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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, is_paddle_available, is_paddlenlp_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)`. 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, or `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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if is_paddlenlp_available() and is_paddle_available(): |
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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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