95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING
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from ..utils import (
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DIFFUSERS_SLOW_IMPORT,
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_LazyModule,
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is_flax_available,
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is_torch_available,
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)
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_import_structure = {}
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if is_torch_available():
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_import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
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_import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
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_import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
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_import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
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_import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
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_import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
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_import_structure["controlnet"] = ["ControlNetModel"]
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_import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
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_import_structure["embeddings"] = ["ImageProjection"]
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_import_structure["modeling_utils"] = ["ModelMixin"]
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_import_structure["prior_transformer"] = ["PriorTransformer"]
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_import_structure["t5_film_transformer"] = ["T5FilmDecoder"]
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_import_structure["transformer_2d"] = ["Transformer2DModel"]
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_import_structure["transformer_temporal"] = ["TransformerTemporalModel"]
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_import_structure["unet_1d"] = ["UNet1DModel"]
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_import_structure["unet_2d"] = ["UNet2DModel"]
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_import_structure["unet_2d_condition"] = ["UNet2DConditionModel"]
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_import_structure["unet_3d_condition"] = ["UNet3DConditionModel"]
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_import_structure["unet_kandinsky3"] = ["Kandinsky3UNet"]
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_import_structure["unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
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_import_structure["unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
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_import_structure["uvit_2d"] = ["UVit2DModel"]
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_import_structure["vq_model"] = ["VQModel"]
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if is_flax_available():
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_import_structure["controlnet_flax"] = ["FlaxControlNetModel"]
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_import_structure["unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
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_import_structure["vae_flax"] = ["FlaxAutoencoderKL"]
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if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
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if is_torch_available():
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from .adapter import MultiAdapter, T2IAdapter
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from .autoencoders import (
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AsymmetricAutoencoderKL,
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AutoencoderKL,
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AutoencoderKLTemporalDecoder,
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AutoencoderTiny,
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ConsistencyDecoderVAE,
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)
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from .controlnet import ControlNetModel
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from .dual_transformer_2d import DualTransformer2DModel
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from .embeddings import ImageProjection
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from .modeling_utils import ModelMixin
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from .prior_transformer import PriorTransformer
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from .t5_film_transformer import T5FilmDecoder
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from .transformer_2d import Transformer2DModel
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from .transformer_temporal import TransformerTemporalModel
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from .unet_1d import UNet1DModel
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from .unet_2d import UNet2DModel
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from .unet_2d_condition import UNet2DConditionModel
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from .unet_3d_condition import UNet3DConditionModel
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from .unet_kandinsky3 import Kandinsky3UNet
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from .unet_motion_model import MotionAdapter, UNetMotionModel
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from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
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from .uvit_2d import UVit2DModel
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from .vq_model import VQModel
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if is_flax_available():
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from .controlnet_flax import FlaxControlNetModel
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from .unet_2d_condition_flax import FlaxUNet2DConditionModel
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from .vae_flax import FlaxAutoencoderKL
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else:
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import sys
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sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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