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| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # 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. | |
| # File author: Zhenyu Li | |
| import gc | |
| from ControlNet.share import * | |
| import einops | |
| import torch | |
| import random | |
| import ControlNet.config as config | |
| from pytorch_lightning import seed_everything | |
| from ControlNet.cldm.model import create_model, load_state_dict | |
| from ControlNet.cldm.ddim_hacked import DDIMSampler | |
| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from zoedepth.utils.arg_utils import parse_unknown | |
| import argparse | |
| from zoedepth.models.builder import build_model | |
| from zoedepth.utils.config import get_config_user | |
| import gradio as gr | |
| from ui_prediction import predict_depth | |
| import torch.nn.functional as F | |
| from huggingface_hub import hf_hub_download | |
| DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| def depth_load_state_dict(model, state_dict): | |
| """Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict. | |
| DataParallel prefixes state_dict keys with 'module.' when saving. | |
| If the model is not a DataParallel model but the state_dict is, then prefixes are removed. | |
| If the model is a DataParallel model but the state_dict is not, then prefixes are added. | |
| """ | |
| state_dict = state_dict.get('model', state_dict) | |
| # if model is a DataParallel model, then state_dict keys are prefixed with 'module.' | |
| do_prefix = isinstance( | |
| model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel)) | |
| state = {} | |
| for k, v in state_dict.items(): | |
| if k.startswith('module.') and not do_prefix: | |
| k = k[7:] | |
| if not k.startswith('module.') and do_prefix: | |
| k = 'module.' + k | |
| state[k] = v | |
| model.load_state_dict(state, strict=True) | |
| print("Loaded successfully") | |
| return model | |
| def load_wts(model, checkpoint_path): | |
| ckpt = torch.load(checkpoint_path, map_location='cpu') | |
| return depth_load_state_dict(model, ckpt) | |
| def load_ckpt(model, checkpoint): | |
| model = load_wts(model, checkpoint) | |
| print("Loaded weights from {0}".format(checkpoint)) | |
| return model | |
| pf_ckp = hf_hub_download(repo_id="zhyever/PatchFusion", filename="patchfusion_u4k.pt") | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--ckp_path", type=str, default=pf_ckp) | |
| parser.add_argument("-m", "--model", type=str, default="zoedepth_custom") | |
| parser.add_argument("--model_cfg_path", type=str, default="./zoedepth/models/zoedepth_custom/configs/config_zoedepth_patchfusion.json") | |
| args, unknown_args = parser.parse_known_args() | |
| overwrite_kwargs = parse_unknown(unknown_args) | |
| overwrite_kwargs['model_cfg_path'] = args.model_cfg_path | |
| overwrite_kwargs["model"] = args.model | |
| config_depth = get_config_user(args.model, **overwrite_kwargs) | |
| config_depth["pretrained_resource"] = '' | |
| depth_model = build_model(config_depth) | |
| depth_model = load_ckpt(depth_model, args.ckp_path) | |
| depth_model.eval() | |
| controlnet_ckp = hf_hub_download(repo_id="zhyever/PatchFusion", filename="control_sd15_depth.pth") | |
| model = create_model('./ControlNet/models/cldm_v15.yaml') | |
| model.load_state_dict(load_state_dict(controlnet_ckp, location=DEVICE), strict=False) | |
| model = model.to(DEVICE) | |
| ddim_sampler = DDIMSampler(model) | |
| # controlnet | |
| title = "# PatchFusion" | |
| description = """Official demo for **PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation**. | |
| PatchFusion is a deep learning model for high-resolution metric depth estimation from a single image. | |
| Please refer to our [paper](???) or [github](???) for more details.""" | |
| def rescale(A, lbound=-1, ubound=1): | |
| """ | |
| Rescale an array to [lbound, ubound]. | |
| Parameters: | |
| - A: Input data as numpy array | |
| - lbound: Lower bound of the scale, default is 0. | |
| - ubound: Upper bound of the scale, default is 1. | |
| Returns: | |
| - Rescaled array | |
| """ | |
| A_min = np.min(A) | |
| A_max = np.max(A) | |
| return (ubound - lbound) * (A - A_min) / (A_max - A_min) + lbound | |
| def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, mode, patch_number, resolution, patch_size): | |
| with torch.no_grad(): | |
| w, h = input_image.size | |
| depth_model.to(DEVICE) | |
| detected_map = predict_depth(depth_model, input_image, mode, patch_number, resolution, patch_size, device=DEVICE) | |
| depth_model.cpu() # free some mem | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| detected_map = F.interpolate(torch.from_numpy(detected_map).unsqueeze(dim=0).unsqueeze(dim=0), (image_resolution, image_resolution), mode='bicubic', align_corners=True).squeeze().numpy() | |
| H, W = detected_map.shape | |
| detected_map_temp = ((1 - detected_map / np.max(detected_map)) * 255) | |
| detected_map = detected_map_temp.astype("uint8") | |
| detected_map_temp = detected_map_temp[:, :, None] | |
| detected_map_temp = np.concatenate([detected_map_temp, detected_map_temp, detected_map_temp], axis=2) | |
| detected_map = detected_map[:, :, None] | |
| detected_map = np.concatenate([detected_map, detected_map, detected_map], axis=2) | |
| control = torch.from_numpy(detected_map.copy()).float().to(DEVICE) / 255.0 | |
| control = torch.stack([control for _ in range(num_samples)], dim=0) | |
| control = einops.rearrange(control, 'b h w c -> b c h w').clone() | |
| if seed == -1: | |
| seed = random.randint(0, 65535) | |
| seed_everything(seed) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} | |
| un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} | |
| shape = (4, H // 8, W // 8) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=True) | |
| model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 | |
| samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, | |
| shape, cond, verbose=False, eta=eta, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=un_cond) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| x_samples = model.decode_first_stage(samples) | |
| x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255) | |
| results = [x_samples[i] for i in range(num_samples)] | |
| return_list = [detected_map_temp] + results | |
| update_return_list = [] | |
| for r in return_list: | |
| t_r = torch.from_numpy(r).unsqueeze(dim=0).permute(0, 3, 1, 2) | |
| t_r = F.interpolate(t_r, (h, w), mode='bicubic', align_corners=True).squeeze().permute(1, 2, 0).numpy().astype(np.uint8) | |
| update_return_list.append(t_r) | |
| return update_return_list | |
| title = "# PatchFusion" | |
| description = """Official demo for **PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation**. | |
| PatchFusion is a deep learning model for high-resolution metric depth estimation from a single image. | |
| Please refer to our [project webpage](https://zhyever.github.io/patchfusion), [paper](https://arxiv.org/abs/2312.02284) or [github](https://github.com/zhyever/PatchFusion) for more details. | |
| # Advanced tips | |
| I know people don't like reading introductions, so you could run this demo without any extra modifications. | |
| But for people want to do some crazy things, I recommand to read the following texts to better under stand how this demo work. | |
| The overall pipeline: image --> (PatchFusion) --> depth --> (controlnet) --> generated image. | |
| As for the PatchFusion, it works on default 4k (2160x3840) resolution. All input images will be resized to 4k before passing through PatchFusion as default. It means if you have a higher resolution image, you can increase the resolution in the advanced option. | |
| For ControlNet, it works on default 896x896 resolution. Again, all input images will be resized to 896x896 before passing through ControlNet as default. You might be not happy because the 4K->896x896 downsampling, but limited by the GPU resource, this demo could only achieve this. | |
| We provide some tips might be helpful: (1) Try our experimental demo (see our project website) running on a local 80G gpu. But of course, it would be expired soon (in two days maybe); (2) Clone our code repo, and look for a gpu with more than 24G memory; (3) Clone our code repo, run the depth estimation (there are another demos for depth estimation and image-to-3D), and search for another guided high-resolution image generation strategy; (4) Some kind people give this space a stronger gpu support. | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| gr.Markdown("## Control Stable Diffusion with Depth Maps") | |
| with gr.Row(): | |
| with gr.Column(): | |
| # input_image = gr.Image(source='upload', type="pil") | |
| input_image = gr.Image(label="Input Image", type='pil') | |
| prompt = gr.Textbox(label="Prompt (input your description)", value='An evening scene with the Eiffel Tower, the bridge under the glow of street lamps and a twilight sky') | |
| run_button = gr.Button("Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| # mode = gr.Radio(["P49", "R"], label="Tiling mode", info="We recommand using P49 for fast evaluation and R with 1024 patches for best visualization results, respectively", elem_id='mode', value='R'), | |
| mode = gr.Radio(["P49", "R"], label="Tiling mode", info="We recommand using P49 for fast evaluation and R with 1024 patches for best visualization results, respectively", elem_id='mode', value='P49'), | |
| patch_number = gr.Slider(1, 1024, label="Please decide the number of random patches (Only useful in mode=R)", step=1, value=256) | |
| resolution = gr.Textbox(label="(PatchFusion) Proccessing resolution (Default 4K. Use 'x' to split height and width.)", elem_id='mode', value='2160x3840') | |
| patch_size = gr.Textbox(label="(PatchFusion) Patch size (Default 1/4 of image resolution. Use 'x' to split height and width.)", elem_id='mode', value='540x960') | |
| num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) | |
| image_resolution = gr.Slider(label="ControlNet image resolution (higher resolution will lead to OOM)", minimum=256, maximum=1024, value=896, step=64) | |
| strength = gr.Slider(label="Control strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) | |
| guess_mode = gr.Checkbox(label='Guess Mode', value=False) | |
| # detect_resolution = gr.Slider(label="Depth Resolution", minimum=128, maximum=1024, value=384, step=1) | |
| ddim_steps = gr.Slider(label="steps", minimum=1, maximum=100, value=20, step=1) | |
| scale = gr.Slider(label="guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) | |
| seed = gr.Slider(label="seed", minimum=-1, maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| a_prompt = gr.Textbox(label="Added prompt", value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox(label="Negative prompt", value='worst quality, low quality, lose details') | |
| with gr.Column(): | |
| # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery") | |
| ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, mode[0], patch_number, resolution, patch_size] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| examples = gr.Examples(examples=["examples/example_2.jpeg", "examples/example_4.jpeg", "examples/example_5.jpeg"], inputs=[input_image]) | |
| if __name__ == '__main__': | |
| demo.queue().launch(share=True) |