39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
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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# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
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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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