = self.pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        w0, h0 = w0 + 0.1, h0 + 0.1
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
+                                    dim).permute(0, 3, 1, 2),
+            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
+            mode='bicubic',
+        )
+        assert int(w0) == patch_pos_embed.shape[-2] and int(
+            h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(2, -1, dim)
+
+        if self.cls_token is not None:
+            class_pos_embed = self.pos_embed[:, 0]
+            return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed),
+                             dim=1)
+        return patch_pos_embed
+
+    def prepare_tokens(self, x):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)  # patch linear embedding
+
+        # add the [CLS] token to the embed patch tokens
+        cls_tokens = self.cls_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        # add positional encoding to each token
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        return self.pos_drop(x)
+
+    def forward(self, x):
+        x = self.prepare_tokens(x)
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.norm(x)
+        return x[:, 1:]  # return spatial feature maps, not the [CLS] token
+        # return x[:, 0]
+
+    def get_last_selfattention(self, x):
+        x = self.prepare_tokens(x)
+        for i, blk in enumerate(self.blocks):
+            if i < len(self.blocks) - 1:
+                x = blk(x)
+            else:
+                # return attention of the last block
+                return blk(x, return_attention=True)
+
+    def get_intermediate_layers(self, x, n=1):
+        x = self.prepare_tokens(x)
+        # we return the output tokens from the `n` last blocks
+        output = []
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if len(self.blocks) - i <= n:
+                output.append(self.norm(x))
+        return output
+
+
+def vit_tiny(patch_size=16, **kwargs):
+    model = VisionTransformer(patch_size=patch_size,
+                              embed_dim=192,
+                              depth=12,
+                              num_heads=3,
+                              mlp_ratio=4,
+                              qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
+                              **kwargs)
+    return model
+
+
+def vit_small(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),  # type: ignore
+        **kwargs)
+    return model
+
+
+def vit_base(patch_size=16, **kwargs):
+    model = VisionTransformer(patch_size=patch_size,
+                              embed_dim=768,
+                              depth=12,
+                              num_heads=12,
+                              mlp_ratio=4,
+                              qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
+                              **kwargs)
+    return model
+
+
+vits = vit_small
+vitb = vit_base
diff --git a/vit/vit_3d.py b/vit/vit_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..11c4592fef204ca91629e19f9ced12ba4dd9ebce
--- /dev/null
+++ b/vit/vit_3d.py
@@ -0,0 +1,203 @@
+# https://gist.github.com/lucidrains/5193d38d1d889681dd42feb847f1f6da
+# https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_3d.py
+
+import torch
+from torch import nn
+from pdb import set_trace as st
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+
+from .vit_with_mask import Transformer
+
+# helpers
+
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+
+# classes
+
+
+# class PreNorm(nn.Module):
+
+#     def __init__(self, dim, fn):
+#         super().__init__()
+#         self.norm = nn.LayerNorm(dim)
+#         self.fn = fn
+
+#     def forward(self, x, **kwargs):
+#         return self.fn(self.norm(x), **kwargs)
+
+
+# class FeedForward(nn.Module):
+
+#     def __init__(self, dim, hidden_dim, dropout=0.):
+#         super().__init__()
+#         self.net = nn.Sequential(nn.Linear(dim, hidden_dim), nn.GELU(),
+#                                  nn.Dropout(dropout),
+#                                  nn.Linear(hidden_dim,
+#                                            dim), nn.Dropout(dropout))
+
+#     def forward(self, x):
+#         return self.net(x)
+
+
+# class Attention(nn.Module):
+
+#     def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
+#         super().__init__()
+#         inner_dim = dim_head * heads
+#         project_out = not (heads == 1 and dim_head == dim)
+
+#         self.heads = heads
+#         self.scale = dim_head**-0.5
+
+#         self.attend = nn.Softmax(dim=-1)
+#         self.dropout = nn.Dropout(dropout)
+
+#         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
+
+#         self.to_out = nn.Sequential(
+#             nn.Linear(inner_dim, dim),
+#             nn.Dropout(dropout)) if project_out else nn.Identity()
+
+#     def forward(self, x):
+#         qkv = self.to_qkv(x).chunk(3, dim=-1)
+#         q, k, v = map(
+#             lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), qkv)
+
+#         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+#         attn = self.attend(dots)
+#         attn = self.dropout(attn)
+
+#         out = torch.matmul(attn, v)
+#         out = rearrange(out, 'b h n d -> b n (h d)')
+#         return self.to_out(out)
+
+
+# class Transformer(nn.Module):
+
+#     def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
+#         super().__init__()
+#         self.layers = nn.ModuleList([])
+#         for _ in range(depth):
+#             self.layers.append(
+#                 nn.ModuleList([
+#                     PreNorm(
+#                         dim,
+#                         Attention(dim,
+#                                   heads=heads,
+#                                   dim_head=dim_head,
+#                                   dropout=dropout)),
+#                     PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
+#                 ]))
+
+#     def forward(self, x):
+#         for attn, ff in self.layers:
+#             x = attn(x) + x
+#             x = ff(x) + x
+#         return x
+
+
+# https://gist.github.com/lucidrains/213d2be85d67d71147d807737460baf4
+class ViTVoxel(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dropout = 0., emb_dropout = 0.):
+        super().__init__()
+        assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
+        num_patches = (image_size // patch_size) ** 3
+        patch_dim = channels * patch_size ** 3
+
+        self.patch_size = patch_size
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.patch_to_embedding = nn.Linear(patch_dim, dim)
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout)
+
+        self.to_cls_token = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, mlp_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(mlp_dim, num_classes),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, img, mask = None):
+        p = self.patch_size
+
+        x = rearrange(img, 'b c (h p1) (w p2) (d p3) -> b (h w d) (p1 p2 p3 c)', p1 = p, p2 = p, p3 = p)
+        x = self.patch_to_embedding(x)
+
+        cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding
+        x = self.dropout(x)
+
+        x = self.transformer(x, mask)
+
+        x = self.to_cls_token(x[:, 0])
+        return self.mlp_head(x)
+
+
+class ViTTriplane(nn.Module):
+    def __init__(self, *, image_size, triplane_size, image_patch_size, triplane_patch_size, num_classes, dim, depth, heads, mlp_dim, patch_embed=False, channels = 3, dim_head = 64,  dropout = 0., emb_dropout = 0.):
+        super().__init__()
+        assert image_size % image_patch_size == 0, 'image dimensions must be divisible by the patch size'
+
+        num_patches = (image_size // image_patch_size) ** 2 * triplane_size # 14*14*3
+        # patch_dim = channels * image_patch_size ** 3
+
+        self.patch_size = image_patch_size
+        self.triplane_patch_size = triplane_patch_size
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+
+        self.patch_embed = patch_embed
+        # if self.patch_embed:
+        patch_dim = channels * image_patch_size ** 2 * triplane_patch_size # 1
+        self.patch_to_embedding = nn.Linear(patch_dim, dim)
+
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        self.to_cls_token = nn.Identity()
+
+        # self.mlp_head = nn.Sequential(
+        #     nn.LayerNorm(dim),
+        #     nn.Linear(dim, mlp_dim),
+        #     nn.GELU(),
+        #     nn.Dropout(dropout),
+        #     nn.Linear(mlp_dim, num_classes),
+        #     nn.Dropout(dropout)
+        # )
+
+    def forward(self, triplane, mask = None):
+        p = self.patch_size
+        p_3d = self.triplane_patch_size
+
+        x = rearrange(triplane, 'b c (h p1) (w p2) (d p3) -> b (h w d) (p1 p2 p3 c)', p1 = p, p2 = p, p3 = p_3d)
+
+        # if self.patch_embed:
+        x = self.patch_to_embedding(x) # B 14*14*4 768
+
+        cls_tokens = self.cls_token.expand(triplane.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding
+        x = self.dropout(x)
+
+        x = self.transformer(x, mask)
+
+        return x[:, 1:]
+
+        # x = self.to_cls_token(x[:, 0])
+        # return self.mlp_head(x)
\ No newline at end of file
diff --git a/vit/vit_triplane.py b/vit/vit_triplane.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d2671b68ddb8c84d8644514155ef22ed94a7da9
--- /dev/null
+++ b/vit/vit_triplane.py
@@ -0,0 +1,1677 @@
+import math
+from pathlib import Path
+# from pytorch3d.ops import create_sphere
+import torchvision
+import point_cloud_utils as pcu
+from tqdm import trange
+import random
+import einops
+from einops import rearrange
+import torch
+from torch import nn
+from torch.nn import functional as F
+import numpy as np
+
+from functools import partial
+from torch.profiler import profile, record_function, ProfilerActivity
+
+from nsr.networks_stylegan2 import Generator as StyleGAN2Backbone
+from nsr.volumetric_rendering.renderer import ImportanceRenderer, ImportanceRendererfg_bg
+from nsr.volumetric_rendering.ray_sampler import RaySampler
+from nsr.triplane import OSGDecoder, Triplane, Triplane_fg_bg_plane
+# from nsr.losses.helpers import ResidualBlock
+from utils.dust3r.heads.dpt_head import create_dpt_head_ln3diff
+from utils.nerf_utils import get_embedder
+from vit.vision_transformer import TriplaneFusionBlockv4_nested, TriplaneFusionBlockv4_nested_init_from_dino_lite, TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_B_3L_C_withrollout, VisionTransformer, TriplaneFusionBlockv4_nested_init_from_dino
+
+from .vision_transformer import Block, VisionTransformer
+from .utils import trunc_normal_
+
+from guided_diffusion import dist_util, logger
+
+from pdb import set_trace as st
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from torch_utils.components import PixelShuffleUpsample, ResidualBlock, Upsample, PixelUnshuffleUpsample, Conv3x3TriplaneTransformation
+from torch_utils.distributions.distributions import DiagonalGaussianDistribution
+from nsr.superresolution import SuperresolutionHybrid2X, SuperresolutionHybrid4X
+
+from torch.nn.parameter import Parameter, UninitializedParameter, UninitializedBuffer
+
+from nsr.common_blks import ResMlp
+from timm.models.vision_transformer import PatchEmbed, Mlp
+from .vision_transformer import *
+
+from dit.dit_models import get_2d_sincos_pos_embed
+from dit.dit_decoder import DiTBlock2
+from torch import _assert
+from itertools import repeat
+import collections.abc
+
+from nsr.srt.layers import Transformer as SRT_TX
+from nsr.srt.layers import PreNorm
+
+# from diffusers.models.upsampling import Upsample2D
+
+from torch_utils.components import NearestConvSR
+from timm.models.vision_transformer import PatchEmbed
+
+from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply, build_rotation
+
+from nsr.gs import GaussianRenderer
+
+from utils.dust3r.heads import create_dpt_head
+
+from ldm.modules.attention import MemoryEfficientCrossAttention, CrossAttention
+
+# from nsr.geometry.camera.perspective_camera import PerspectiveCamera
+# from nsr.geometry.render.neural_render import NeuralRender
+# from nsr.geometry.rep_3d.flexicubes_geometry import FlexiCubesGeometry
+# from utils.mesh_util import xatlas_uvmap
+
+
+# From PyTorch internals
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+            return tuple(x)
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+
+
+def approx_gelu():
+    return nn.GELU(approximate="tanh")
+
+
+def init_gaussian_prediction(gaussian_pred_mlp):
+
+    # https://github.com/szymanowiczs/splatter-image/blob/98b465731c3273bf8f42a747d1b6ce1a93faf3d6/configs/dataset/chairs.yaml#L15
+
+    out_channels = [3, 1, 3, 4, 3]  # xyz, opacity, scale, rotation, rgb
+    scale_inits = [  # ! avoid affecting final value (offset) 
+        0,  #xyz_scale
+        0.0,  #cfg.model.opacity_scale, 
+        # 0.001,  #cfg.model.scale_scale,
+        0,  #cfg.model.scale_scale,
+        1,  # rotation
+        0
+    ]  # rgb
+
+    bias_inits = [
+        0.0,  # cfg.model.xyz_bias, no deformation here
+        0,  # cfg.model.opacity_bias, sigmoid(0)=0.5 at init
+        -2.5,  # scale_bias
+        0.0,  # rotation
+        0.5
+    ]  # rgb
+
+    start_channels = 0
+
+    # for out_channel, b, s in zip(out_channels, bias, scale):
+    for out_channel, b, s in zip(out_channels, bias_inits, scale_inits):
+        # nn.init.xavier_uniform_(
+        #     self.superresolution['conv_sr'].dpt.head[-1].weight[
+        #         start_channels:start_channels + out_channel, ...], s)
+        nn.init.constant_(
+            gaussian_pred_mlp.weight[start_channels:start_channels +
+                                     out_channel, ...], s)
+        nn.init.constant_(
+            gaussian_pred_mlp.bias[start_channels:start_channels +
+                                   out_channel], b)
+        start_channels += out_channel
+
+
+class PatchEmbedTriplane(nn.Module):
+    """ GroupConv patchembeder on triplane
+    """
+
+    def __init__(
+        self,
+        img_size=32,
+        patch_size=2,
+        in_chans=4,
+        embed_dim=768,
+        norm_layer=None,
+        flatten=True,
+        bias=True,
+        plane_n=3,
+    ):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        self.plane_n = plane_n
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.grid_size = (img_size[0] // patch_size[0],
+                          img_size[1] // patch_size[1])
+        self.num_patches = self.grid_size[0] * self.grid_size[1]
+        self.flatten = flatten
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim * self.plane_n,
+                              kernel_size=patch_size,
+                              stride=patch_size,
+                              bias=bias,
+                              groups=self.plane_n)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        # st()
+        B, C, H, W = x.shape
+        _assert(
+            H == self.img_size[0],
+            f"Input image height ({H}) doesn't match model ({self.img_size[0]})."
+        )
+        _assert(
+            W == self.img_size[1],
+            f"Input image width ({W}) doesn't match model ({self.img_size[1]})."
+        )
+        x = self.proj(x)  # B 3*C token_H token_W
+
+        x = x.reshape(B, x.shape[1] // self.plane_n, self.plane_n, x.shape[-2],
+                      x.shape[-1])  # B C 3 H W
+
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BC3HW -> B 3HW C
+        x = self.norm(x)
+        return x
+
+
+# https://github.com/facebookresearch/MCC/blob/main/mcc_model.py#L81
+class XYZPosEmbed(nn.Module):
+    """ Masked Autoencoder with VisionTransformer backbone
+    """
+
+    def __init__(self, embed_dim, multires=10):
+        super().__init__()
+        self.embed_dim = embed_dim
+        # no [cls] token here.
+
+        # ! use fixed PE here
+        self.embed_fn, self.embed_input_ch = get_embedder(multires)
+        # st()
+
+        # self.two_d_pos_embed = nn.Parameter(
+        #     # torch.zeros(1, 64 + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding
+        #     torch.zeros(1, 64, embed_dim), requires_grad=False)  # fixed sin-cos embedding
+
+        # self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        # self.win_size = 8
+
+        self.xyz_projection = nn.Linear(self.embed_input_ch, embed_dim)
+
+        # self.blocks = nn.ModuleList([
+        #     Block(embed_dim, num_heads=12, mlp_ratio=2.0, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6))
+        #     for _ in range(1)
+        # ])
+
+        # self.invalid_xyz_token = nn.Parameter(torch.zeros(embed_dim,))
+
+        # self.initialize_weights()
+
+    # def initialize_weights(self):
+    #     # torch.nn.init.normal_(self.cls_token, std=.02)
+
+    #     two_d_pos_embed = get_2d_sincos_pos_embed(self.two_d_pos_embed.shape[-1], 8, cls_token=False)
+    #     self.two_d_pos_embed.data.copy_(torch.from_numpy(two_d_pos_embed).float().unsqueeze(0))
+
+    #     torch.nn.init.normal_(self.invalid_xyz_token, std=.02)
+
+    def forward(self, xyz):
+        xyz = self.embed_fn(xyz)  # PE encoding
+        xyz = self.xyz_projection(xyz)  # linear projection
+        return xyz
+
+
+class gaussian_prediction(nn.Module):
+
+    def __init__(
+        self,
+        query_dim,
+    ) -> None:
+        super().__init__()
+        self.gaussian_pred = nn.Sequential(
+            nn.SiLU(), nn.Linear(query_dim, 14,
+                                 bias=True))  # TODO, init require
+
+        self.init_gaussian_prediction()
+
+    def init_gaussian_prediction(self):
+
+        # https://github.com/szymanowiczs/splatter-image/blob/98b465731c3273bf8f42a747d1b6ce1a93faf3d6/configs/dataset/chairs.yaml#L15
+
+        out_channels = [3, 1, 3, 4, 3]  # xyz, opacity, scale, rotation, rgb
+        scale_inits = [  # ! avoid affecting final value (offset) 
+            0,  #xyz_scale
+            0.0,  #cfg.model.opacity_scale, 
+            # 0.001,  #cfg.model.scale_scale,
+            0,  #cfg.model.scale_scale,
+            1.0,  # rotation
+            0
+        ]  # rgb
+
+        bias_inits = [
+            0.0,  # cfg.model.xyz_bias, no deformation here
+            0,  # cfg.model.opacity_bias, sigmoid(0)=0.5 at init
+            -2.5,  # scale_bias
+            0.0,  # rotation
+            0.5
+        ]  # rgb
+
+        start_channels = 0
+
+        # for out_channel, b, s in zip(out_channels, bias, scale):
+        for out_channel, b, s in zip(out_channels, bias_inits, scale_inits):
+            # nn.init.xavier_uniform_(
+            #     self.superresolution['conv_sr'].dpt.head[-1].weight[
+            #         start_channels:start_channels + out_channel, ...], s)
+            nn.init.constant_(
+                self.gaussian_pred[1].weight[start_channels:start_channels +
+                                             out_channel, ...], s)
+            nn.init.constant_(
+                self.gaussian_pred[1].bias[start_channels:start_channels +
+                                           out_channel], b)
+            start_channels += out_channel
+
+    def forward(self, x):
+
+        return self.gaussian_pred(x)
+
+
+class surfel_prediction(nn.Module):
+    # for 2dgs
+
+    def __init__(
+        self,
+        query_dim,
+    ) -> None:
+        super().__init__()
+        self.gaussian_pred = nn.Sequential(
+            nn.SiLU(), nn.Linear(query_dim, 13,
+                                 bias=True))  # TODO, init require
+
+        self.init_gaussian_prediction()
+
+    def init_gaussian_prediction(self):
+
+        # https://github.com/szymanowiczs/splatter-image/blob/98b465731c3273bf8f42a747d1b6ce1a93faf3d6/configs/dataset/chairs.yaml#L15
+
+        out_channels = [3, 1, 2, 4, 3]  # xyz, opacity, scale, rotation, rgb
+        scale_inits = [  # ! avoid affecting final value (offset) 
+            0,  #xyz_scale
+            0.0,  #cfg.model.opacity_scale, 
+            # 0.001,  #cfg.model.scale_scale,
+            0,  #cfg.model.scale_scale,
+            1.0,  # rotation
+            0
+        ]  # rgb
+
+        bias_inits = [
+            0.0,  # cfg.model.xyz_bias, no deformation here
+            0,  # cfg.model.opacity_bias, sigmoid(0)=0.5 at init
+            -2.5,  # scale_bias
+            0,  # scale bias, also 0
+            0.0,  # rotation
+            0.5
+        ]  # rgb
+
+        start_channels = 0
+
+        # for out_channel, b, s in zip(out_channels, bias, scale):
+        for out_channel, b, s in zip(out_channels, bias_inits, scale_inits):
+            # nn.init.xavier_uniform_(
+            #     self.superresolution['conv_sr'].dpt.head[-1].weight[
+            #         start_channels:start_channels + out_channel, ...], s)
+            nn.init.constant_(
+                self.gaussian_pred[1].weight[start_channels:start_channels +
+                                             out_channel, ...], s)
+            nn.init.constant_(
+                self.gaussian_pred[1].bias[start_channels:start_channels +
+                                           out_channel], b)
+            start_channels += out_channel
+
+    def forward(self, x):
+
+        return self.gaussian_pred(x)
+
+
+class pointInfinityWriteCA(gaussian_prediction):
+
+    def __init__(self,
+                 query_dim,
+                 context_dim,
+                 heads=8,
+                 dim_head=64,
+                 dropout=0.0) -> None:
+        super().__init__(query_dim=query_dim)
+        self.write_ca = MemoryEfficientCrossAttention(query_dim, context_dim,
+                                                      heads, dim_head, dropout)
+
+    def forward(self, x, z, return_x=False):
+        # x: point to write
+        # z: extracted latent
+        x = self.write_ca(x, z)  # write from z to x
+        if return_x:
+            return self.gaussian_pred(x), x  # ! integrate it into dit?
+        else:
+            return self.gaussian_pred(x)  # ! integrate it into dit?
+
+
+class pointInfinityWriteCA_cascade(pointInfinityWriteCA):
+    # gradually (in 6 times) add deformation offsets to the initialized canonical pts, follow PI
+    def __init__(self,
+                 vit_depth,
+                 query_dim,
+                 context_dim,
+                 heads=8,
+                 dim_head=64,
+                 dropout=0) -> None:
+        super().__init__(query_dim, context_dim, heads, dim_head, dropout)
+
+        del self.write_ca
+        # query_dim = 384 # to speed up CA compute
+        write_ca_interval = 12 // 4
+        # self.deform_pred = nn.Sequential( # to-gaussian layer
+        #     nn.SiLU(), nn.Linear(query_dim, 3, bias=True)) # TODO, init require
+
+        # query_dim = 384 here
+        self.write_ca_blocks = nn.ModuleList([
+            MemoryEfficientCrossAttention(query_dim, context_dim,
+                                          heads=heads)  # make it lite
+            for _ in range(write_ca_interval)
+            # for _ in range(write_ca_interval)
+        ])
+        self.hooks = [3, 7, 11]  # hard coded for now
+        # [(vit_depth * 1 // 3) - 1, (vit_depth * 2 // 4) - 1, (vit_depth * 3 // 4) - 1,
+        #             vit_depth - 1]
+
+    def forward(self, x: torch.Tensor, z: list):
+        # x is the canonical point
+        # z: extracted latent (for different layers), all layers in dit
+        # TODO, optimize memory, no need to return all layers?
+        # st()
+
+        z = [z[hook] for hook in self.hooks]
+        # st()
+
+        for idx, ca_blk in enumerate(self.write_ca_blocks):
+            x = x + ca_blk(x, z[idx])  # learn residual feature
+
+        return self.gaussian_pred(x)
+
+
+def create_sphere(radius, num_points):
+    # Generate spherical coordinates
+    phi = torch.linspace(0, 2 * torch.pi, num_points)
+    theta = torch.linspace(0, torch.pi, num_points)
+    phi, theta = torch.meshgrid(phi, theta, indexing='xy')
+
+    # Convert spherical coordinates to Cartesian coordinates
+    x = radius * torch.sin(theta) * torch.cos(phi)
+    y = radius * torch.sin(theta) * torch.sin(phi)
+    z = radius * torch.cos(theta)
+
+    # Stack x, y, z coordinates
+    points = torch.stack([x.flatten(), y.flatten(), z.flatten()], dim=1)
+
+    return points
+
+
+class GS_Adaptive_Write_CA(nn.Module):
+
+    def __init__(
+            self,
+            query_dim,
+            context_dim,
+            f=4,  # upsampling ratio
+            heads=8,
+            dim_head=64,
+            dropout=0.0) -> None:
+        super().__init__()
+
+        self.f = f
+        self.write_ca = MemoryEfficientCrossAttention(query_dim, context_dim,
+                                                      heads, dim_head, dropout)
+        self.gaussian_residual_pred = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(query_dim, 14,
+                      bias=True))  # predict residual, before activations
+
+        # ! hard coded
+        self.scene_extent = 0.9  # g-buffer, [-0.45, 0.45]
+        self.percent_dense = 0.01  # 3dgs official value
+        self.residual_offset_act = lambda x: torch.tanh(
+            x) * self.scene_extent * 0.015  # avoid large deformation
+
+        init_gaussian_prediction(self.gaussian_residual_pred[1])
+
+    # def densify_and_split(self, gaussians_base, base_gaussian_xyz_embed):
+
+    def forward(self,
+                gaussians_base,
+                gaussian_base_pre_activate,
+                gaussian_base_feat,
+                xyz_embed_fn,
+                shrink_scale=True):
+        # gaussians_base: xyz_base after activations and deform offset
+        # xyz_base: original features (before activations)
+
+        # ! use point embedder, or other features?
+        # base_gaussian_xyz_embed = xyz_embed_fn(gaussians_base[..., :3])
+
+        # x = self.densify_and_split(gaussians_base, base_gaussian_xyz_embed)
+
+        # ! densify
+        B, N = gaussians_base.shape[:2]  # gaussians upsample factor
+        # n_init_points = self.get_xyz.shape[0]
+
+        pos, opacity, scaling, rotation = gaussians_base[
+            ..., 0:3], gaussians_base[..., 3:4], gaussians_base[
+                ..., 4:7], gaussians_base[..., 7:11]
+
+        # ! filter clone/densify based on scaling range
+        split_mask = scaling.max(
+            dim=-1
+        )[0] > self.scene_extent * self.percent_dense  # shape: B 4096
+        # clone_mask = ~split_mask
+
+        stds = scaling.repeat_interleave(self.f, dim=1)  #  0 0 1 1 2 2...
+        means = torch.zeros_like(stds)
+        samples = torch.normal(mean=means, std=stds)  # B f*N 3
+
+        # rots = build_rotation(rotation).repeat(N, 1, 1)
+        # rots = rearrange(build_rotation(rearrange(rotation, 'B N ... -> (B N) ...')), '(B N) ... -> B N ...', B=B, N=N)
+        # rots = rots.repeat_interleave(self.f, dim=1) # B f*N 3 3
+
+        # torch.bmm only supports ndim=3 Tensor
+        # new_xyz = torch.matmul(rots, samples.unsqueeze(-1)).squeeze(-1) + pos.repeat_interleave(self.f, dim=1)
+        new_xyz = samples + pos.repeat_interleave(
+            self.f, dim=1)  # ! no rotation for now
+        # new_xyz: B f*N 3
+
+        # ! new points to features
+        new_xyz_embed = xyz_embed_fn(new_xyz)
+        new_gaussian_embed = self.write_ca(
+            new_xyz_embed, gaussian_base_feat)  # write from z to x
+
+        # ! predict gaussians residuals
+        gaussian_residual_pre_activate = self.gaussian_residual_pred(
+            new_gaussian_embed)
+
+        # ! add back. how to deal with new rotations? check the range first.
+        # scaling and rotation.
+        if shrink_scale:
+            gaussian_base_pre_activate[split_mask][
+                4:7] -= 1  # reduce scale for those points
+
+        gaussian_base_pre_activate_repeat = gaussian_base_pre_activate.repeat_interleave(
+            self.f, dim=1)
+
+        # new scaling
+        # ! pre-activate scaling value, shall be negative? since more values are 0.1 before softplus.
+        # TODO wrong here, shall get new scaling before repeat
+        gaussians = gaussian_residual_pre_activate + gaussian_base_pre_activate_repeat  # learn the residual
+
+        new_gaussians_pos = new_xyz + self.residual_offset_act(
+            gaussians[..., :3])
+
+        return gaussians, new_gaussians_pos  # return positions independently
+
+
+class GS_Adaptive_Read_Write_CA(nn.Module):
+
+    def __init__(
+            self,
+            query_dim,
+            context_dim,
+            mlp_ratio,
+            vit_heads,
+            f=4,  # upsampling ratio
+            heads=8,
+            dim_head=64,
+            dropout=0.0,
+            depth=2,
+            vit_blk=DiTBlock2) -> None:
+        super().__init__()
+
+        self.f = f
+        self.read_ca = MemoryEfficientCrossAttention(query_dim, context_dim,
+                                                     heads, dim_head, dropout)
+
+        # more dit blocks
+        self.point_infinity_blocks = nn.ModuleList([
+            vit_blk(context_dim, num_heads=vit_heads, mlp_ratio=mlp_ratio)
+            for _ in range(depth)  # since dit-b here
+        ])
+
+        self.write_ca = MemoryEfficientCrossAttention(query_dim, context_dim,
+                                                      heads, dim_head, dropout)
+
+        self.gaussian_residual_pred = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(query_dim, 14,
+                      bias=True))  # predict residual, before activations
+
+        # ! hard coded
+        self.scene_extent = 0.9  # g-buffer, [-0.45, 0.45]
+        self.percent_dense = 0.01  # 3dgs official value
+        self.residual_offset_act = lambda x: torch.tanh(
+            x) * self.scene_extent * 0.015  # avoid large deformation
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        init_gaussian_prediction(self.gaussian_residual_pred[1])
+
+        for block in self.point_infinity_blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+    # def densify_and_split(self, gaussians_base, base_gaussian_xyz_embed):
+
+    def forward(self, gaussians_base, gaussian_base_pre_activate,
+                gaussian_base_feat, latent_from_vit, vae_latent, xyz_embed_fn):
+        # gaussians_base: xyz_base after activations and deform offset
+        # xyz_base: original features (before activations)
+
+        # ========= START read CA ========
+        latent_from_vit = self.read_ca(latent_from_vit,
+                                       gaussian_base_feat)  # z_i -> z_(i+1)
+
+        for blk_idx, block in enumerate(self.point_infinity_blocks):
+            latent_from_vit = block(latent_from_vit,
+                                    vae_latent)  # vae_latent: c
+
+        # ========= END read CA ========
+
+        # ! use point embedder, or other features?
+        # base_gaussian_xyz_embed = xyz_embed_fn(gaussians_base[..., :3])
+
+        # x = self.densify_and_split(gaussians_base, base_gaussian_xyz_embed)
+
+        # ! densify
+        B, N = gaussians_base.shape[:2]  # gaussians upsample factor
+        # n_init_points = self.get_xyz.shape[0]
+
+        pos, opacity, scaling, rotation = gaussians_base[
+            ..., 0:3], gaussians_base[..., 3:4], gaussians_base[
+                ..., 4:7], gaussians_base[..., 7:11]
+
+        # ! filter clone/densify based on scaling range
+        split_mask = scaling.max(
+            dim=-1
+        )[0] > self.scene_extent * self.percent_dense  # shape: B 4096
+        # clone_mask = ~split_mask
+
+        stds = scaling.repeat_interleave(self.f, dim=1)  #  0 0 1 1 2 2...
+        means = torch.zeros_like(stds)
+        samples = torch.normal(mean=means, std=stds)  # B f*N 3
+
+        rots = build_rotation(rotation).repeat(N, 1, 1)
+        rots = rearrange(build_rotation(
+            rearrange(rotation, 'B N ... -> (B N) ...')),
+                         '(B N) ... -> B N ...',
+                         B=B,
+                         N=N)
+        rots = rots.repeat_interleave(self.f, dim=1)  # B f*N 3 3
+
+        # torch.bmm only supports ndim=3 Tensor
+        new_xyz = torch.matmul(
+            rots, samples.unsqueeze(-1)).squeeze(-1) + pos.repeat_interleave(
+                self.f, dim=1)
+        # new_xyz = samples + pos.repeat_interleave(
+        #     self.f, dim=1)  # ! no rotation for now
+        # new_xyz: B f*N 3
+
+        # ! new points to features
+        new_xyz_embed = xyz_embed_fn(new_xyz)
+        new_gaussian_embed = self.write_ca(
+            new_xyz_embed, latent_from_vit
+        )  # ! use z_(i+1), rather than gaussian_base_feat here
+
+        # ! predict gaussians residuals
+        gaussian_residual_pre_activate = self.gaussian_residual_pred(
+            new_gaussian_embed)
+
+        # ! add back. how to deal with new rotations? check the range first.
+        # scaling and rotation.
+        gaussian_base_pre_activate[split_mask][
+            4:7] -= 1  # reduce scale for those points
+        gaussian_base_pre_activate_repeat = gaussian_base_pre_activate.repeat_interleave(
+            self.f, dim=1)
+
+        # new scaling
+        # ! pre-activate scaling value, shall be negative? since more values are 0.1 before softplus.
+        # TODO wrong here, shall get new scaling before repeat
+        gaussians = gaussian_residual_pre_activate + gaussian_base_pre_activate_repeat  # learn the residual
+
+        new_gaussians_pos = new_xyz + self.residual_offset_act(
+            gaussians[..., :3])
+
+        return gaussians, new_gaussians_pos, latent_from_vit, new_gaussian_embed  # return positions independently
+
+
+class GS_Adaptive_Read_Write_CA_adaptive(GS_Adaptive_Read_Write_CA):
+
+    def __init__(self,
+                 query_dim,
+                 context_dim,
+                 mlp_ratio,
+                 vit_heads,
+                 f=4,
+                 heads=8,
+                 dim_head=64,
+                 dropout=0,
+                 depth=2,
+                 vit_blk=DiTBlock2) -> None:
+        super().__init__(query_dim, context_dim, mlp_ratio, vit_heads, f,
+                         heads, dim_head, dropout, depth, vit_blk)
+
+        # assert self.f == 6
+
+    def forward(self, gaussians_base, gaussian_base_pre_activate,
+                gaussian_base_feat, latent_from_vit, vae_latent, xyz_embed_fn):
+        # gaussians_base: xyz_base after activations and deform offset
+        # xyz_base: original features (before activations)
+
+        # ========= START read CA ========
+        latent_from_vit = self.read_ca(latent_from_vit,
+                                       gaussian_base_feat)  # z_i -> z_(i+1)
+
+        for blk_idx, block in enumerate(self.point_infinity_blocks):
+            latent_from_vit = block(latent_from_vit,
+                                    vae_latent)  # vae_latent: c
+
+        # ========= END read CA ========
+
+        # ! use point embedder, or other features?
+        # base_gaussian_xyz_embed = xyz_embed_fn(gaussians_base[..., :3])
+
+        # x = self.densify_and_split(gaussians_base, base_gaussian_xyz_embed)
+
+        # ! densify
+        B, N = gaussians_base.shape[:2]  # gaussians upsample factor
+        # n_init_points = self.get_xyz.shape[0]
+
+        pos, opacity, scaling, rotation = gaussians_base[
+            ..., 0:3], gaussians_base[..., 3:4], gaussians_base[
+                ..., 4:7], gaussians_base[..., 7:11]
+
+        # ! filter clone/densify based on scaling range
+
+        split_mask = scaling.max(
+            dim=-1
+        )[0] > self.scene_extent * self.percent_dense  # shape: B 4096
+
+        # clone_mask = ~split_mask
+
+        # stds = scaling.repeat_interleave(self.f, dim=1)  #  B 13824 3
+        # stds = scaling.unsqueeze(1).repeat_interleave(self.f, dim=1)  #  B 6 13824 3
+        stds = scaling  #  B 13824 3
+
+        # TODO, in mat form. axis aligned creation.
+        samples = torch.zeros(B, N, 3, 3).to(stds.device)
+
+        samples[..., 0, 0] = stds[..., 0]
+        samples[..., 1, 1] = stds[..., 1]
+        samples[..., 2, 2] = stds[..., 2]
+
+        eye_mat = torch.cat([torch.eye(3), -torch.eye(3)],
+                            0)  # 6 * 3, to put gaussians along the axis
+        eye_mat = eye_mat.reshape(1, 1, 6, 3).repeat(B, N, 1,
+                                                     1).to(stds.device)
+        samples = (eye_mat @ samples).squeeze(-1)
+
+        # st()
+        # means = torch.zeros_like(stds)
+        # samples = torch.normal(mean=means, std=stds)  # B f*N 3
+
+        rots = rearrange(build_rotation(
+            rearrange(rotation, 'B N ... -> (B N) ...')),
+                         '(B N) ... -> B N ...',
+                         B=B,
+                         N=N)
+        rots = rots.unsqueeze(2).repeat_interleave(self.f, dim=2)  # B f*N 3 3
+
+        # torch.bmm only supports ndim=3 Tensor
+        # new_xyz = torch.matmul(rots, samples.unsqueeze(-1)).squeeze(-1) + pos.repeat_interleave(self.f, dim=1)
+        # st()
+
+        # new_xyz = torch.matmul(rots, samples.unsqueeze(-1)).squeeze(-1) + pos.repeat_interleave(self.f, dim=1)
+        new_xyz = (rots @ samples.unsqueeze(-1)).squeeze(-1) + pos.unsqueeze(
+            2).repeat_interleave(self.f, dim=2)  # B N 6 3
+        new_xyz = rearrange(new_xyz, 'b n f c -> b (n f) c')
+
+        # ! not considering rotation here
+        # new_xyz = samples + pos.repeat_interleave(
+        #     self.f, dim=1)  # ! no rotation for now
+        # new_xyz: B f*N 3
+
+        # ! new points to features
+        new_xyz_embed = xyz_embed_fn(new_xyz)
+        new_gaussian_embed = self.write_ca(
+            new_xyz_embed, latent_from_vit
+        )  # ! use z_(i+1), rather than gaussian_base_feat here
+
+        # ! predict gaussians residuals
+        gaussian_residual_pre_activate = self.gaussian_residual_pred(
+            new_gaussian_embed)
+
+        # ! add back. how to deal with new rotations? check the range first.
+        # scaling and rotation.
+        # gaussian_base_pre_activate[split_mask][
+        #     4:7] -= 1  # reduce scale for those points
+
+        gaussian_base_pre_activate_repeat = gaussian_base_pre_activate.repeat_interleave(
+            self.f, dim=1)
+
+        # new scaling
+        # ! pre-activate scaling value, shall be negative? since more values are 0.1 before softplus.
+        # TODO wrong here, shall get new scaling before repeat
+        gaussians = gaussian_residual_pre_activate + gaussian_base_pre_activate_repeat  # learn the residual
+
+        # new_gaussians_pos = new_xyz + self.residual_offset_act(
+        #     gaussians[..., :3])
+
+        return gaussians, new_xyz, latent_from_vit, new_gaussian_embed  # return positions independently
+
+
+class GS_Adaptive_Read_Write_CA_adaptive_f14_prepend(
+        GS_Adaptive_Read_Write_CA_adaptive):
+
+    def __init__(self,
+                 query_dim,
+                 context_dim,
+                 mlp_ratio,
+                 vit_heads,
+                 f=4,
+                 heads=8,
+                 dim_head=64,
+                 dropout=0,
+                 depth=2,
+                 vit_blk=DiTBlock2, 
+                 no_flash_op=False,) -> None:
+        super().__init__(query_dim, context_dim, mlp_ratio, vit_heads, f,
+                         heads, dim_head, dropout, depth, vit_blk)
+
+        # corner_mat = torch.empty(8,3)
+        # counter = 0
+        # for i in range(-1,3,2):
+        #     for j in range(-1,3,2):
+        #         for k in range(-1,3,2):
+        #             corner_mat[counter] = torch.Tensor([i,j,k])
+        #             counter += 1
+
+        # self.corner_mat=corner_mat.contiguous().to(dist_util.dev()).reshape(1,1,8,3)
+
+        del self.read_ca, self.write_ca
+        del self.point_infinity_blocks
+
+        # ? why not saved to checkpoint
+        # self.latent_embedding = nn.Parameter(torch.randn(1, f, query_dim)).to(
+        #     dist_util.dev())
+
+        # ! not .cuda() here
+        self.latent_embedding = nn.Parameter(torch.randn(1, f, query_dim),
+                                             requires_grad=True)
+
+        self.transformer = SRT_TX(
+            context_dim,  # 12 * 64 = 768
+            depth=depth,
+            heads=context_dim // 64,  # vit-b default.
+            mlp_dim=4 * context_dim,  # 1536 by default
+            no_flash_op=no_flash_op,
+        )
+
+        # self.offset_act = lambda x: torch.tanh(x) * (self.scene_range[
+        #     1]) * 0.5  # regularize small offsets
+
+    def forward(self, gaussians_base, gaussian_base_pre_activate,
+                gaussian_base_feat, latent_from_vit, vae_latent, xyz_embed_fn,
+                offset_act):
+        # gaussians_base: xyz_base after activations and deform offset
+        # xyz_base: original features (before activations)
+
+        # ========= START read CA ========
+        # latent_from_vit = self.read_ca(latent_from_vit,
+        #                                gaussian_base_feat)  # z_i -> z_(i+1)
+
+        # for blk_idx, block in enumerate(self.point_infinity_blocks):
+        #     latent_from_vit = block(latent_from_vit,
+        #                             vae_latent)  # vae_latent: c
+
+        # ========= END read CA ========
+
+        # ! use point embedder, or other features?
+        # base_gaussian_xyz_embed = xyz_embed_fn(gaussians_base[..., :3])
+
+        # x = self.densify_and_split(gaussians_base, base_gaussian_xyz_embed)
+
+        # ! densify
+        B, N = gaussians_base.shape[:2]  # gaussians upsample factor
+        # n_init_points = self.get_xyz.shape[0]
+
+        pos, opacity, scaling, rotation = gaussians_base[
+            ..., 0:3], gaussians_base[..., 3:4], gaussians_base[
+                ..., 4:7], gaussians_base[..., 7:11]
+
+        # ! filter clone/densify based on scaling range
+        """
+
+        # split_mask = scaling.max(
+        #     dim=-1
+        # )[0] > self.scene_extent * self.percent_dense  # shape: B 4096
+
+        stds = scaling  #  B 13824 3
+
+        # TODO, in mat form. axis aligned creation.
+        samples = torch.zeros(B, N, 3, 3).to(stds.device) 
+
+        samples[..., 0,0] = stds[..., 0]
+        samples[..., 1,1] = stds[..., 1]
+        samples[..., 2,2] = stds[..., 2]
+
+        eye_mat = torch.cat([torch.eye(3), -torch.eye(3)], 0) # 6 * 3, to put gaussians along the axis
+        eye_mat = eye_mat.reshape(1,1,6,3).repeat(B, N, 1, 1).to(stds.device)
+        samples = (eye_mat @ samples).squeeze(-1) # B N 6 3
+
+        # ! create corner
+        samples_corner = stds.clone().unsqueeze(-2).repeat(1,1,8,1) # B N 8 3
+
+        # ! optimize with matmul, register to self
+        samples_corner = torch.mul(samples_corner,self.corner_mat)
+
+        samples = torch.cat([samples, samples_corner], -2)
+
+        rots = rearrange(build_rotation(rearrange(rotation, 'B N ... -> (B N) ...')), '(B N) ... -> B N ...', B=B, N=N)
+        rots = rots.unsqueeze(2).repeat_interleave(self.f, dim=2) # B f*N 3 3
+
+        new_xyz = (rots @ samples.unsqueeze(-1)).squeeze(-1) + pos.unsqueeze(2).repeat_interleave(self.f, dim=2) # B N 6 3
+        new_xyz = rearrange(new_xyz, 'b n f c -> b (n f) c')
+        
+        # ! new points to features
+        new_xyz_embed = xyz_embed_fn(new_xyz)
+        new_gaussian_embed = self.write_ca(
+            new_xyz_embed, latent_from_vit
+        )  # ! use z_(i+1), rather than gaussian_base_feat here
+
+        """
+
+        # ! [global_emb, local_emb, learnable_query_emb] self attention -> fetch last K tokens as the learned query -> add to base
+
+        # ! query from local point emb
+        global_local_query_emb = torch.cat(
+            [
+                # rearrange(latent_from_vit.unsqueeze(1).expand(-1,N,-1,-1), 'B N L C -> (B N) L C'), # 8, 768, 1024. expand() returns a new view.
+                rearrange(gaussian_base_feat,
+                          'B N C -> (B N) 1 C'),  # 8, 2304, 1024 -> 8*2304 1 C
+                self.latent_embedding.repeat(B * N, 1,
+                                             1)  # 1, 14, 1024 -> B*N 14 1024
+            ],
+            dim=1)  # OOM if prepend global feat
+        global_local_query_emb = self.transformer(
+            global_local_query_emb)  # torch.Size([18432, 15, 1024])
+        # st() # do self attention
+
+        # ! query from global shape emb
+        # new_gaussian_embed = self.write_ca(
+        #     global_local_query_emb,
+        #     rearrange(latent_from_vit.unsqueeze(1).expand(-1,N,-1,-1), 'B N L C -> (B N) L C'),
+        # )  # ! use z_(i+1), rather than gaussian_base_feat here
+
+        # ! predict gaussians residuals
+        gaussian_residual_pre_activate = self.gaussian_residual_pred(
+            global_local_query_emb[:, 1:, :])
+
+        gaussian_residual_pre_activate = rearrange(
+            gaussian_residual_pre_activate, '(B N) L C -> B N L C', B=B,
+            N=N)  # B 2304 14 C
+        # TODO here
+        # ? new_xyz from where
+        offsets = offset_act(gaussian_residual_pre_activate[..., 0:3])
+        new_xyz = offsets + pos.unsqueeze(2).repeat_interleave(
+            self.f, dim=2)  # B N F 3
+        new_xyz = rearrange(new_xyz, 'b n f c -> b (n f) c')
+
+        gaussian_base_pre_activate_repeat = gaussian_base_pre_activate.unsqueeze(
+            -2).expand(-1, -1, self.f, -1)  # avoid new memory allocation
+        gaussians = rearrange(gaussian_residual_pre_activate +
+                              gaussian_base_pre_activate_repeat,
+                              'B N F C -> B (N F) C',
+                              B=B,
+                              N=N)  # learn the residual in the feature space
+
+        # return gaussians, new_xyz, latent_from_vit, new_gaussian_embed  # return positions independently
+        # return gaussians, latent_from_vit, new_gaussian_embed  # return positions independently
+        return gaussians, new_xyz
+
+
+class GS_Adaptive_Read_Write_CA_adaptive_2dgs(
+        GS_Adaptive_Read_Write_CA_adaptive_f14_prepend):
+
+    def __init__(self,
+                 query_dim,
+                 context_dim,
+                 mlp_ratio,
+                 vit_heads,
+                 f=16,
+                 heads=8,
+                 dim_head=64,
+                 dropout=0,
+                 depth=2,
+                 vit_blk=DiTBlock2, 
+                 no_flash_op=False,
+                 cross_attention=False,) -> None:
+        super().__init__(query_dim, context_dim, mlp_ratio, vit_heads, f,
+                         heads, dim_head, dropout, depth, vit_blk, no_flash_op)
+
+        # del self.gaussian_residual_pred # will use base one
+
+        self.cross_attention = cross_attention
+        if cross_attention: # since much efficient than self attention, linear complexity
+            # del self.transformer
+            self.sr_ca = CrossAttention(query_dim, context_dim, # xformers fails large batch size: https://github.com/facebookresearch/xformers/issues/845
+                                                      heads, dim_head, dropout, 
+                                                      no_flash_op=no_flash_op)
+
+        # predict residual over base (features)
+        self.gaussian_residual_pred = PreNorm(  # add prenorm since using pre-norm TX as the sr module
+            query_dim, nn.Linear(query_dim, 13, bias=True))
+
+        # init as full zero, since predicting residual here
+        nn.init.constant_(self.gaussian_residual_pred.fn.weight, 0)
+        nn.init.constant_(self.gaussian_residual_pred.fn.bias, 0)
+
+    def forward(self,
+                latent_from_vit,
+                base_gaussians,
+                skip_weight,
+                offset_act,
+                gs_pred_fn,
+                gs_act_fn,
+                gaussian_base_pre_activate=None):
+        B, N, C = latent_from_vit.shape  # e.g., B 768 768
+
+        if not self.cross_attention:
+            # ! query from local point emb
+            global_local_query_emb = torch.cat(
+                [
+                    rearrange(latent_from_vit,
+                            'B N C -> (B N) 1 C'),  # 8, 2304, 1024 -> 8*2304 1 C
+                    self.latent_embedding.repeat(B * N, 1, 1).to(
+                        latent_from_vit)  # 1, 14, 1024 -> B*N 14 1024
+                ],
+                dim=1)  # OOM if prepend global feat
+
+            global_local_query_emb = self.transformer(
+                global_local_query_emb)  # torch.Size([18432, 15, 1024])
+
+            # ! add residuals to the base features
+            global_local_query_emb = rearrange(global_local_query_emb[:, 1:],
+                                            '(B N) L C -> B N L C',
+                                            B=B,
+                                            N=N)  # B N C f
+        else:
+
+            # st()
+            # for xformers debug
+            # global_local_query_emb = self.sr_ca( self.latent_embedding.repeat(B, 1, 1).to( latent_from_vit).contiguous(), latent_from_vit[:, 0:1, :],)
+            # st()
+
+            # self.sr_ca( self.latent_embedding.repeat(B * N, 1, 1).to(latent_from_vit)[:8000], rearrange(latent_from_vit, 'B N C -> (B N) 1 C')[:8000],).shape
+            global_local_query_emb = self.sr_ca( self.latent_embedding.repeat(B * N, 1, 1).to(latent_from_vit), rearrange(latent_from_vit, 'B N C -> (B N) 1 C'),)
+
+            global_local_query_emb = self.transformer(
+                global_local_query_emb)  # torch.Size([18432, 15, 1024])
+
+            # ! add residuals to the base features
+            global_local_query_emb = rearrange(global_local_query_emb,
+                                            '(B N) L C -> B N L C',
+                                            B=B,
+                                            N=N)  # B N C f
+
+        # * predict residual features
+        gaussian_residual_pre_activate = self.gaussian_residual_pred(
+            global_local_query_emb)
+
+        # ! directly add xyz offsets 
+        offsets = offset_act(gaussian_residual_pre_activate[..., :3])
+
+        gaussians_upsampled_pos = offsets + einops.repeat(
+            base_gaussians[..., :3], 'B N C -> B N F C',
+            F=self.f)  # ! reasonable init
+
+        # ! add residual features
+        gaussian_residual_pre_activate = gaussian_residual_pre_activate + einops.repeat(
+            gaussian_base_pre_activate, 'B N C -> B N F C', F=self.f)
+
+        gaussians_upsampled = gs_act_fn(pos=gaussians_upsampled_pos,
+                                        x=gaussian_residual_pre_activate)
+
+        gaussians_upsampled = rearrange(gaussians_upsampled,
+                                        'B N F C -> B (N F) C')
+
+        return gaussians_upsampled, (rearrange(
+            gaussian_residual_pre_activate, 'B N F C -> B (N F) C'
+        ), rearrange(
+            global_local_query_emb, 'B N F C -> B (N F) C'
+        ))
+
+
+class ViTTriplaneDecomposed(nn.Module):
+
+    def __init__(
+        self,
+        vit_decoder,
+        triplane_decoder: Triplane,
+        cls_token=False,
+        decoder_pred_size=-1,
+        unpatchify_out_chans=-1,
+        sr_ratio=2,
+    ) -> None:
+        super().__init__()
+        self.superresolution = None
+
+        self.decomposed_IN = False
+
+        self.decoder_pred_3d = None
+        self.transformer_3D_blk = None
+        self.logvar = None
+
+        self.cls_token = cls_token
+        self.vit_decoder = vit_decoder
+        self.triplane_decoder = triplane_decoder
+        # triplane_sr_ratio = self.triplane_decoder.triplane_size / self.vit_decoder.img_size
+        # self.decoder_pred = nn.Linear(self.vit_decoder.embed_dim,
+        #                               self.vit_decoder.patch_size**2 *
+        #                               self.triplane_decoder.out_chans,
+        #                               bias=True)  # decoder to pat
+
+        # self.patch_size = self.vit_decoder.patch_embed.patch_size
+        self.patch_size = 14  # TODO, hard coded here
+        if isinstance(self.patch_size, tuple):  # dino-v2
+            self.patch_size = self.patch_size[0]
+
+        # self.img_size = self.vit_decoder.patch_embed.img_size
+        self.img_size = None  # TODO, hard coded
+        if decoder_pred_size == -1:
+            decoder_pred_size = self.patch_size**2 * self.triplane_decoder.out_chans
+
+        if unpatchify_out_chans == -1:
+            self.unpatchify_out_chans = self.triplane_decoder.out_chans
+        else:
+            self.unpatchify_out_chans = unpatchify_out_chans
+
+        self.decoder_pred = nn.Linear(
+            self.vit_decoder.embed_dim,
+            decoder_pred_size,
+            #   self.patch_size**2 *
+            #   self.triplane_decoder.out_chans,
+            bias=True)  # decoder to pat
+        # st()
+
+    def triplane_decode(self, latent, c):
+        ret_dict = self.triplane_decoder(latent, c)  # triplane latent -> imgs
+        ret_dict.update({'latent': latent})
+        return ret_dict
+
+    def triplane_renderer(self, latent, coordinates, directions):
+
+        planes = latent.view(len(latent), 3,
+                             self.triplane_decoder.decoder_in_chans,
+                             latent.shape[-2],
+                             latent.shape[-1])  # BS 96 256 256
+
+        ret_dict = self.triplane_decoder.renderer.run_model(
+            planes, self.triplane_decoder.decoder, coordinates, directions,
+            self.triplane_decoder.rendering_kwargs)  # triplane latent -> imgs
+        # ret_dict.update({'latent': latent})
+        return ret_dict
+
+        # * increase encoded encoded latent dim to match decoder
+
+    def forward_vit_decoder(self, x, img_size=None):
+        # latent: (N, L, C) from DINO/CLIP ViT encoder
+
+        # * also dino ViT
+        # add positional encoding to each token
+        if img_size is None:
+            img_size = self.img_size
+
+        if self.cls_token:
+            x = x + self.vit_decoder.interpolate_pos_encoding(
+                x, img_size, img_size)[:, :]  # B, L, C
+        else:
+            x = x + self.vit_decoder.interpolate_pos_encoding(
+                x, img_size, img_size)[:, 1:]  # B, L, C
+
+        for blk in self.vit_decoder.blocks:
+            x = blk(x)
+        x = self.vit_decoder.norm(x)
+
+        return x
+
+    def unpatchify(self, x, p=None, unpatchify_out_chans=None):
+        """
+        x: (N, L, patch_size**2 * self.out_chans)
+        imgs: (N, self.out_chans, H, W)
+        """
+        # st()
+        if unpatchify_out_chans is None:
+            unpatchify_out_chans = self.unpatchify_out_chans
+        # p = self.vit_decoder.patch_size
+        if self.cls_token:  # TODO, how to better use cls token
+            x = x[:, 1:]
+
+        if p is None:  # assign upsample patch size
+            p = self.patch_size
+        h = w = int(x.shape[1]**.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, unpatchify_out_chans))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], unpatchify_out_chans, h * p,
+                                h * p))
+        return imgs
+
+    def forward(self, latent, c, img_size):
+        latent = self.forward_vit_decoder(latent, img_size)  # pred_vit_latent
+
+        if self.cls_token:
+            # latent, cls_token = latent[:, 1:], latent[:, :1]
+            cls_token = latent[:, :1]
+        else:
+            cls_token = None
+
+        # ViT decoder projection, from MAE
+        latent = self.decoder_pred(
+            latent)  # pred_vit_latent -> patch or original size
+        # st()
+        latent = self.unpatchify(
+            latent)  # spatial_vit_latent, B, C, H, W (B, 96, 256,256)
+
+        # TODO 2D convolutions -> Triplane
+        # * triplane rendering
+        # ret_dict = self.forward_triplane_decoder(latent,
+        #                                          c)  # triplane latent -> imgs
+        ret_dict = self.triplane_decoder(planes=latent, c=c)
+        ret_dict.update({'latent': latent, 'cls_token': cls_token})
+
+        return ret_dict
+
+
+# merged above class into a single class
+
+class vae_3d(nn.Module):
+    def __init__(
+            self,
+            vit_decoder: VisionTransformer,
+            triplane_decoder: Triplane_fg_bg_plane,
+            cls_token,
+            ldm_z_channels,
+            ldm_embed_dim,
+            plane_n=1,
+            vae_dit_token_size=16,
+            **kwargs) -> None:
+        super().__init__()
+
+        self.reparameterization_soft_clamp = True  # some instability in training VAE
+
+        # st()
+        self.plane_n = plane_n
+        self.cls_token = cls_token
+        self.vit_decoder = vit_decoder
+        self.triplane_decoder = triplane_decoder
+
+        self.patch_size = 14  # TODO, hard coded here
+        if isinstance(self.patch_size, tuple):  # dino-v2
+            self.patch_size = self.patch_size[0]
+
+        self.img_size = None  # TODO, hard coded
+
+        self.ldm_z_channels = ldm_z_channels
+        self.ldm_embed_dim = ldm_embed_dim
+        self.vae_p = 4  # resolution = 4 * 16
+        self.token_size = vae_dit_token_size  # use dino-v2 dim tradition here
+        self.vae_res = self.vae_p * self.token_size
+
+        self.superresolution = nn.ModuleDict({}) # put all the stuffs here
+        self.embed_dim = vit_decoder.embed_dim
+
+        # placeholder for compat issue
+        self.decoder_pred = None
+        self.decoder_pred_3d = None
+        self.transformer_3D_blk = None
+        self.logvar = None
+        self.register_buffer('w_avg', torch.zeros([512]))
+
+
+
+    def init_weights(self):
+        # ! init (learnable) PE for DiT
+        self.vit_decoder.pos_embed = nn.Parameter(
+            torch.zeros(1, self.vit_decoder.embed_dim,
+                        self.vit_decoder.embed_dim),
+            requires_grad=True)  # token_size = embed_size by default.
+        trunc_normal_(self.vit_decoder.pos_embed, std=.02)
+
+
+# the base class
+class pcd_structured_latent_space_vae_decoder(vae_3d):
+    def __init__(
+            self,
+            vit_decoder: VisionTransformer,
+            triplane_decoder: Triplane_fg_bg_plane,
+            cls_token,
+            **kwargs) -> None:
+        super().__init__(vit_decoder, triplane_decoder, cls_token, **kwargs)
+        # from splatting_dit_v4_PI_V1_trilatent_sphere
+        self.D_roll_out_input = False
+
+        # ! renderer
+        self.gs = triplane_decoder  # compat
+
+        self.rendering_kwargs = self.gs.rendering_kwargs
+        self.scene_range = [
+            self.rendering_kwargs['sampler_bbox_min'],
+            self.rendering_kwargs['sampler_bbox_max']
+        ]
+
+        # hyper parameters
+        self.skip_weight = torch.tensor(0.1).to(dist_util.dev())
+
+        self.offset_act = lambda x: torch.tanh(x) * (self.scene_range[
+            1]) * 0.5  # regularize small offsets
+
+        self.vit_decoder.pos_embed = nn.Parameter(
+            torch.zeros(1,
+                        self.plane_n * (self.token_size**2 + self.cls_token),
+                        vit_decoder.embed_dim))
+        self.init_weights()  # re-init weights after re-writing token_size
+
+        self.output_size = {
+            'gaussians_base': 128,
+        }
+
+        # activations
+        self.rot_act = lambda x: F.normalize(x, dim=-1)  # as fixed in lgm
+        self.scene_extent = self.rendering_kwargs['sampler_bbox_max'] * 0.01
+        scaling_factor = (self.scene_extent /
+                          F.softplus(torch.tensor(0.0))).to(dist_util.dev())
+        self.scale_act = lambda x: F.softplus(
+            x
+        ) * scaling_factor  # make sure F.softplus(0) is the average scale size
+        self.rgb_act = lambda x: 0.5 * torch.tanh(
+            x) + 0.5  # NOTE: may use sigmoid if train again
+        self.pos_act = lambda x: x.clamp(-0.45, 0.45)
+        self.opacity_act = lambda x: torch.sigmoid(x)
+
+
+        self.superresolution.update(
+            dict(
+                conv_sr=surfel_prediction(query_dim=vit_decoder.embed_dim),
+                quant_conv=Mlp(in_features=2 * self.ldm_z_channels,
+                               out_features=2 * self.ldm_embed_dim,
+                               act_layer=approx_gelu,
+                               drop=0),
+                post_quant_conv=Mlp(in_features=self.ldm_z_channels,
+                                    out_features=vit_decoder.embed_dim,
+                                    act_layer=approx_gelu,
+                                    drop=0),
+                ldm_upsample=nn.Identity(),
+                xyz_pos_embed=nn.Identity(),
+            ))
+
+        # for gs prediction
+        self.superresolution.update(  # f=14 here
+            dict(
+                ada_CA_f4_1=GS_Adaptive_Read_Write_CA_adaptive_2dgs(
+                    self.embed_dim,
+                    vit_decoder.embed_dim,
+                    vit_heads=vit_decoder.num_heads,
+                    mlp_ratio=vit_decoder.mlp_ratio,
+                    # depth=vit_decoder.depth // 6,
+                    depth=vit_decoder.depth // 6 if vit_decoder.depth==12 else 2,
+                    # f=16,  # 
+                    f=8,  # 
+                    heads=8),  # write
+            ))
+
+
+    def vae_reparameterization(self, latent, sample_posterior):
+        # latent: B 24 32 32
+
+        # assert self.vae_p > 1
+
+        # ! do VAE here
+        posterior = self.vae_encode(latent)  # B self.ldm_z_channels 3 L
+
+        assert sample_posterior
+        if sample_posterior:
+            # torch.manual_seed(0)
+            # np.random.seed(0)
+            kl_latent = posterior.sample()
+        else:
+            kl_latent = posterior.mode()  # B C 3 L
+
+        ret_dict = dict(
+            latent_normalized=rearrange(kl_latent, 'B C L -> B L C'),
+            posterior=posterior,
+            query_pcd_xyz=latent['query_pcd_xyz'],
+        )
+
+        return ret_dict
+
+    # from pcd_structured_latent_space_lion_learnoffset_surfel_sr_noptVAE.vae_encode
+    def vae_encode(self, h):
+        # * smooth convolution before triplane
+        # B, L, C = h.shape #
+        h, query_pcd_xyz = h['h'], h['query_pcd_xyz']
+        moments = self.superresolution['quant_conv'](
+            h)  # Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), groups=3)
+
+        moments = rearrange(moments,
+                            'B L C -> B C L')  # for sd vae code compat
+
+        posterior = DiagonalGaussianDistribution(
+            moments, soft_clamp=self.reparameterization_soft_clamp)
+
+        return posterior
+
+    # from pcd_structured_latent_space_lion_learnoffset_surfel_novaePT._get_base_gaussians
+    def _get_base_gaussians(self, ret_after_decoder, c=None):
+        x = ret_after_decoder['gaussian_base_pre_activate']
+        B, N, C = x.shape  # B C D H W, 14-dim voxel features
+        assert C == 13  # 2dgs
+
+        offsets = self.offset_act(x[..., 0:3])  # ! model prediction
+        # st()
+        # vae_sampled_xyz = ret_after_decoder['latent_normalized'][..., :3] # B L C
+
+        vae_sampled_xyz = ret_after_decoder['query_pcd_xyz'].to(
+            x.dtype)  # ! directly use fps pcd as "anchor points"
+
+        pos = offsets * self.skip_weight + vae_sampled_xyz  # ! reasonable init
+
+        opacity = self.opacity_act(x[..., 3:4])
+
+        scale = self.scale_act(x[..., 4:6])
+
+        rotation = self.rot_act(x[..., 6:10])
+        rgbs = self.rgb_act(x[..., 10:])
+
+        gaussians = torch.cat([pos, opacity, scale, rotation, rgbs],
+                              dim=-1)  # [B, N, 14]
+
+        return gaussians
+
+    # from pcd_structured_latent_space
+    def vit_decode_backbone(self, latent, img_size):
+        # assert x.ndim == 3  # N L C
+        if isinstance(latent, dict):
+            latent = latent['latent_normalized']  # B, C*3, H, W
+
+        latent = self.superresolution['post_quant_conv'](
+            latent)  # to later dit embed dim
+
+        # ! directly feed to vit_decoder
+        return {
+            'latent': latent,
+            'latent_from_vit': self.forward_vit_decoder(latent, img_size)
+        }  # pred_vit_latent
+
+    # from pcd_structured_latent_space_lion_learnoffset_surfel_sr
+    def _gaussian_pred_activations(self, pos, x):
+        # if pos is None:
+        opacity = self.opacity_act(x[..., 3:4])
+        scale = self.scale_act(x[..., 4:6])
+        rotation = self.rot_act(x[..., 6:10])
+        rgbs = self.rgb_act(x[..., 10:])
+
+        gaussians = torch.cat([pos, opacity, scale, rotation, rgbs],
+                              dim=-1)  # [B, N, 14]
+
+        return gaussians.float()
+
+    # from pcd_structured_latent_space_lion_learnoffset_surfel_sr
+    def vis_gaussian(self, gaussians, file_name_base):
+        # gaussians = ret_after_decoder['gaussians']
+        # gaussians = ret_after_decoder['latent_after_vit']['gaussians_base']
+        B = gaussians.shape[0]
+        pos, opacity, scale, rotation, rgbs = gaussians[..., 0:3], gaussians[
+            ..., 3:4], gaussians[..., 4:6], gaussians[...,
+                                                      6:10], gaussians[...,
+                                                                       10:13]
+        file_path = Path(logger.get_dir())
+
+        for b in range(B):
+            file_name = f'{file_name_base}-{b}'
+
+            np.save(file_path / f'{file_name}_opacity.npy',
+                    opacity[b].float().detach().cpu().numpy())
+            np.save(file_path / f'{file_name}_scale.npy',
+                    scale[b].float().detach().cpu().numpy())
+            np.save(file_path / f'{file_name}_rotation.npy',
+                    rotation[b].float().detach().cpu().numpy())
+
+            pcu.save_mesh_vc(str(file_path / f'{file_name}.ply'),
+                             pos[b].float().detach().cpu().numpy(),
+                             rgbs[b].float().detach().cpu().numpy())
+
+    def vit_decode_postprocess(self, latent_from_vit, ret_dict: dict, return_upsampled_residual=False):
+        # from ViT_decode_backbone()
+
+        # latent_from_vit = latent_from_vit['latent_from_vit']
+        # vae_sampled_xyz = ret_dict['query_pcd_xyz'].to(latent_from_vit.dtype) # ! directly use fps pcd as "anchor points"
+        gaussian_base_pre_activate = self.superresolution['conv_sr'](
+            latent_from_vit['latent_from_vit'])  # B 14 H W
+
+        gaussians_base = self._get_base_gaussians(
+            {
+                # 'latent_from_vit': latent_from_vit,  # latent (vae latent), latent_from_vit (dit)
+                # 'ret_dict': ret_dict,
+                **ret_dict,
+                'gaussian_base_pre_activate':
+                gaussian_base_pre_activate,
+            }, )
+
+        gaussians_upsampled, (gaussian_upsampled_residual_pre_activate, upsampled_global_local_query_emb) = self.superresolution['ada_CA_f4_1'](
+            latent_from_vit['latent_from_vit'],
+            gaussians_base,
+            skip_weight=self.skip_weight,
+            gs_pred_fn=self.superresolution['conv_sr'],
+            gs_act_fn=self._gaussian_pred_activations,
+            offset_act=self.offset_act,
+            gaussian_base_pre_activate=gaussian_base_pre_activate)
+
+        ret_dict.update({
+            'gaussians_upsampled': gaussians_upsampled,
+            'gaussians_base': gaussians_base
+        })  #
+
+        if return_upsampled_residual:
+            return ret_dict, (gaussian_upsampled_residual_pre_activate, upsampled_global_local_query_emb)
+        else:
+            return ret_dict
+
+    def vit_decode(self, latent, img_size, sample_posterior=True, c=None):
+
+        ret_dict = self.vae_reparameterization(latent, sample_posterior)
+
+        latent = self.vit_decode_backbone(ret_dict, img_size)
+        ret_after_decoder = self.vit_decode_postprocess(latent, ret_dict)
+
+        return self.forward_gaussians(ret_after_decoder, c=c)
+
+    # from pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr.forward_gaussians
+    def forward_gaussians(self, ret_after_decoder, c=None):
+
+        # ! currently, only using upsampled gaussians for training.
+
+        # if True:
+        if False:
+            ret_after_decoder['gaussians'] = torch.cat([
+                ret_after_decoder['gaussians_base'],
+                ret_after_decoder['gaussians_upsampled'],
+            ],
+                                                       dim=1)
+        else:  # only adopt SR
+            # ! random drop out requires
+            ret_after_decoder['gaussians'] = ret_after_decoder[
+                'gaussians_upsampled']
+            # ret_after_decoder['gaussians'] = ret_after_decoder['gaussians_base']
+            pass  # directly use base. vis first.
+
+        ret_after_decoder.update({
+            'gaussians': ret_after_decoder['gaussians'],
+            'pos': ret_after_decoder['gaussians'][..., :3],
+            'gaussians_base_opa': ret_after_decoder['gaussians_base'][..., 3:4]
+        })
+
+        # st()
+        # self.vis_gaussian(ret_after_decoder['gaussians'], 'sr-8')
+        # self.vis_gaussian(ret_after_decoder['gaussians_base'], 'sr-8-base')
+        # pcu.save_mesh_v(f'{Path(logger.get_dir())}/anchor-fps-8.ply',ret_after_decoder['query_pcd_xyz'][0].float().detach().cpu().numpy())
+        # st()
+
+        # ! render at L:8414 triplane_decode()
+        return ret_after_decoder
+
+    def forward_vit_decoder(self, x, img_size=None):
+        return self.vit_decoder(x)
+
+    # from pcd_structured_latent_space_lion_learnoffset_surfel_novaePT_sr_cascade.triplane_decode
+    def triplane_decode(self,
+                        ret_after_gaussian_forward,
+                        c,
+                        bg_color=None, 
+                        render_all_scale=False,
+                        **kwargs):
+        # ! render multi-res img with different gaussians
+
+        def render_gs(gaussians, c_data, output_size):
+
+            results = self.gs.render(
+                gaussians,  #  type: ignore
+                c_data['cam_view'],
+                c_data['cam_view_proj'],
+                c_data['cam_pos'],
+                tanfov=c_data['tanfov'],
+                bg_color=bg_color,
+                output_size=output_size,
+            )
+
+            results['image_raw'] = results[
+                'image'] * 2 - 1  # [0,1] -> [-1,1], match tradition
+            results['image_depth'] = results['depth']
+            results['image_mask'] = results['alpha']
+
+            return results
+
+        cascade_splatting_results = {}
+
+        # for gaussians_key in ('gaussians_base', 'gaussians_upsampled'):
+        all_keys_to_render = list(self.output_size.keys())
+
+    
+        if self.rand_base_render and not render_all_scale:
+            keys_to_render = [random.choice(all_keys_to_render[:-1])] + [all_keys_to_render[-1]]
+        else:
+            keys_to_render = all_keys_to_render
+
+        for gaussians_key in keys_to_render:
+            cascade_splatting_results[gaussians_key] = render_gs(ret_after_gaussian_forward[gaussians_key], c, self.output_size[gaussians_key])
+
+        return cascade_splatting_results
+
+
+class pcd_structured_latent_space_vae_decoder_cascaded(pcd_structured_latent_space_vae_decoder):
+    # for 2dgs
+
+    def __init__(
+            self,
+            vit_decoder: VisionTransformer,
+            triplane_decoder: Triplane_fg_bg_plane,
+            cls_token,
+            **kwargs) -> None:
+        super().__init__(vit_decoder, triplane_decoder, cls_token, **kwargs)
+
+        self.output_size.update(
+            {
+                'gaussians_upsampled': 256,
+                'gaussians_upsampled_2': 384,
+                'gaussians_upsampled_3': 512,
+            }
+        ) 
+                        
+        self.rand_base_render = True
+
+        # further x8 up-sampling.
+        self.superresolution.update(  
+            dict(
+                ada_CA_f4_2=GS_Adaptive_Read_Write_CA_adaptive_2dgs(
+                    self.embed_dim,
+                    vit_decoder.embed_dim,
+                    vit_heads=vit_decoder.num_heads,
+                    mlp_ratio=vit_decoder.mlp_ratio,
+                    # depth=vit_decoder.depth // 6,
+                    depth=1,
+                    f=4,  # 
+                    heads=8, 
+                    no_flash_op=True,  # fails when bs>1
+                    cross_attention=False),  # write
+                ada_CA_f4_3=GS_Adaptive_Read_Write_CA_adaptive_2dgs(
+                    self.embed_dim,
+                    vit_decoder.embed_dim,
+                    vit_heads=vit_decoder.num_heads,
+                    mlp_ratio=vit_decoder.mlp_ratio,
+                    # depth=vit_decoder.depth // 6,
+                    depth=1,
+                    f=3,  # 
+                    heads=8, 
+                    no_flash_op=True, 
+                    cross_attention=False),  # write
+            ),
+        )
+
+
+
+    def vit_decode_postprocess(self, latent_from_vit, ret_dict: dict):
+
+        # further x8 using upper class
+        # TODO, merge this into ln3diff open sourced code.
+        ret_dict, (gaussian_upsampled_residual_pre_activate, upsampled_global_local_query_emb) = super().vit_decode_postprocess(latent_from_vit, ret_dict, return_upsampled_residual=True)
+
+        gaussians_upsampled_2, (gaussian_upsampled_residual_pre_activate_2, upsampled_global_local_query_emb_2) = self.superresolution['ada_CA_f4_2'](
+            upsampled_global_local_query_emb,
+            ret_dict['gaussians_upsampled'],
+            skip_weight=self.skip_weight,
+            gs_pred_fn=self.superresolution['conv_sr'],
+            gs_act_fn=self._gaussian_pred_activations,
+            offset_act=self.offset_act,
+            gaussian_base_pre_activate=gaussian_upsampled_residual_pre_activate)
+
+
+        gaussians_upsampled_3, _ = self.superresolution['ada_CA_f4_3'](
+            upsampled_global_local_query_emb_2,
+            gaussians_upsampled_2,
+            skip_weight=self.skip_weight,
+            gs_pred_fn=self.superresolution['conv_sr'],
+            gs_act_fn=self._gaussian_pred_activations,
+            offset_act=self.offset_act,
+            gaussian_base_pre_activate=gaussian_upsampled_residual_pre_activate_2)
+
+
+        ret_dict.update({
+            'gaussians_upsampled_2': gaussians_upsampled_2,
+            'gaussians_upsampled_3': gaussians_upsampled_3,
+        }) 
+
+        return ret_dict
+
diff --git a/vit/vit_with_mask.py b/vit/vit_with_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..56dc76bcdbf0dd9c2cde73e20543d81a86c201bf
--- /dev/null
+++ b/vit/vit_with_mask.py
@@ -0,0 +1,153 @@
+# https://gist.githubusercontent.com/lucidrains/5193d38d1d889681dd42feb847f1f6da/raw/1408da9b6a4170399c082358d1b7ca56428b9d8c/vit_with_mask.py
+
+from pdb import set_trace as st
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# classes
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, dim),
+            nn.Dropout(dropout)
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        project_out = not (heads == 1 and dim_head == dim)
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        ) if project_out else nn.Identity()
+
+    def forward(self, x, mask = None):
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        if mask is not None:
+            mask = rearrange(mask, 'b ... -> b (...)')
+            mask = F.pad(mask, (x.shape[-2] - mask.shape[-1], 0), value = True)
+            dots = dots.masked_fill(~mask, -torch.finfo(dots.dtype).max)
+
+        attn = self.attend(dots)
+        attn = self.dropout(attn)
+
+        out = torch.matmul(attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class Transformer(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
+            ]))
+    def forward(self, x, mask = None):
+        for attn, ff in self.layers:
+            x = attn(x, mask = mask) + x
+            x = ff(x) + x
+        return x
+
+class ViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
+
+        num_patches = (image_height // patch_height) * (image_width // patch_width)
+        patch_dim = channels * patch_height * patch_width
+        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.Linear(patch_dim, dim),
+        )
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        st()
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        self.pool = pool
+        self.to_latent = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img, mask = None):
+        x = self.to_patch_embedding(img)
+        b, n, _ = x.shape
+
+        cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding[:, :(n + 1)]
+        x = self.dropout(x)
+
+        x = self.transformer(x, mask = mask)
+
+        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
+
+        x = self.to_latent(x)
+        return self.mlp_head(x)
+
+if __name__ == '__main__':
+    x = torch.randn(1, 3, 256, 256)
+    mask = torch.ones(1, 16, 16).bool()
+
+    vit = ViT(
+        dim = 512,
+        depth = 6,
+        heads = 8,
+        mlp_dim = 1024,
+        image_size = 256,
+        patch_size = 16,
+        num_classes = 10
+    )
+
+    out = vit(x, mask = mask)