changes

app.py

@@ -104,7 +104,7 @@ device = torch.device('cuda')
 print('Loading diffusion model ...')
 pipeline = DiffusionPipeline.from_pretrained(
     "sudo-ai/zero123plus-v1.2", 
-    custom_pipeline="./zero123plus",
+    custom_pipeline="zero123plus",
     torch_dtype=torch.float16,
 )
 pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(

zero123plus/pipeline.py

@@ -0,0 +1,406 @@
+from typing import Any, Dict, Optional
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.schedulers import KarrasDiffusionSchedulers
+
+import numpy
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+import torch.distributed
+import transformers
+from collections import OrderedDict
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    EulerAncestralDiscreteScheduler,
+    UNet2DConditionModel,
+    ImagePipelineOutput
+)
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import Attention, AttnProcessor, XFormersAttnProcessor, AttnProcessor2_0
+from diffusers.utils.import_utils import is_xformers_available
+
+
+def to_rgb_image(maybe_rgba: Image.Image):
+    if maybe_rgba.mode == 'RGB':
+        return maybe_rgba
+    elif maybe_rgba.mode == 'RGBA':
+        rgba = maybe_rgba
+        img = numpy.random.randint(255, 256, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8)
+        img = Image.fromarray(img, 'RGB')
+        img.paste(rgba, mask=rgba.getchannel('A'))
+        return img
+    else:
+        raise ValueError("Unsupported image type.", maybe_rgba.mode)
+
+
+class ReferenceOnlyAttnProc(torch.nn.Module):
+    def __init__(
+        self,
+        chained_proc,
+        enabled=False,
+        name=None
+    ) -> None:
+        super().__init__()
+        self.enabled = enabled
+        self.chained_proc = chained_proc
+        self.name = name
+
+    def __call__(
+        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None,
+        mode="w", ref_dict: dict = None, is_cfg_guidance = False
+    ) -> Any:
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        if self.enabled and is_cfg_guidance:
+            res0 = self.chained_proc(attn, hidden_states[:1], encoder_hidden_states[:1], attention_mask)
+            hidden_states = hidden_states[1:]
+            encoder_hidden_states = encoder_hidden_states[1:]
+        if self.enabled:
+            if mode == 'w':
+                ref_dict[self.name] = encoder_hidden_states
+            elif mode == 'r':
+                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict.pop(self.name)], dim=1)
+            elif mode == 'm':
+                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict[self.name]], dim=1)
+            else:
+                assert False, mode
+        res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask)
+        if self.enabled and is_cfg_guidance:
+            res = torch.cat([res0, res])
+        return res
+
+
+class RefOnlyNoisedUNet(torch.nn.Module):
+    def __init__(self, unet: UNet2DConditionModel, train_sched: DDPMScheduler, val_sched: EulerAncestralDiscreteScheduler) -> None:
+        super().__init__()
+        self.unet = unet
+        self.train_sched = train_sched
+        self.val_sched = val_sched
+
+        unet_lora_attn_procs = dict()
+        for name, _ in unet.attn_processors.items():
+            if torch.__version__ >= '2.0':
+                default_attn_proc = AttnProcessor2_0()
+            elif is_xformers_available():
+                default_attn_proc = XFormersAttnProcessor()
+            else:
+                default_attn_proc = AttnProcessor()
+            unet_lora_attn_procs[name] = ReferenceOnlyAttnProc(
+                default_attn_proc, enabled=name.endswith("attn1.processor"), name=name
+            )
+        unet.set_attn_processor(unet_lora_attn_procs)
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+
+    def forward_cond(self, noisy_cond_lat, timestep, encoder_hidden_states, class_labels, ref_dict, is_cfg_guidance, **kwargs):
+        if is_cfg_guidance:
+            encoder_hidden_states = encoder_hidden_states[1:]
+            class_labels = class_labels[1:]
+        self.unet(
+            noisy_cond_lat, timestep,
+            encoder_hidden_states=encoder_hidden_states,
+            class_labels=class_labels,
+            cross_attention_kwargs=dict(mode="w", ref_dict=ref_dict),
+            **kwargs
+        )
+
+    def forward(
+        self, sample, timestep, encoder_hidden_states, class_labels=None,
+        *args, cross_attention_kwargs,
+        down_block_res_samples=None, mid_block_res_sample=None,
+        **kwargs
+    ):
+        cond_lat = cross_attention_kwargs['cond_lat']
+        is_cfg_guidance = cross_attention_kwargs.get('is_cfg_guidance', False)
+        noise = torch.randn_like(cond_lat)
+        if self.training:
+            noisy_cond_lat = self.train_sched.add_noise(cond_lat, noise, timestep)
+            noisy_cond_lat = self.train_sched.scale_model_input(noisy_cond_lat, timestep)
+        else:
+            noisy_cond_lat = self.val_sched.add_noise(cond_lat, noise, timestep.reshape(-1))
+            noisy_cond_lat = self.val_sched.scale_model_input(noisy_cond_lat, timestep.reshape(-1))
+        ref_dict = {}
+        self.forward_cond(
+            noisy_cond_lat, timestep,
+            encoder_hidden_states, class_labels,
+            ref_dict, is_cfg_guidance, **kwargs
+        )
+        weight_dtype = self.unet.dtype
+        return self.unet(
+            sample, timestep,
+            encoder_hidden_states, *args,
+            class_labels=class_labels,
+            cross_attention_kwargs=dict(mode="r", ref_dict=ref_dict, is_cfg_guidance=is_cfg_guidance),
+            down_block_additional_residuals=[
+                sample.to(dtype=weight_dtype) for sample in down_block_res_samples
+            ] if down_block_res_samples is not None else None,
+            mid_block_additional_residual=(
+                mid_block_res_sample.to(dtype=weight_dtype)
+                if mid_block_res_sample is not None else None
+            ),
+            **kwargs
+        )
+
+
+def scale_latents(latents):
+    latents = (latents - 0.22) * 0.75
+    return latents
+
+
+def unscale_latents(latents):
+    latents = latents / 0.75 + 0.22
+    return latents
+
+
+def scale_image(image):
+    image = image * 0.5 / 0.8
+    return image
+
+
+def unscale_image(image):
+    image = image / 0.5 * 0.8
+    return image
+
+
+class DepthControlUNet(torch.nn.Module):
+    def __init__(self, unet: RefOnlyNoisedUNet, controlnet: Optional[diffusers.ControlNetModel] = None, conditioning_scale=1.0) -> None:
+        super().__init__()
+        self.unet = unet
+        if controlnet is None:
+            self.controlnet = diffusers.ControlNetModel.from_unet(unet.unet)
+        else:
+            self.controlnet = controlnet
+        DefaultAttnProc = AttnProcessor2_0
+        if is_xformers_available():
+            DefaultAttnProc = XFormersAttnProcessor
+        self.controlnet.set_attn_processor(DefaultAttnProc())
+        self.conditioning_scale = conditioning_scale
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+
+    def forward(self, sample, timestep, encoder_hidden_states, class_labels=None, *args, cross_attention_kwargs: dict, **kwargs):
+        cross_attention_kwargs = dict(cross_attention_kwargs)
+        control_depth = cross_attention_kwargs.pop('control_depth')
+        down_block_res_samples, mid_block_res_sample = self.controlnet(
+            sample,
+            timestep,
+            encoder_hidden_states=encoder_hidden_states,
+            controlnet_cond=control_depth,
+            conditioning_scale=self.conditioning_scale,
+            return_dict=False,
+        )
+        return self.unet(
+            sample,
+            timestep,
+            encoder_hidden_states=encoder_hidden_states,
+            down_block_res_samples=down_block_res_samples,
+            mid_block_res_sample=mid_block_res_sample,
+            cross_attention_kwargs=cross_attention_kwargs
+        )
+
+
+class ModuleListDict(torch.nn.Module):
+    def __init__(self, procs: dict) -> None:
+        super().__init__()
+        self.keys = sorted(procs.keys())
+        self.values = torch.nn.ModuleList(procs[k] for k in self.keys)
+
+    def __getitem__(self, key):
+        return self.values[self.keys.index(key)]
+
+
+class SuperNet(torch.nn.Module):
+    def __init__(self, state_dict: Dict[str, torch.Tensor]):
+        super().__init__()
+        state_dict = OrderedDict((k, state_dict[k]) for k in sorted(state_dict.keys()))
+        self.layers = torch.nn.ModuleList(state_dict.values())
+        self.mapping = dict(enumerate(state_dict.keys()))
+        self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
+
+        # .processor for unet, .self_attn for text encoder
+        self.split_keys = [".processor", ".self_attn"]
+
+        # we add a hook to state_dict() and load_state_dict() so that the
+        # naming fits with `unet.attn_processors`
+        def map_to(module, state_dict, *args, **kwargs):
+            new_state_dict = {}
+            for key, value in state_dict.items():
+                num = int(key.split(".")[1])  # 0 is always "layers"
+                new_key = key.replace(f"layers.{num}", module.mapping[num])
+                new_state_dict[new_key] = value
+
+            return new_state_dict
+
+        def remap_key(key, state_dict):
+            for k in self.split_keys:
+                if k in key:
+                    return key.split(k)[0] + k
+            return key.split('.')[0]
+
+        def map_from(module, state_dict, *args, **kwargs):
+            all_keys = list(state_dict.keys())
+            for key in all_keys:
+                replace_key = remap_key(key, state_dict)
+                new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
+                state_dict[new_key] = state_dict[key]
+                del state_dict[key]
+
+        self._register_state_dict_hook(map_to)
+        self._register_load_state_dict_pre_hook(map_from, with_module=True)
+
+
+class Zero123PlusPipeline(diffusers.StableDiffusionPipeline):
+    tokenizer: transformers.CLIPTokenizer
+    text_encoder: transformers.CLIPTextModel
+    vision_encoder: transformers.CLIPVisionModelWithProjection
+
+    feature_extractor_clip: transformers.CLIPImageProcessor
+    unet: UNet2DConditionModel
+    scheduler: diffusers.schedulers.KarrasDiffusionSchedulers
+
+    vae: AutoencoderKL
+    ramping: nn.Linear
+
+    feature_extractor_vae: transformers.CLIPImageProcessor
+
+    depth_transforms_multi = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.5], [0.5])
+    ])
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        vision_encoder: transformers.CLIPVisionModelWithProjection,
+        feature_extractor_clip: CLIPImageProcessor, 
+        feature_extractor_vae: CLIPImageProcessor,
+        ramping_coefficients: Optional[list] = None,
+        safety_checker=None,
+    ):
+        DiffusionPipeline.__init__(self)
+
+        self.register_modules(
+            vae=vae, text_encoder=text_encoder, tokenizer=tokenizer,
+            unet=unet, scheduler=scheduler, safety_checker=None,
+            vision_encoder=vision_encoder,
+            feature_extractor_clip=feature_extractor_clip,
+            feature_extractor_vae=feature_extractor_vae
+        )
+        self.register_to_config(ramping_coefficients=ramping_coefficients)
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def prepare(self):
+        train_sched = DDPMScheduler.from_config(self.scheduler.config)
+        if isinstance(self.unet, UNet2DConditionModel):
+            self.unet = RefOnlyNoisedUNet(self.unet, train_sched, self.scheduler).eval()
+
+    def add_controlnet(self, controlnet: Optional[diffusers.ControlNetModel] = None, conditioning_scale=1.0):
+        self.prepare()
+        self.unet = DepthControlUNet(self.unet, controlnet, conditioning_scale)
+        return SuperNet(OrderedDict([('controlnet', self.unet.controlnet)]))
+
+    def encode_condition_image(self, image: torch.Tensor):
+        image = self.vae.encode(image).latent_dist.sample()
+        return image
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Image.Image = None,
+        prompt = "",
+        *args,
+        num_images_per_prompt: Optional[int] = 1,
+        guidance_scale=4.0,
+        depth_image: Image.Image = None,
+        output_type: Optional[str] = "pil",
+        width=640,
+        height=960,
+        num_inference_steps=28,
+        return_dict=True,
+        **kwargs
+    ):
+        self.prepare()
+        if image is None:
+            raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.")
+        assert not isinstance(image, torch.Tensor)
+        image = to_rgb_image(image)
+        image_1 = self.feature_extractor_vae(images=image, return_tensors="pt").pixel_values
+        image_2 = self.feature_extractor_clip(images=image, return_tensors="pt").pixel_values
+        if depth_image is not None and hasattr(self.unet, "controlnet"):
+            depth_image = to_rgb_image(depth_image)
+            depth_image = self.depth_transforms_multi(depth_image).to(
+                device=self.unet.controlnet.device, dtype=self.unet.controlnet.dtype
+            )
+        image = image_1.to(device=self.vae.device, dtype=self.vae.dtype)
+        image_2 = image_2.to(device=self.vae.device, dtype=self.vae.dtype)
+        cond_lat = self.encode_condition_image(image)
+        if guidance_scale > 1:
+            negative_lat = self.encode_condition_image(torch.zeros_like(image))
+            cond_lat = torch.cat([negative_lat, cond_lat])
+        encoded = self.vision_encoder(image_2, output_hidden_states=False)
+        global_embeds = encoded.image_embeds
+        global_embeds = global_embeds.unsqueeze(-2)
+        
+        if hasattr(self, "encode_prompt"):
+            encoder_hidden_states = self.encode_prompt(
+                prompt,
+                self.device,
+                num_images_per_prompt,
+                False
+            )[0]
+        else:
+            encoder_hidden_states = self._encode_prompt(
+                prompt,
+                self.device,
+                num_images_per_prompt,
+                False
+            )
+        ramp = global_embeds.new_tensor(self.config.ramping_coefficients).unsqueeze(-1)
+        encoder_hidden_states = encoder_hidden_states + global_embeds * ramp
+        cak = dict(cond_lat=cond_lat)
+        if hasattr(self.unet, "controlnet"):
+            cak['control_depth'] = depth_image
+        latents: torch.Tensor = super().__call__(
+            None,
+            *args,
+            cross_attention_kwargs=cak,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            prompt_embeds=encoder_hidden_states,
+            num_inference_steps=num_inference_steps,
+            output_type='latent',
+            width=width,
+            height=height,
+            **kwargs
+        ).images
+        latents = unscale_latents(latents)
+        if not output_type == "latent":
+            image = unscale_image(self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0])
+        else:
+            image = latents
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)