441 lines
22 KiB
Python
441 lines
22 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from typing import Callable, List, Optional, Union
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import numpy as np
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import torch
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import torch.utils.checkpoint
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import PIL
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from transformers import CLIPFeatureExtractor, CLIPVisionModelWithProjection
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from ...utils import is_accelerate_available, logging
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
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r"""
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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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Parameters:
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vqvae ([`VQModel`]):
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Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
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bert ([`LDMBertModel`]):
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Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture.
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tokenizer (`transformers.BertTokenizer`):
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Tokenizer of class
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[BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer).
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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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"""
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image_feature_extractor: CLIPFeatureExtractor
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image_encoder: CLIPVisionModelWithProjection
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image_unet: UNet2DConditionModel
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vae: AutoencoderKL
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
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def __init__(
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self,
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image_feature_extractor: CLIPFeatureExtractor,
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image_encoder: CLIPVisionModelWithProjection,
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image_unet: UNet2DConditionModel,
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vae: AutoencoderKL,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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):
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super().__init__()
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self.register_modules(
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image_feature_extractor=image_feature_extractor,
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image_encoder=image_encoder,
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image_unet=image_unet,
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vae=vae,
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scheduler=scheduler,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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def enable_sequential_cpu_offload(self, gpu_id=0):
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r"""
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Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
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text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
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`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
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"""
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if is_accelerate_available():
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from accelerate import cpu_offload
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else:
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raise ImportError("Please install accelerate via `pip install accelerate`")
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device = torch.device(f"cuda:{gpu_id}")
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for cpu_offloaded_model in [self.image_unet, self.text_unet, self.text_encoder, self.vae]:
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if cpu_offloaded_model is not None:
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cpu_offload(cpu_offloaded_model, device)
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@property
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device with unet->image_unet
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def _execution_device(self):
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r"""
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Returns the device on which the pipeline's models will be executed. After calling
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`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
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hooks.
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"""
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if self.device != torch.device("meta") or not hasattr(self.image_unet, "_hf_hook"):
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return self.device
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for module in self.image_unet.modules():
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if (
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hasattr(module, "_hf_hook")
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and hasattr(module._hf_hook, "execution_device")
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and module._hf_hook.execution_device is not None
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):
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return torch.device(module._hf_hook.execution_device)
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return self.device
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def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `list(int)`):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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"""
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def normalize_embeddings(encoder_output):
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embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)
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embeds = self.image_encoder.visual_projection(embeds)
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embeds_pooled = embeds[:, 0:1]
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embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)
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return embeds
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if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4:
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prompt = [p for p in prompt]
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batch_size = len(prompt) if isinstance(prompt, list) else 1
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# get prompt text embeddings
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image_input = self.image_feature_extractor(images=prompt, return_tensors="pt")
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pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)
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image_embeddings = self.image_encoder(pixel_values)
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image_embeddings = normalize_embeddings(image_embeddings)
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# duplicate image embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = image_embeddings.shape
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image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
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image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
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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_images: List[str]
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if negative_prompt is None:
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uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, PIL.Image.Image):
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uncond_images = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_images = negative_prompt
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uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt")
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pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)
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uncond_embeddings = self.image_encoder(pixel_values)
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uncond_embeddings = normalize_embeddings(uncond_embeddings)
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = uncond_embeddings.shape[1]
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uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
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uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
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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 conditional embeddings into a single batch
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# to avoid doing two forward passes
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image_embeddings = torch.cat([uncond_embeddings, image_embeddings])
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return image_embeddings
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
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def decode_latents(self, latents):
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents).sample
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image = (image / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
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image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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return image
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(self, generator, eta):
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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://arxiv.org/abs/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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return extra_step_kwargs
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs
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def check_inputs(self, image, height, width, callback_steps):
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if (
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not isinstance(image, torch.Tensor)
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and not isinstance(image, PIL.Image.Image)
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and not isinstance(image, list)
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):
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raise ValueError(
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"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
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f" {type(image)}"
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)
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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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
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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 = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
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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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rand_device = "cpu" if device.type == "mps" else device
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if isinstance(generator, list):
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shape = (1,) + shape[1:]
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latents = [
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torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
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for i in range(batch_size)
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]
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latents = torch.cat(latents, dim=0).to(device)
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else:
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latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
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else:
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if latents.shape != shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
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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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@torch.no_grad()
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def __call__(
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self,
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image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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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[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = 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.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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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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image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
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The image prompt or prompts to guide the image generation.
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height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor):
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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://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). 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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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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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://arxiv.org/abs/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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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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latents (`torch.FloatTensor`, *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 ge generated by sampling using the supplied random `generator`.
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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 [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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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.FloatTensor)`.
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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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Examples:
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```py
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>>> from diffusers import VersatileDiffusionImageVariationPipeline
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>>> import torch
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>>> import requests
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>>> from io import BytesIO
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>>> from PIL import Image
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>>> # let's download an initial image
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>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
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>>> response = requests.get(url)
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>>> image = Image.open(BytesIO(response.content)).convert("RGB")
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>>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(
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... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> generator = torch.Generator(device="cuda").manual_seed(0)
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>>> image = pipe(image, generator=generator).images[0]
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>>> image.save("./car_variation.png")
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```
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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# 0. Default height and width to unet
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height = height or self.image_unet.config.sample_size * self.vae_scale_factor
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width = width or self.image_unet.config.sample_size * self.vae_scale_factor
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(image, height, width, callback_steps)
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# 2. Define call parameters
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batch_size = 1 if isinstance(image, PIL.Image.Image) else len(image)
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device = self._execution_device
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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://arxiv.org/pdf/2205.11487.pdf . `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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# 3. Encode input prompt
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image_embeddings = self._encode_prompt(
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image, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
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)
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# 4. Prepare timesteps
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self.scheduler.set_timesteps(num_inference_steps, device=device)
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timesteps = self.scheduler.timesteps
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# 5. Prepare latent variables
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num_channels_latents = self.image_unet.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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image_embeddings.dtype,
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device,
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generator,
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latents,
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)
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# 6. Prepare extra step kwargs.
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extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
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# 7. Denoising loop
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for i, t in enumerate(self.progress_bar(timesteps)):
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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.image_unet(latent_model_input, t, encoder_hidden_states=image_embeddings).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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|
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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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|
callback(i, t, latents)
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|
|
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# 8. Post-processing
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|
image = self.decode_latents(latents)
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|
|
|
# 9. Convert to PIL
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|
if output_type == "pil":
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|
image = self.numpy_to_pil(image)
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|
|
|
if not return_dict:
|
|
return (image,)
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|
|
|
return ImagePipelineOutput(images=image)
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