422 lines
21 KiB
Python
422 lines
21 KiB
Python
import inspect
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from typing import Callable, List, Optional, Union
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import PIL.Image
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import torch
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import logging
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from ..pipeline_utils import DiffusionPipeline
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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
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implemented for all pipelines (downloading, 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 ([`~transformers.CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer ([`~transformers.CLIPTokenizer`]):
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A `CLIPTokenizer` to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` 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 ([`StableDiffusionSafetyChecker`]):
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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 more details
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about a model's potential harms.
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feature_extractor ([`~transformers.CLIPImageProcessor`]):
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A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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"""
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tokenizer: CLIPTokenizer
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image_feature_extractor: CLIPImageProcessor
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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: KarrasDiffusionSchedulers
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def __init__(
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self,
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tokenizer: CLIPTokenizer,
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image_feature_extractor: CLIPImageProcessor,
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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: KarrasDiffusionSchedulers,
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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: int = 1,
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):
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r"""
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The call function to 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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A higher guidance scale value encourages the model to generate images closely linked to the text
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`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to
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pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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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
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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 is 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 generated image. Choose between `PIL.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 calls every `callback_steps` steps during inference. The function is called with the
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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 is called. If not specified, the callback is called at
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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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If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
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otherwise a `tuple` is returned where the first element is a list with the generated images and the
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second element is a list of `bool`s indicating whether the corresponding generated image contains
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"not-safe-for-work" (nsfw) content.
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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: int = 1,
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):
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r"""
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The call function to 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 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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A higher guidance scale value encourages the model to generate images closely linked to the text
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`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to
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pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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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
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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 is 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 generated image. Choose between `PIL.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 calls every `callback_steps` steps during inference. The function is called with the
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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 is called. If not specified, the callback is called at
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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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If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
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otherwise a `tuple` is returned where the first element is a list with the generated images and the
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second element is a list of `bool`s indicating whether the corresponding generated image contains
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"not-safe-for-work" (nsfw) content.
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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: int = 1,
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):
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r"""
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The call function to 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 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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A higher guidance scale value encourages the model to generate images closely linked to the text
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`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to
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|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
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|
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
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|
tensor is 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 generated image. Choose between `PIL.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
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
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|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
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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 is called. If not specified, the callback is called at
|
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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.ImagePipelineOutput`] or `tuple`:
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If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
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returned where the first element is a list with the generated images.
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"""
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expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__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 = VersatileDiffusionDualGuidedPipeline(**components)
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output = temp_pipeline(
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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,
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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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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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temp_pipeline._revert_dual_attention()
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return output
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