mirror of
https://github.com/huggingface/diffusers.git
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345 lines
17 KiB
Python
345 lines
17 KiB
Python
import inspect
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from typing import Callable, List, Optional, Union
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import torch
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from PIL import Image
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from retriever import Retriever, normalize_images, preprocess_images
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from transformers import CLIPImageProcessor, CLIPModel, CLIPTokenizer
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DiffusionPipeline,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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ImagePipelineOutput,
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LMSDiscreteScheduler,
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PNDMScheduler,
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UNet2DConditionModel,
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)
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
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from diffusers.utils import logging
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from diffusers.utils.torch_utils import randn_tensor
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class RDMPipeline(DiffusionPipeline, StableDiffusionMixin):
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r"""
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Pipeline for text-to-image generation using Retrieval Augmented Diffusion.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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clip ([`CLIPModel`]):
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Frozen CLIP model. Retrieval Augmented Diffusion uses the CLIP model, specifically the
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[clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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feature_extractor ([`CLIPImageProcessor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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def __init__(
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self,
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vae: AutoencoderKL,
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clip: CLIPModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[
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DDIMScheduler,
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PNDMScheduler,
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LMSDiscreteScheduler,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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DPMSolverMultistepScheduler,
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],
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feature_extractor: CLIPImageProcessor,
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retriever: Optional[Retriever] = None,
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):
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super().__init__()
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self.register_modules(
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vae=vae,
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clip=clip,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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)
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# Copy from statement here and all the methods we take from stable_diffusion_pipeline
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.retriever = retriever
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def _encode_prompt(self, prompt):
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# get prompt text embeddings
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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prompt_embeds = self.clip.get_text_features(text_input_ids.to(self.device))
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prompt_embeds = prompt_embeds / torch.linalg.norm(prompt_embeds, dim=-1, keepdim=True)
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prompt_embeds = prompt_embeds[:, None, :]
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return prompt_embeds
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def _encode_image(self, retrieved_images, batch_size):
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if len(retrieved_images[0]) == 0:
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return None
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for i in range(len(retrieved_images)):
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retrieved_images[i] = normalize_images(retrieved_images[i])
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retrieved_images[i] = preprocess_images(retrieved_images[i], self.feature_extractor).to(
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self.clip.device, dtype=self.clip.dtype
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)
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_, c, h, w = retrieved_images[0].shape
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retrieved_images = torch.reshape(torch.cat(retrieved_images, dim=0), (-1, c, h, w))
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image_embeddings = self.clip.get_image_features(retrieved_images)
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image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)
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_, d = image_embeddings.shape
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image_embeddings = torch.reshape(image_embeddings, (batch_size, -1, d))
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return image_embeddings
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def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
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shape = (
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batch_size,
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num_channels_latents,
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int(height) // self.vae_scale_factor,
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int(width) // self.vae_scale_factor,
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)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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def retrieve_images(self, retrieved_images, prompt_embeds, knn=10):
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if self.retriever is not None:
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additional_images = self.retriever.retrieve_imgs_batch(prompt_embeds[:, 0].cpu(), knn).total_examples
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for i in range(len(retrieved_images)):
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retrieved_images[i] += additional_images[i][self.retriever.config.image_column]
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return retrieved_images
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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retrieved_images: Optional[List[Image.Image]] = None,
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height: int = 768,
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width: int = 768,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.Tensor] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
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callback_steps: Optional[int] = 1,
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knn: Optional[int] = 10,
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**kwargs,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation.
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height (`int`, *optional*, defaults to 512):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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latents (`torch.Tensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will be generated by sampling using the supplied random `generator`.
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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Returns:
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[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
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`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
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generated images.
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"""
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height = height or self.unet.config.sample_size * self.vae_scale_factor
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width = width or self.unet.config.sample_size * self.vae_scale_factor
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if retrieved_images is not None:
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retrieved_images = [retrieved_images for _ in range(batch_size)]
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else:
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retrieved_images = [[] for _ in range(batch_size)]
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device = self._execution_device
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if (callback_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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if prompt_embeds is None:
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prompt_embeds = self._encode_prompt(prompt)
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retrieved_images = self.retrieve_images(retrieved_images, prompt_embeds, knn=knn)
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image_embeddings = self._encode_image(retrieved_images, batch_size)
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if image_embeddings is not None:
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prompt_embeds = torch.cat([prompt_embeds, image_embeddings], dim=1)
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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uncond_embeddings = torch.zeros_like(prompt_embeds).to(prompt_embeds.device)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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prompt_embeds = torch.cat([uncond_embeddings, prompt_embeds])
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# get the initial random noise unless the user supplied it
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num_channels_latents = self.unet.config.in_channels
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latents = self.prepare_latents(
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batch_size * num_images_per_prompt,
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num_channels_latents,
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height,
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width,
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prompt_embeds.dtype,
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device,
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generator,
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latents,
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)
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# set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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# Some schedulers like PNDM have timesteps as arrays
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# It's more optimized to move all timesteps to correct device beforehand
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timesteps_tensor = self.scheduler.timesteps.to(self.device)
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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for i, t in enumerate(self.progress_bar(timesteps_tensor)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
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# call the callback, if provided
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if callback is not None and i % callback_steps == 0:
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step_idx = i // getattr(self.scheduler, "order", 1)
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callback(step_idx, t, latents)
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if not output_type == "latent":
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image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
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else:
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image = latents
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image = self.image_processor.postprocess(
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image, output_type=output_type, do_denormalize=[True] * image.shape[0]
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)
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# Offload last model to CPU
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if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
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self.final_offload_hook.offload()
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if not return_dict:
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return (image,)
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return ImagePipelineOutput(images=image)
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