586 lines
28 KiB
Python
586 lines
28 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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# Copy from https://github.com/mikonvergence/ControlNetInpaint/blob/main/src/pipeline_stable_diffusion_controlnet_inpaint.py
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import torch
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import PIL.Image
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import numpy as np
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import *
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> # !pip install opencv-python transformers accelerate
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>>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
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>>> from diffusers.utils import load_image
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>>> import numpy as np
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>>> import torch
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>>> import cv2
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>>> from PIL import Image
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>>> # download an image
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>>> image = load_image(
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... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
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... )
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>>> image = np.array(image)
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>>> mask_image = load_image(
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... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
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... )
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>>> mask_image = np.array(mask_image)
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>>> # get canny image
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>>> canny_image = cv2.Canny(image, 100, 200)
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>>> canny_image = canny_image[:, :, None]
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>>> canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
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>>> canny_image = Image.fromarray(canny_image)
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>>> # load control net and stable diffusion v1-5
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>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
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>>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
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... "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
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... )
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>>> # speed up diffusion process with faster scheduler and memory optimization
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>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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>>> # remove following line if xformers is not installed
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>>> pipe.enable_xformers_memory_efficient_attention()
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>>> pipe.enable_model_cpu_offload()
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>>> # generate image
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>>> generator = torch.manual_seed(0)
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>>> image = pipe(
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... "futuristic-looking doggo",
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... num_inference_steps=20,
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... generator=generator,
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... image=image,
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... control_image=canny_image,
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... mask_image=mask_image
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... ).images[0]
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```
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"""
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def prepare_mask_and_masked_image(image, mask):
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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 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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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 = 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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return mask, masked_image
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class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline):
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r"""
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Pipeline for text-guided image inpainting using Stable Diffusion with ControlNet guidance.
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This model inherits from [`StableDiffusionControlNetPipeline`]. 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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controlnet ([`ControlNetModel`]):
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Provides additional conditioning to the unet during the denoising process
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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 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 prepare_mask_latents(
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self,
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mask,
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masked_image,
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batch_size,
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height,
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width,
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dtype,
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device,
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generator,
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do_classifier_free_guidance,
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):
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# resize the mask to latents shape as we concatenate the mask to the latents
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# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
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# and half precision
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mask = torch.nn.functional.interpolate(
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mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
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)
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mask = mask.to(device=device, dtype=dtype)
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masked_image = masked_image.to(device=device, dtype=dtype)
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# encode the mask image into latents space so we can concatenate it to the latents
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if isinstance(generator, list):
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masked_image_latents = [
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self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(
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generator=generator[i]
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)
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for i in range(batch_size)
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]
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masked_image_latents = torch.cat(masked_image_latents, dim=0)
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else:
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masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(
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generator=generator
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)
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masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
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# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
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if mask.shape[0] < batch_size:
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if not batch_size % mask.shape[0] == 0:
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raise ValueError(
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"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
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f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
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" of masks that you pass is divisible by the total requested batch size."
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)
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mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
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if masked_image_latents.shape[0] < batch_size:
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if not batch_size % masked_image_latents.shape[0] == 0:
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raise ValueError(
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"The passed images and the required batch size don't match. Images are supposed to be duplicated"
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f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
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" Make sure the number of images that you pass is divisible by the total requested batch size."
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)
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masked_image_latents = masked_image_latents.repeat(
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batch_size // masked_image_latents.shape[0], 1, 1, 1
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)
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mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
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masked_image_latents = (
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torch.cat([masked_image_latents] * 2)
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if do_classifier_free_guidance
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else masked_image_latents
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)
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# aligning device to prevent device errors when concating it with the latent model input
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masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
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return mask, masked_image_latents
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Union[str, List[str]] = None,
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image: Union[torch.FloatTensor, PIL.Image.Image] = None,
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control_image: Union[
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torch.FloatTensor,
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PIL.Image.Image,
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List[torch.FloatTensor],
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List[PIL.Image.Image],
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] = None,
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mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: int = 1,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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controlnet_conditioning_scale: float = 1.0,
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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]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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image (`PIL.Image.Image`):
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`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
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be masked out with `mask_image` and repainted according to `prompt`.
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control_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
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The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
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the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
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also be accepted as an image. The control image is automatically resized to fit the output image.
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mask_image (`PIL.Image.Image`):
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`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
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repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
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to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
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instead of 3, so the expected shape would be `(B, H, W, 1)`.
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height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
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Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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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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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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cross_attention_kwargs (`dict`, *optional*):
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A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
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`self.processor` in
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[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
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controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
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The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
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to the residual in the original unet.
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Examples:
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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# 0. Default height and width to unet
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height, width = self._default_height_width(height, width, control_image)
|
|
|
|
# 1. Check inputs. Raise error if not correct
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|
self.check_inputs(
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|
prompt,
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|
control_image,
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|
height,
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|
width,
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|
callback_steps,
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|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
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|
batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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|
batch_size = len(prompt)
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|
else:
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|
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.
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|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
prompt_embeds = self._encode_prompt(
|
|
prompt,
|
|
device,
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|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
)
|
|
|
|
# 4. Prepare image
|
|
control_image = self.prepare_image(
|
|
control_image,
|
|
width,
|
|
height,
|
|
batch_size * num_images_per_prompt,
|
|
num_images_per_prompt,
|
|
device,
|
|
self.controlnet.dtype,
|
|
)
|
|
|
|
if do_classifier_free_guidance:
|
|
control_image = torch.cat([control_image] * 2)
|
|
|
|
# 5. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 6. Prepare latent variables
|
|
num_channels_latents = self.controlnet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# EXTRA: prepare mask latents
|
|
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
|
|
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,
|
|
)
|
|
|
|
# 7. 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)
|
|
|
|
# 8. 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
|
|
)
|
|
latent_model_input = self.scheduler.scale_model_input(
|
|
latent_model_input, t
|
|
)
|
|
|
|
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
controlnet_cond=control_image,
|
|
return_dict=False,
|
|
)
|
|
|
|
down_block_res_samples = [
|
|
down_block_res_sample * controlnet_conditioning_scale
|
|
for down_block_res_sample in down_block_res_samples
|
|
]
|
|
mid_block_res_sample *= controlnet_conditioning_scale
|
|
|
|
# predict the noise residual
|
|
latent_model_input = torch.cat(
|
|
[latent_model_input, mask, masked_image_latents], dim=1
|
|
)
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
down_block_additional_residuals=down_block_res_samples,
|
|
mid_block_additional_residual=mid_block_res_sample,
|
|
).sample
|
|
|
|
# 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
|
|
).prev_sample
|
|
|
|
# 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 we do sequential model offloading, let's offload unet and controlnet
|
|
# manually for max memory savings
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.unet.to("cpu")
|
|
self.controlnet.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
if output_type == "latent":
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
elif output_type == "pil":
|
|
# 8. Post-processing
|
|
image = self.decode_latents(latents)
|
|
|
|
# 9. Run safety checker
|
|
image, has_nsfw_concept = self.run_safety_checker(
|
|
image, device, prompt_embeds.dtype
|
|
)
|
|
|
|
# 10. Convert to PIL
|
|
image = self.numpy_to_pil(image)
|
|
else:
|
|
# 8. Post-processing
|
|
image = self.decode_latents(latents)
|
|
|
|
# 9. Run safety checker
|
|
image, has_nsfw_concept = self.run_safety_checker(
|
|
image, device, prompt_embeds.dtype
|
|
)
|
|
|
|
# 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
|
|
)
|