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from dataclasses import dataclass |
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from typing import List, Optional, Union |
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import numpy as np |
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import PIL |
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from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available |
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from .timesteps import ( |
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fast27_timesteps, |
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smart27_timesteps, |
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smart50_timesteps, |
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smart100_timesteps, |
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smart185_timesteps, |
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super27_timesteps, |
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super40_timesteps, |
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super100_timesteps, |
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) |
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@dataclass |
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class IFPipelineOutput(BaseOutput): |
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""" |
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Args: |
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Output class for Stable Diffusion pipelines. |
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images (`List[PIL.Image.Image]` or `np.ndarray`) |
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List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, |
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num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. |
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nsfw_detected (`List[bool]`) |
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List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" |
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(nsfw) content or a watermark. `None` if safety checking could not be performed. |
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watermark_detected (`List[bool]`) |
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List of flags denoting whether the corresponding generated image likely has a watermark. `None` if safety |
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checking could not be performed. |
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""" |
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images: Union[List[PIL.Image.Image], np.ndarray] |
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nsfw_detected: Optional[List[bool]] |
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watermark_detected: Optional[List[bool]] |
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try: |
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if not (is_transformers_available() and is_torch_available()): |
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raise OptionalDependencyNotAvailable() |
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except OptionalDependencyNotAvailable: |
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from ...utils.dummy_torch_and_transformers_objects import * |
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else: |
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from .pipeline_if import IFPipeline |
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from .pipeline_if_img2img import IFImg2ImgPipeline |
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from .pipeline_if_img2img_superresolution import IFImg2ImgSuperResolutionPipeline |
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from .pipeline_if_inpainting import IFInpaintingPipeline |
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from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline |
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from .pipeline_if_superresolution import IFSuperResolutionPipeline |
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from .safety_checker import IFSafetyChecker |
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from .watermark import IFWatermarker |
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