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| | from dataclasses import dataclass |
| | from typing import List, Optional, Union |
| |
|
| | import numpy as np |
| | import PIL |
| |
|
| | from ...utils import BaseOutput, is_paddle_available, is_paddlenlp_available |
| |
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|
| | @dataclass |
| | |
| | class AltDiffusionPipelineOutput(BaseOutput): |
| | """ |
| | Output class for Alt Diffusion pipelines. |
| | |
| | Args: |
| | images (`List[PIL.Image.Image]` or `np.ndarray`) |
| | List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, |
| | num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. |
| | nsfw_content_detected (`List[bool]`) |
| | List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" |
| | (nsfw) content, or `None` if safety checking could not be performed. |
| | """ |
| |
|
| | images: Union[List[PIL.Image.Image], np.ndarray] |
| | nsfw_content_detected: Optional[List[bool]] |
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|
| | if is_paddlenlp_available() and is_paddle_available(): |
| | from .modeling_roberta_series import RobertaSeriesModelWithTransformation |
| | from .pipeline_alt_diffusion import AltDiffusionPipeline |
| | from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline |
| |
|