436 lines
23 KiB
Python
436 lines
23 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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import PIL.Image
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...pipeline_utils import DiffusionPipeline
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from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from ...utils import logging
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from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
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from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
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from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class VersatileDiffusionPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using Stable 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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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [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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safety_checker ([`StableDiffusionMegaSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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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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tokenizer: CLIPTokenizer
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image_feature_extractor: CLIPFeatureExtractor
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text_encoder: CLIPTextModel
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image_encoder: CLIPVisionModel
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image_unet: UNet2DConditionModel
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text_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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tokenizer: CLIPTokenizer,
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image_feature_extractor: CLIPFeatureExtractor,
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text_encoder: CLIPTextModel,
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image_encoder: CLIPVisionModel,
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image_unet: UNet2DConditionModel,
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text_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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tokenizer=tokenizer,
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image_feature_extractor=image_feature_extractor,
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text_encoder=text_encoder,
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image_encoder=image_encoder,
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image_unet=image_unet,
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text_unet=text_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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@torch.no_grad()
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def image_variation(
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self,
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image: Union[torch.FloatTensor, PIL.Image.Image],
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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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):
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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 VersatileDiffusionPipeline
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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 = VersatileDiffusionPipeline.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_variation(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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expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys()
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components = {name: component for name, component in self.components.items() if name in expected_components}
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return VersatileDiffusionImageVariationPipeline(**components)(
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image=image,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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)
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@torch.no_grad()
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def text_to_image(
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self,
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prompt: Union[str, List[str]],
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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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):
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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 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 VersatileDiffusionPipeline
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>>> import torch
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>>> pipe = VersatileDiffusionPipeline.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.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0]
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>>> image.save("./astronaut.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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expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys()
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components = {name: component for name, component in self.components.items() if name in expected_components}
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temp_pipeline = VersatileDiffusionTextToImagePipeline(**components)
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output = temp_pipeline(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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)
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# swap the attention blocks back to the original state
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temp_pipeline._swap_unet_attention_blocks()
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return output
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@torch.no_grad()
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def dual_guided(
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self,
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prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
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image: Union[str, List[str]],
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text_to_image_strength: float = 0.5,
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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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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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):
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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 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
|
|
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).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`torch.Generator`, *optional*):
|
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
|
to make generation deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor will ge generated by sampling using the supplied random `generator`.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
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 VersatileDiffusionPipeline
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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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>>> text = "a red car in the sun"
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>>> pipe = VersatileDiffusionPipeline.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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>>> text_to_image_strength = 0.75
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>>> image = pipe.dual_guided(
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... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
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... ).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.ImagePipelineOutput`] or `tuple`:
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|
[`~pipelines.stable_diffusion.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
|
|
returning a tuple, the first element is a list with the generated images.
|
|
"""
|
|
|
|
expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()
|
|
components = {name: component for name, component in self.components.items() if name in expected_components}
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|
temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components)
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|
output = temp_pipeline(
|
|
prompt=prompt,
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|
image=image,
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|
text_to_image_strength=text_to_image_strength,
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|
height=height,
|
|
width=width,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
eta=eta,
|
|
generator=generator,
|
|
latents=latents,
|
|
output_type=output_type,
|
|
return_dict=return_dict,
|
|
callback=callback,
|
|
callback_steps=callback_steps,
|
|
)
|
|
temp_pipeline._revert_dual_attention()
|
|
|
|
return output
|