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https://github.com/huggingface/diffusers.git
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430c557b6a
* add magcache * formatting * add magcache support with calibration mode * add imports * improvements * Apply style fixes * fix kandinsky errors * add tests and documentation * Apply style fixes * improvements * Apply style fixes * make fix-copies. * minor fixes --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
# Copyright 2025 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 Optional
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import torch
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from ..models.attention import AttentionModuleMixin, FeedForward, LuminaFeedForward
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from ..models.attention_processor import Attention, MochiAttention
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_ATTENTION_CLASSES = (Attention, MochiAttention, AttentionModuleMixin)
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_FEEDFORWARD_CLASSES = (FeedForward, LuminaFeedForward)
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_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS = (
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"blocks",
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"transformer_blocks",
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"single_transformer_blocks",
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"layers",
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"visual_transformer_blocks",
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)
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_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("temporal_transformer_blocks",)
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_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "layers")
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_ALL_TRANSFORMER_BLOCK_IDENTIFIERS = tuple(
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{
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*_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS,
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*_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS,
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*_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS,
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}
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)
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# Layers supported for group offloading and layerwise casting
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_GO_LC_SUPPORTED_PYTORCH_LAYERS = (
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torch.nn.Conv1d,
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torch.nn.Conv2d,
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torch.nn.Conv3d,
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torch.nn.ConvTranspose1d,
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torch.nn.ConvTranspose2d,
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torch.nn.ConvTranspose3d,
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torch.nn.Linear,
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# TODO(aryan): look into torch.nn.LayerNorm, torch.nn.GroupNorm later, seems to be causing some issues with CogVideoX
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# because of double invocation of the same norm layer in CogVideoXLayerNorm
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)
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def _get_submodule_from_fqn(module: torch.nn.Module, fqn: str) -> Optional[torch.nn.Module]:
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for submodule_name, submodule in module.named_modules():
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if submodule_name == fqn:
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return submodule
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return None
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