1244 lines
55 KiB
Python
1244 lines
55 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import PIL
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import torch
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from packaging import version
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
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from diffusers.configuration_utils import FrozenDict
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.loaders import (
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FromSingleFileMixin,
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LoraLoaderMixin,
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TextualInversionLoaderMixin,
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)
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from diffusers.models import (
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AsymmetricAutoencoderKL,
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AutoencoderKL,
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UNet2DConditionModel,
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)
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from diffusers.schedulers import KarrasDiffusionSchedulers
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from diffusers.utils import (
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deprecate,
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is_accelerate_available,
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is_accelerate_version,
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logging,
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)
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.pipelines.stable_diffusion.safety_checker import (
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StableDiffusionSafetyChecker,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def prepare_mask_and_masked_image(
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image, mask, height, width, return_image: bool = False
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):
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"""
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Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
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converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
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``image`` and ``1`` for the ``mask``.
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The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
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binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
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Args:
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image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
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It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
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``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
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mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
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It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
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``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
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Raises:
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ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
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should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
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TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
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(ot the other way around).
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Returns:
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tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
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dimensions: ``batch x channels x height x width``.
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"""
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if image is None:
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raise ValueError("`image` input cannot be undefined.")
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if mask is None:
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raise ValueError("`mask_image` input cannot be undefined.")
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if isinstance(image, torch.Tensor):
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if not isinstance(mask, torch.Tensor):
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raise TypeError(
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f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not"
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)
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# Batch single image
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if image.ndim == 3:
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assert (
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image.shape[0] == 3
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), "Image outside a batch should be of shape (3, H, W)"
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image = image.unsqueeze(0)
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# Batch and add channel dim for single mask
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if mask.ndim == 2:
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mask = mask.unsqueeze(0).unsqueeze(0)
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# Batch single mask or add channel dim
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if mask.ndim == 3:
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# Single batched mask, no channel dim or single mask not batched but channel dim
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if mask.shape[0] == 1:
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mask = mask.unsqueeze(0)
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# Batched masks no channel dim
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else:
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mask = mask.unsqueeze(1)
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assert (
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image.ndim == 4 and mask.ndim == 4
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), "Image and Mask must have 4 dimensions"
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assert (
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image.shape[-2:] == mask.shape[-2:]
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), "Image and Mask must have the same spatial dimensions"
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assert (
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image.shape[0] == mask.shape[0]
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), "Image and Mask must have the same batch size"
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# Check image is in [-1, 1]
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if image.min() < -1 or image.max() > 1:
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raise ValueError("Image should be in [-1, 1] range")
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# Check mask is in [0, 1]
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if mask.min() < 0 or mask.max() > 1:
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raise ValueError("Mask should be in [0, 1] range")
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# Binarize mask
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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# Image as float32
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image = image.to(dtype=torch.float32)
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elif isinstance(mask, torch.Tensor):
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raise TypeError(
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f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not"
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)
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else:
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# preprocess image
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if isinstance(image, (PIL.Image.Image, np.ndarray)):
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image = [image]
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if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
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# resize all images w.r.t passed height an width
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image = [
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i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image
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]
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image = [np.array(i.convert("RGB"))[None, :] for i in image]
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image = np.concatenate(image, axis=0)
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elif isinstance(image, list) and isinstance(image[0], np.ndarray):
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image = np.concatenate([i[None, :] for i in image], axis=0)
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image = image.transpose(0, 3, 1, 2)
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image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
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# preprocess mask
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if isinstance(mask, (PIL.Image.Image, np.ndarray)):
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mask = [mask]
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if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
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mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
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mask = np.concatenate(
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[np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
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)
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mask = mask.astype(np.float32) / 255.0
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elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
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mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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mask = torch.from_numpy(mask)
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masked_image = image * (mask < 0.5)
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# n.b. ensure backwards compatibility as old function does not return image
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if return_image:
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return mask, masked_image, image
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return mask, masked_image
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class StableDiffusionInpaintPipeline(
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DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
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):
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r"""
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Pipeline for text-guided image inpainting 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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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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Args:
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vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):
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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 ([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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_optional_components = ["safety_checker", "feature_extractor"]
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def __init__(
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self,
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vae: Union[AutoencoderKL, AsymmetricAutoencoderKL],
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: KarrasDiffusionSchedulers,
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPImageProcessor,
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requires_safety_checker: bool = True,
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):
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super().__init__()
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if (
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hasattr(scheduler.config, "steps_offset")
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and scheduler.config.steps_offset != 1
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):
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate(
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"steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
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)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if (
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hasattr(scheduler.config, "skip_prk_steps")
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and scheduler.config.skip_prk_steps is False
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):
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} has not set the configuration"
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" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
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" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
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" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
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" Hub, it would be very nice if you could open a Pull request for the"
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" `scheduler/scheduler_config.json` file"
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)
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deprecate(
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"skip_prk_steps not set",
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"1.0.0",
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deprecation_message,
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standard_warn=False,
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)
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new_config = dict(scheduler.config)
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new_config["skip_prk_steps"] = True
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scheduler._internal_dict = FrozenDict(new_config)
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if safety_checker is None and requires_safety_checker:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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if safety_checker is not None and feature_extractor is None:
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raise ValueError(
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"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
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" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
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)
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is_unet_version_less_0_9_0 = hasattr(
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unet.config, "_diffusers_version"
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) and version.parse(
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version.parse(unet.config._diffusers_version).base_version
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) < version.parse(
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"0.9.0.dev0"
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)
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is_unet_sample_size_less_64 = (
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hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
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)
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if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
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deprecation_message = (
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"The configuration file of the unet has set the default `sample_size` to smaller than"
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" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
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" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
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" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
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" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
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" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
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" in the config might lead to incorrect results in future versions. If you have downloaded this"
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" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
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" the `unet/config.json` file"
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)
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deprecate(
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"sample_size<64", "1.0.0", deprecation_message, standard_warn=False
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)
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new_config = dict(unet.config)
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new_config["sample_size"] = 64
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unet._internal_dict = FrozenDict(new_config)
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# Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
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if unet.config.in_channels != 9:
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logger.info(
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f"You have loaded a UNet with {unet.config.in_channels} input channels which."
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)
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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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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.register_to_config(requires_safety_checker=requires_safety_checker)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
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def enable_model_cpu_offload(self, gpu_id=0):
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r"""
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Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
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time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
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Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
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iterative execution of the `unet`.
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"""
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if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
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from accelerate import cpu_offload_with_hook
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else:
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raise ImportError(
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"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."
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)
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device = torch.device(f"cuda:{gpu_id}")
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if self.device.type != "cpu":
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self.to("cpu", silence_dtype_warnings=True)
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torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
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hook = None
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for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
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_, hook = cpu_offload_with_hook(
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cpu_offloaded_model, device, prev_module_hook=hook
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)
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if self.safety_checker is not None:
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_, hook = cpu_offload_with_hook(
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self.safety_checker, device, prev_module_hook=hook
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)
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# We'll offload the last model manually.
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self.final_offload_hook = hook
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
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def _encode_prompt(
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self,
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promptA,
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promptB,
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t,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_promptA=None,
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negative_promptB=None,
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t_nag=None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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lora_scale: Optional[float] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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lora_scale (`float`, *optional*):
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A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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"""
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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if lora_scale is not None and isinstance(self, LoraLoaderMixin):
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self._lora_scale = lora_scale
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prompt = promptA
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negative_prompt = negative_promptA
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if promptA is not None and isinstance(promptA, str):
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batch_size = 1
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elif promptA is not None and isinstance(promptA, list):
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batch_size = len(promptA)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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promptA = self.maybe_convert_prompt(promptA, self.tokenizer)
|
|
|
|
text_inputsA = self.tokenizer(
|
|
promptA,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_inputsB = self.tokenizer(
|
|
promptB,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_idsA = text_inputsA.input_ids
|
|
text_input_idsB = text_inputsB.input_ids
|
|
untruncated_ids = self.tokenizer(
|
|
promptA, padding="longest", return_tensors="pt"
|
|
).input_ids
|
|
|
|
if untruncated_ids.shape[-1] >= text_input_idsA.shape[
|
|
-1
|
|
] and not torch.equal(text_input_idsA, untruncated_ids):
|
|
removed_text = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
if (
|
|
hasattr(self.text_encoder.config, "use_attention_mask")
|
|
and self.text_encoder.config.use_attention_mask
|
|
):
|
|
attention_mask = text_inputsA.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
# print("text_input_idsA: ",text_input_idsA)
|
|
# print("text_input_idsB: ",text_input_idsB)
|
|
# print('t: ',t)
|
|
|
|
prompt_embedsA = self.text_encoder(
|
|
text_input_idsA.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
prompt_embedsA = prompt_embedsA[0]
|
|
|
|
prompt_embedsB = self.text_encoder(
|
|
text_input_idsB.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
prompt_embedsB = prompt_embedsB[0]
|
|
prompt_embeds = prompt_embedsA * (t) + (1 - t) * prompt_embedsB
|
|
# print("prompt_embeds: ",prompt_embeds)
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(
|
|
bs_embed * num_images_per_prompt, seq_len, -1
|
|
)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
uncond_tokensA: List[str]
|
|
uncond_tokensB: List[str]
|
|
if negative_prompt is None:
|
|
uncond_tokensA = [""] * batch_size
|
|
uncond_tokensB = [""] * batch_size
|
|
elif prompt is not None and type(prompt) is not type(negative_prompt):
|
|
raise TypeError(
|
|
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
|
f" {type(prompt)}."
|
|
)
|
|
elif isinstance(negative_prompt, str):
|
|
uncond_tokensA = [negative_promptA]
|
|
uncond_tokensB = [negative_promptB]
|
|
elif batch_size != len(negative_prompt):
|
|
raise ValueError(
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
|
" the batch size of `prompt`."
|
|
)
|
|
else:
|
|
uncond_tokensA = negative_promptA
|
|
uncond_tokensB = negative_promptB
|
|
|
|
# textual inversion: procecss multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokensA = self.maybe_convert_prompt(
|
|
uncond_tokensA, self.tokenizer
|
|
)
|
|
uncond_tokensB = self.maybe_convert_prompt(
|
|
uncond_tokensB, self.tokenizer
|
|
)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_inputA = self.tokenizer(
|
|
uncond_tokensA,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
uncond_inputB = self.tokenizer(
|
|
uncond_tokensB,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
if (
|
|
hasattr(self.text_encoder.config, "use_attention_mask")
|
|
and self.text_encoder.config.use_attention_mask
|
|
):
|
|
attention_mask = uncond_inputA.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embedsA = self.text_encoder(
|
|
uncond_inputA.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embedsB = self.text_encoder(
|
|
uncond_inputB.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = (
|
|
negative_prompt_embedsA[0] * (t_nag)
|
|
+ (1 - t_nag) * negative_prompt_embedsB[0]
|
|
)
|
|
|
|
# negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(
|
|
dtype=prompt_embeds_dtype, device=device
|
|
)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(
|
|
1, num_images_per_prompt, 1
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(
|
|
batch_size * num_images_per_prompt, seq_len, -1
|
|
)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
# print("prompt_embeds: ",prompt_embeds)
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
return prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
|
def run_safety_checker(self, image, device, dtype):
|
|
if self.safety_checker is None:
|
|
has_nsfw_concept = None
|
|
else:
|
|
if torch.is_tensor(image):
|
|
feature_extractor_input = self.image_processor.postprocess(
|
|
image, output_type="pil"
|
|
)
|
|
else:
|
|
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
|
safety_checker_input = self.feature_extractor(
|
|
feature_extractor_input, return_tensors="pt"
|
|
).to(device)
|
|
image, has_nsfw_concept = self.safety_checker(
|
|
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
|
)
|
|
return image, has_nsfw_concept
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(
|
|
inspect.signature(self.scheduler.step).parameters.keys()
|
|
)
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(
|
|
inspect.signature(self.scheduler.step).parameters.keys()
|
|
)
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
strength,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
):
|
|
if strength < 0 or strength > 1:
|
|
raise ValueError(
|
|
f"The value of strength should in [0.0, 1.0] but is {strength}"
|
|
)
|
|
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
raise ValueError(
|
|
f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
|
|
)
|
|
|
|
if (callback_steps is None) or (
|
|
callback_steps is not None
|
|
and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
|
):
|
|
raise ValueError(
|
|
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
|
f" {type(callback_steps)}."
|
|
)
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and (
|
|
not isinstance(prompt, str) and not isinstance(prompt, list)
|
|
):
|
|
raise ValueError(
|
|
f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
|
|
)
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents=None,
|
|
image=None,
|
|
timestep=None,
|
|
is_strength_max=True,
|
|
return_noise=False,
|
|
return_image_latents=False,
|
|
):
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
height // self.vae_scale_factor,
|
|
width // self.vae_scale_factor,
|
|
)
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
if (image is None or timestep is None) and not is_strength_max:
|
|
raise ValueError(
|
|
"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
|
|
"However, either the image or the noise timestep has not been provided."
|
|
)
|
|
|
|
if return_image_latents or (latents is None and not is_strength_max):
|
|
image = image.to(device=device, dtype=dtype)
|
|
image_latents = self._encode_vae_image(image=image, generator=generator)
|
|
|
|
if latents is None:
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
# if strength is 1. then initialise the latents to noise, else initial to image + noise
|
|
latents = (
|
|
noise
|
|
if is_strength_max
|
|
else self.scheduler.add_noise(image_latents, noise, timestep)
|
|
)
|
|
# if pure noise then scale the initial latents by the Scheduler's init sigma
|
|
latents = (
|
|
latents * self.scheduler.init_noise_sigma
|
|
if is_strength_max
|
|
else latents
|
|
)
|
|
else:
|
|
noise = latents.to(device)
|
|
latents = noise * self.scheduler.init_noise_sigma
|
|
|
|
outputs = (latents,)
|
|
|
|
if return_noise:
|
|
outputs += (noise,)
|
|
|
|
if return_image_latents:
|
|
outputs += (image_latents,)
|
|
|
|
return outputs
|
|
|
|
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
|
|
if isinstance(generator, list):
|
|
image_latents = [
|
|
self.vae.encode(image[i : i + 1]).latent_dist.sample(
|
|
generator=generator[i]
|
|
)
|
|
for i in range(image.shape[0])
|
|
]
|
|
image_latents = torch.cat(image_latents, dim=0)
|
|
else:
|
|
image_latents = self.vae.encode(image).latent_dist.sample(
|
|
generator=generator
|
|
)
|
|
|
|
image_latents = self.vae.config.scaling_factor * image_latents
|
|
|
|
return image_latents
|
|
|
|
def prepare_mask_latents(
|
|
self,
|
|
mask,
|
|
masked_image,
|
|
batch_size,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
do_classifier_free_guidance,
|
|
):
|
|
# resize the mask to latents shape as we concatenate the mask to the latents
|
|
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
|
|
# and half precision
|
|
mask = torch.nn.functional.interpolate(
|
|
mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
|
|
)
|
|
mask = mask.to(device=device, dtype=dtype)
|
|
|
|
masked_image = masked_image.to(device=device, dtype=dtype)
|
|
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
|
|
|
|
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
|
|
if mask.shape[0] < batch_size:
|
|
if not batch_size % mask.shape[0] == 0:
|
|
raise ValueError(
|
|
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
|
|
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
|
|
" of masks that you pass is divisible by the total requested batch size."
|
|
)
|
|
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
|
|
if masked_image_latents.shape[0] < batch_size:
|
|
if not batch_size % masked_image_latents.shape[0] == 0:
|
|
raise ValueError(
|
|
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
|
|
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
|
|
" Make sure the number of images that you pass is divisible by the total requested batch size."
|
|
)
|
|
masked_image_latents = masked_image_latents.repeat(
|
|
batch_size // masked_image_latents.shape[0], 1, 1, 1
|
|
)
|
|
|
|
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
|
|
masked_image_latents = (
|
|
torch.cat([masked_image_latents] * 2)
|
|
if do_classifier_free_guidance
|
|
else masked_image_latents
|
|
)
|
|
|
|
# aligning device to prevent device errors when concating it with the latent model input
|
|
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
|
|
return mask, masked_image_latents
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
|
|
def get_timesteps(self, num_inference_steps, strength, device):
|
|
# get the original timestep using init_timestep
|
|
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
|
|
|
t_start = max(num_inference_steps - init_timestep, 0)
|
|
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
|
|
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
promptA: Union[str, List[str]] = None,
|
|
promptB: Union[str, List[str]] = None,
|
|
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
|
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
strength: float = 1.0,
|
|
tradoff: float = 1.0,
|
|
tradoff_nag: float = 1.0,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
negative_promptA: Optional[Union[str, List[str]]] = None,
|
|
negative_promptB: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
callback_steps: int = 1,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
task_class: Union[torch.Tensor, float, int] = None,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
image (`PIL.Image.Image`):
|
|
`Image` or tensor representing an image batch to be inpainted (which parts of the image to be masked
|
|
out with `mask_image` and repainted according to `prompt`).
|
|
mask_image (`PIL.Image.Image`):
|
|
`Image` or tensor representing an image batch to mask `image`. White pixels in the mask are repainted
|
|
while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel
|
|
(luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the
|
|
expected shape would be `(B, H, W, 1)`.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated image.
|
|
strength (`float`, *optional*, defaults to 1.0):
|
|
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
|
|
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
|
|
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
|
|
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
|
|
essentially ignores `image`.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference. This parameter is modulated by `strength`.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
|
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
|
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
|
every step.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
|
|
Examples:
|
|
|
|
```py
|
|
>>> import PIL
|
|
>>> import requests
|
|
>>> import torch
|
|
>>> from io import BytesIO
|
|
|
|
>>> from diffusers import StableDiffusionInpaintPipeline
|
|
|
|
|
|
>>> def download_image(url):
|
|
... response = requests.get(url)
|
|
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
|
|
|
|
|
|
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
|
|
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
|
|
|
|
>>> init_image = download_image(img_url).resize((512, 512))
|
|
>>> mask_image = download_image(mask_url).resize((512, 512))
|
|
|
|
>>> pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
... "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
|
|
... )
|
|
>>> pipe = pipe.to("cuda")
|
|
|
|
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
|
>>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
|
|
```
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
# 0. Default height and width to unet
|
|
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
|
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
|
prompt = promptA
|
|
negative_prompt = negative_promptA
|
|
# 1. Check inputs
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
strength,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
cross_attention_kwargs.get("scale", None)
|
|
if cross_attention_kwargs is not None
|
|
else None
|
|
)
|
|
prompt_embeds = self._encode_prompt(
|
|
promptA,
|
|
promptB,
|
|
tradoff,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_promptA,
|
|
negative_promptB,
|
|
tradoff_nag,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
)
|
|
|
|
# 4. set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps, num_inference_steps = self.get_timesteps(
|
|
num_inference_steps=num_inference_steps, strength=strength, device=device
|
|
)
|
|
# check that number of inference steps is not < 1 - as this doesn't make sense
|
|
if num_inference_steps < 1:
|
|
raise ValueError(
|
|
f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
|
|
f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
|
|
)
|
|
# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
|
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
|
|
is_strength_max = strength == 1.0
|
|
|
|
# 5. Preprocess mask and image
|
|
mask, masked_image, init_image = prepare_mask_and_masked_image(
|
|
image, mask_image, height, width, return_image=True
|
|
)
|
|
mask_condition = mask.clone()
|
|
|
|
# 6. Prepare latent variables
|
|
num_channels_latents = self.vae.config.latent_channels
|
|
num_channels_unet = self.unet.config.in_channels
|
|
return_image_latents = num_channels_unet == 4
|
|
|
|
latents_outputs = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
image=init_image,
|
|
timestep=latent_timestep,
|
|
is_strength_max=is_strength_max,
|
|
return_noise=True,
|
|
return_image_latents=return_image_latents,
|
|
)
|
|
|
|
if return_image_latents:
|
|
latents, noise, image_latents = latents_outputs
|
|
else:
|
|
latents, noise = latents_outputs
|
|
|
|
# 7. Prepare mask latent variables
|
|
mask, masked_image_latents = self.prepare_mask_latents(
|
|
mask,
|
|
masked_image,
|
|
batch_size * num_images_per_prompt,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
do_classifier_free_guidance,
|
|
)
|
|
|
|
# 8. Check that sizes of mask, masked image and latents match
|
|
if num_channels_unet == 9:
|
|
# default case for runwayml/stable-diffusion-inpainting
|
|
num_channels_mask = mask.shape[1]
|
|
num_channels_masked_image = masked_image_latents.shape[1]
|
|
if (
|
|
num_channels_latents + num_channels_mask + num_channels_masked_image
|
|
!= self.unet.config.in_channels
|
|
):
|
|
raise ValueError(
|
|
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
|
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
|
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
|
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
|
|
" `pipeline.unet` or your `mask_image` or `image` input."
|
|
)
|
|
elif num_channels_unet != 4:
|
|
raise ValueError(
|
|
f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
|
|
)
|
|
|
|
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 10. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = (
|
|
torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
)
|
|
|
|
# concat latents, mask, masked_image_latents in the channel dimension
|
|
latent_model_input = self.scheduler.scale_model_input(
|
|
latent_model_input, t
|
|
)
|
|
|
|
if num_channels_unet == 9:
|
|
latent_model_input = torch.cat(
|
|
[latent_model_input, mask, masked_image_latents], dim=1
|
|
)
|
|
|
|
# predict the noise residual
|
|
if task_class is not None:
|
|
noise_pred = self.unet(
|
|
sample=latent_model_input,
|
|
timestep=t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
return_dict=False,
|
|
task_class=task_class,
|
|
)[0]
|
|
else:
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (
|
|
noise_pred_text - noise_pred_uncond
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(
|
|
noise_pred, t, latents, **extra_step_kwargs, return_dict=False
|
|
)[0]
|
|
|
|
if num_channels_unet == 4:
|
|
init_latents_proper = image_latents[:1]
|
|
init_mask = mask[:1]
|
|
|
|
if i < len(timesteps) - 1:
|
|
noise_timestep = timesteps[i + 1]
|
|
init_latents_proper = self.scheduler.add_noise(
|
|
init_latents_proper, noise, torch.tensor([noise_timestep])
|
|
)
|
|
|
|
latents = (
|
|
1 - init_mask
|
|
) * init_latents_proper + init_mask * latents
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or (
|
|
(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
|
|
):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
if not output_type == "latent":
|
|
condition_kwargs = {}
|
|
if isinstance(self.vae, AsymmetricAutoencoderKL):
|
|
init_image = init_image.to(
|
|
device=device, dtype=masked_image_latents.dtype
|
|
)
|
|
init_image_condition = init_image.clone()
|
|
init_image = self._encode_vae_image(init_image, generator=generator)
|
|
mask_condition = mask_condition.to(
|
|
device=device, dtype=masked_image_latents.dtype
|
|
)
|
|
condition_kwargs = {
|
|
"image": init_image_condition,
|
|
"mask": mask_condition,
|
|
}
|
|
image = self.vae.decode(
|
|
latents / self.vae.config.scaling_factor,
|
|
return_dict=False,
|
|
**condition_kwargs,
|
|
)[0]
|
|
image, has_nsfw_concept = self.run_safety_checker(
|
|
image, device, prompt_embeds.dtype
|
|
)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(
|
|
image, output_type=output_type, do_denormalize=do_denormalize
|
|
)
|
|
|
|
# Offload last model to CPU
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.final_offload_hook.offload()
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
)
|