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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 PIL import Image |
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from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available |
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@dataclass |
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class AltDiffusionPipelineOutput(BaseOutput): |
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""" |
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Output class for Alt 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)`. |
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nsfw_content_detected (`List[bool]`) |
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List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or |
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`None` if safety 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_content_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 ShapEPipeline |
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else: |
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from .modeling_roberta_series import RobertaSeriesModelWithTransformation |
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from .pipeline_alt_diffusion import AltDiffusionPipeline |
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from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline |
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