43 lines
1.5 KiB
Python
43 lines
1.5 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.Image
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from ...utils import BaseOutput
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@dataclass
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class LEditsPPDiffusionPipelineOutput(BaseOutput):
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"""
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Output class for LEdits++ 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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@dataclass
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class LEditsPPInversionPipelineOutput(BaseOutput):
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"""
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Output class for LEdits++ Diffusion pipelines.
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Args:
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input_images (`List[PIL.Image.Image]` or `np.ndarray`)
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List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape `
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(batch_size, height, width, num_channels)`.
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vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`)
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List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape
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` (batch_size, height, width, num_channels)`.
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"""
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images: Union[List[PIL.Image.Image], np.ndarray]
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vae_reconstruction_images: Union[List[PIL.Image.Image], np.ndarray]
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