311 lines
15 KiB
Python
311 lines
15 KiB
Python
import inspect
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from typing import List, Optional, Union, Callable
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import numpy as np
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import torch
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import PIL
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from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, PNDMScheduler
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from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker, StableDiffusionPipelineOutput
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from diffusers.utils import logging
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from tqdm.auto import tqdm
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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logger = logging.get_logger(__name__)
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def preprocess_image(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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def preprocess_mask(mask):
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mask = mask.convert("L")
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w, h = mask.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = np.tile(mask, (4, 1, 1))
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mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
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mask = 1 - mask # repaint white, keep black
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mask = torch.from_numpy(mask)
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return mask
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class StableDiffusionInpaintPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
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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 latens. 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 offsensive or harmful.
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Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) 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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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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scheduler = scheduler.set_format("pt")
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logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `set_attention_slice`
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self.enable_attention_slice(None)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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init_image: Union[torch.FloatTensor, PIL.Image.Image],
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mask_image: Union[torch.FloatTensor, PIL.Image.Image],
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strength: float = 0.8,
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num_inference_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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eta: Optional[float] = 0.0,
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generator: Optional[torch.Generator] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callbacks: List[Callable[[int], None]] = None
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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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init_image (`torch.FloatTensor` or `PIL.Image.Image`):
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`Image`, or tensor representing an image batch, that will be used as the starting point for the
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process. This is the image whose masked region will be inpainted.
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mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
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`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
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replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
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converted to a single channel (luminance) before use.
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strength (`float`, *optional*, defaults to 0.8):
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Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
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is 1, the denoising process will be run on the masked area for the full number of iterations specified
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in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
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noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
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the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
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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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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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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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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 `nd.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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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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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if strength < 0 or strength > 1:
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raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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# set timesteps
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accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
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extra_set_kwargs = {}
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offset = 0
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if accepts_offset:
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offset = 1
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extra_set_kwargs["offset"] = 1
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self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
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# preprocess image
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init_image = preprocess_image(init_image).to(self.device)
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# encode the init image into latents and scale the latents
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init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
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init_latents = init_latent_dist.sample(generator=generator)
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init_latents = 0.18215 * init_latents
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# Expand init_latents for batch_size
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init_latents = torch.cat([init_latents] * batch_size)
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init_latents_orig = init_latents
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# preprocess mask
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mask = preprocess_mask(mask_image).to(self.device)
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mask = torch.cat([mask] * batch_size)
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# check sizes
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if not mask.shape == init_latents.shape:
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raise ValueError("The mask and init_image should be the same size!")
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# get the original timestep using init_timestep
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init_timestep = int(num_inference_steps * strength) + offset
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init_timestep = min(init_timestep, num_inference_steps)
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timesteps = self.scheduler.timesteps[-init_timestep]
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timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
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# add noise to latents using the timesteps
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noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
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init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
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# get prompt text embeddings
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text_input = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_encoder_device = self.text_encoder.device
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text_embeddings = self.text_encoder(text_input.input_ids.to(text_encoder_device, non_blocking=True))[0].to(self.device, non_blocking=True)
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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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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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max_length = text_input.input_ids.shape[-1]
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uncond_input = self.tokenizer(
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[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(text_encoder_device, non_blocking=True))[0].to(self.device, non_blocking=True)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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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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latents = init_latents
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t_start = max(num_inference_steps - init_timestep + offset, 0)
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for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
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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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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_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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# masking
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init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
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latents = (init_latents_proper * mask) + (latents * (1 - mask))
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if callbacks is not None:
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for callback in callbacks:
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callback(i)
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# scale and decode the image latents with vae
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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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image = image.cpu().permute(0, 2, 3, 1).numpy()
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# run safety checker
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safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
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image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
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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:
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return (image, has_nsfw_concept)
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return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
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