59 lines
2.4 KiB
Python
59 lines
2.4 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 Dict
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import torch
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class AttnProcsLayers(torch.nn.Module):
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def __init__(self, state_dict: Dict[str, torch.Tensor]):
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super().__init__()
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self.layers = torch.nn.ModuleList(state_dict.values())
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self.mapping = dict(enumerate(state_dict.keys()))
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self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
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# .processor for unet, .self_attn for text encoder
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self.split_keys = [".processor", ".self_attn"]
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# we add a hook to state_dict() and load_state_dict() so that the
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# naming fits with `unet.attn_processors`
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def map_to(module, state_dict, *args, **kwargs):
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new_state_dict = {}
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for key, value in state_dict.items():
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num = int(key.split(".")[1]) # 0 is always "layers"
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new_key = key.replace(f"layers.{num}", module.mapping[num])
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new_state_dict[new_key] = value
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return new_state_dict
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def remap_key(key, state_dict):
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for k in self.split_keys:
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if k in key:
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return key.split(k)[0] + k
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raise ValueError(
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f"There seems to be a problem with the state_dict: {set(state_dict.keys())}. {key} has to have one of {self.split_keys}."
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)
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def map_from(module, state_dict, *args, **kwargs):
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all_keys = list(state_dict.keys())
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for key in all_keys:
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replace_key = remap_key(key, state_dict)
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new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
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state_dict[new_key] = state_dict[key]
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del state_dict[key]
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self._register_state_dict_hook(map_to)
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self._register_load_state_dict_pre_hook(map_from, with_module=True)
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