102 lines
4.1 KiB
Python
102 lines
4.1 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 (
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BaseOutput,
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OptionalDependencyNotAvailable,
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is_flax_available,
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is_k_diffusion_available,
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is_onnx_available,
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is_torch_available,
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is_transformers_available,
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is_transformers_version,
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)
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@dataclass
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class StableDiffusionPipelineOutput(BaseOutput):
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"""
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Output class for Stable 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_transformers_available() and is_torch_available():
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from .pipeline_cycle_diffusion import CycleDiffusionPipeline
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from .pipeline_stable_diffusion import StableDiffusionPipeline
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from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
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from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
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from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
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from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
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from .safety_checker import StableDiffusionSafetyChecker
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try:
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if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
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raise OptionalDependencyNotAvailable()
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except OptionalDependencyNotAvailable:
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from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
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else:
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from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
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try:
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if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0.dev0")):
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raise OptionalDependencyNotAvailable()
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except OptionalDependencyNotAvailable:
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from ...utils.dummy_torch_and_transformers_objects import StableDiffusionDepth2ImgPipeline
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else:
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from .pipeline_stable_diffusion_depth2img import StableDiffusionDepth2ImgPipeline
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try:
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if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
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raise OptionalDependencyNotAvailable()
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except OptionalDependencyNotAvailable:
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from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
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else:
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from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
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if is_transformers_available() and is_onnx_available():
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from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
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from .pipeline_onnx_stable_diffusion_img2img import OnnxStableDiffusionImg2ImgPipeline
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from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
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from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
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if is_transformers_available() and is_flax_available():
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import flax
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@flax.struct.dataclass
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class FlaxStableDiffusionPipelineOutput(BaseOutput):
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"""
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Output class for Stable Diffusion pipelines.
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Args:
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images (`np.ndarray`)
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Array of shape `(batch_size, height, width, num_channels)` with images from 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.
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"""
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images: np.ndarray
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nsfw_content_detected: List[bool]
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from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
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from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
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from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
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