Upload pipeline_mvrag.py with huggingface_hub

pipeline_mvrag.py

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+import PIL
+import einops
+import inspect
+from torchvision.transforms import  v2
+from typing import List, Optional
+from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModel, CLIPImageProcessor
+from diffusers import AutoencoderKL, DiffusionPipeline
+from diffusers.utils import (
+    deprecate,
+    is_accelerate_available,
+    is_accelerate_version,
+    logging,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.configuration_utils import FrozenDict
+from diffusers.schedulers import DDIMScheduler
+
+from modules.mv_unet import MultiViewUNetModel
+from utils import *
+from modules.f_score import SimilarityModel
+from modules.resampler import Resampler
+
+logger = logging.get_logger(__name__)
+
+
+class MVRAGPipeline(DiffusionPipeline):
+    def __init__(
+            self,
+            vae: AutoencoderKL,
+            unet: MultiViewUNetModel,
+            tokenizer: CLIPTokenizer,
+            text_encoder: CLIPTextModel,
+            scheduler: DDIMScheduler,
+            feature_extractor: CLIPImageProcessor,
+            image_encoder: CLIPVisionModel,
+            resampler: Resampler,
+            requires_safety_checker: bool = False,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:  # type: ignore
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "  # type: ignore
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate(
+                "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
+            )
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:  # type: ignore
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate(
+                "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False
+            )
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+        self.similarity_model = SimilarityModel()
+        self.register_modules(
+            vae=vae,
+            unet=unet,
+            scheduler=scheduler,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+            resampler=resampler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+        self.transform = v2.Compose([
+            pad_to_square,
+            v2.Resize(size=256),
+        ])
+
+    def to(self, device, **kwargs):
+        super().to(device, **kwargs)
+        if self.similarity_model is not None:
+            self.similarity_model.to(device)
+        return self
+
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
+        several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
+            from accelerate import cpu_offload
+        else:
+            raise ImportError(
+                "`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"
+            )
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            cpu_offload(cpu_offloaded_model, device)
+
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError(
+                "`enable_model_offload` requires `accelerate v0.17.0` or higher."
+            )
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(
+                cpu_offloaded_model, device, prev_module_hook=hook
+            )
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                    hasattr(module, "_hf_hook")
+                    and hasattr(module._hf_hook, "execution_device")
+                    and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+            self,
+            prompt,
+            device,
+            do_classifier_free_guidance: bool,
+            negative_prompt=None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+            if prompt.endswith('.'):
+                prompt = prompt[:-1]
+            prompt = [prompt + ", 3d asset"]
+
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+            prompt = [p[:-1] + ", 3d asset" if p.endswith(".") else p + ", 3d asset" for p in prompt]
+        else:
+            raise ValueError(
+                f"`prompt` should be either a string or a list of strings, but got {type(prompt)}."
+            )
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(
+            prompt, padding="longest", return_tensors="pt"
+        ).input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+        ):
+            removed_text = self.tokenizer.batch_decode(
+                untruncated_ids[:, self.tokenizer.model_max_length - 1: -1]
+            )
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if (
+                hasattr(self.text_encoder.config, "use_attention_mask")
+                and self.text_encoder.config.use_attention_mask
+        ):
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        prompt_embeds = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt] * batch_size
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if (
+                    hasattr(self.text_encoder.config, "use_attention_mask")
+                    and self.text_encoder.config.use_attention_mask
+            ):
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )[0]
+            negative_prompt_embeds = negative_prompt_embeds.to(
+                dtype=self.text_encoder.dtype, device=device
+            )
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def prepare_latents(
+            self,
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+            dtype,
+            device,
+            generator,
+            latents=None,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+
+        if latents is None:
+            latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype
+            )
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def encode_images(self, images, device):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        ret_images = [self.transform(ret) for ret in images]
+        images_proc = self.feature_extractor(ret_images, return_tensors="pt").pixel_values
+        images_proc = images_proc.to(device=device, dtype=dtype)
+        clip_images = self.image_encoder(images_proc, output_hidden_states=True).hidden_states[-2]
+
+        neg_images = torch.zeros_like(images_proc, device=device)
+        clip_images_neg = self.image_encoder(neg_images, output_hidden_states=True).hidden_states[-2]
+
+        image_embeds = torch.cat([clip_images_neg, clip_images], dim=0)
+        image_tokens = self.resampler(image_embeds)
+        return image_tokens
+
+
+    @torch.no_grad()
+    def __call__(
+            self,
+            prompt: str = "",
+            images = None,
+            height: int = 256,
+            width: int = 256,
+            elevation: int = 0,
+            azimuth_start:int = 0,
+            num_inference_steps: int = 50,
+            num_initial_steps: int = 10,
+            guidance_scale: float = 7.0,
+            negative_prompt: str = "",
+            eta: float = 0.0,
+            output_type: Optional[str] = "numpy",  # pil, numpy
+            num_frames: int = 4,
+            device=torch.device("cuda:0"),
+            seed: Optional[int] = None,
+    ):
+        self.unet = self.unet.to(device=device)
+        self.vae = self.vae.to(device=device)
+        self.text_encoder = self.text_encoder.to(device=device)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        bs = len(prompt) if isinstance(prompt, list) else 1
+        bs = bs * 2 if do_classifier_free_guidance else bs
+        assert isinstance(images, list) and len(images) > 0 and isinstance(images[0], PIL.Image.Image)
+        self.image_encoder = self.image_encoder.to(device=device)
+        ret_eval_embs = self.similarity_model.get_embeddings(images)
+        image_tokens = self.encode_images(images, device)
+        image_tokens = einops.rearrange(image_tokens, "(b n) c f -> b (n c) f", b=bs)
+        image_tokens = torch.repeat_interleave(image_tokens, num_frames, dim=0)
+
+        prompt_embeds = self._encode_prompt(
+            prompt=prompt,
+            device=device,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+        )
+        generator = torch.Generator(device=device)
+        if seed is not None:
+            generator.manual_seed(seed)
+        # Prepare latent variables
+        latents: torch.Tensor = self.prepare_latents(
+            num_frames,
+            4,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator=generator,
+        )
+        # Get camera
+        camera = get_camera(num_frames, elevation=elevation, azimuth_start=azimuth_start).to(dtype=latents.dtype, device=device)
+
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # initial forward pass for fusion coefficient
+        base_out = self._sample(camera=camera,
+                               device=device,
+                               extra_step_kwargs=extra_step_kwargs,
+                               guidance_scale=guidance_scale,
+                               latents=latents.clone(),
+                               num_frames=num_frames,
+                               num_inference_steps=num_initial_steps,
+                               prompt_embeds=prompt_embeds,
+                               )
+        embs = self.similarity_model.get_embeddings(base_out)
+        scale = max(0, self.similarity_model.get_similarity_score(embs, ret_eval_embs))
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            latents = self._sample(camera=camera,
+                                   device=device,
+                                   extra_step_kwargs=extra_step_kwargs,
+                                   guidance_scale=guidance_scale,
+                                   image_tokens=image_tokens,
+                                   latents=latents,
+                                   num_frames=num_frames,
+                                   num_inference_steps=num_inference_steps,
+                                   progress_bar=progress_bar,
+                                   prompt_embeds=prompt_embeds,
+                                   scale=scale,
+                                   )
+        # Post-processing
+        images = latents.cpu().permute(0, 2, 3, 1).float().numpy()
+        if output_type == "pil":
+            images = self.numpy_to_pil(images)
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        return images
+
+    def _sample(self, device,
+                extra_step_kwargs, guidance_scale,
+                latents,
+                num_inference_steps, prompt_embeds,
+                progress_bar=None,
+                num_frames=4,
+                camera=None,
+                do_classifier_free_guidance=True,
+                image_tokens=None,
+                scale=1.0
+                ):
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        for i, t in enumerate(timesteps):
+            # expand the latents if we are doing classifier free guidance
+            multiplier = 2 if do_classifier_free_guidance else 1
+            latent_model_input = torch.cat([latents] * multiplier)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            unet_inputs = {
+                'x': latent_model_input,
+                'timesteps': torch.tensor([t] * num_frames * multiplier, dtype=latent_model_input.dtype, device=device),
+                'context': torch.repeat_interleave(prompt_embeds, num_frames, dim=0),
+                'num_frames': num_frames,
+                'camera': torch.cat([camera] * multiplier),
+                'images_tokens': image_tokens,
+                'scale': scale
+            }
+            # predict the noise residual
+            noise_pred = self.unet.forward(**unet_inputs)
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents: torch.Tensor = self.scheduler.step(
+                noise_pred, t, latents, **extra_step_kwargs, return_dict=False
+            )[0]
+
+            if (i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+            )) and progress_bar is not None:
+                progress_bar.update()
+        latents = self.decode_latents(latents)
+        return latents