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from dataclasses import dataclass |
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from typing import List, Union |
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import numpy as np |
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import PIL |
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from PIL import Image |
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from ...utils import BaseOutput, is_onnx_available, is_transformers_available |
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@dataclass |
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class StableDiffusionPipelineOutput(BaseOutput): |
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""" |
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Output class for Stable Diffusion pipelines. |
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Args: |
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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_content_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. |
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""" |
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images: Union[List[PIL.Image.Image], np.ndarray] |
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nsfw_content_detected: List[bool] |
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if is_transformers_available(): |
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from .pipeline_stable_diffusion import StableDiffusionPipeline |
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from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline |
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from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline |
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from .safety_checker import StableDiffusionSafetyChecker |
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if is_transformers_available() and is_onnx_available(): |
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from .pipeline_stable_diffusion_onnx import StableDiffusionOnnxPipeline |
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