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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict
import torch
class AttnProcsLayers(torch.nn.Module):
def __init__(self, state_dict: Dict[str, torch.Tensor]):
super().__init__()
self.layers = torch.nn.ModuleList(state_dict.values())
self.mapping = dict(enumerate(state_dict.keys()))
self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
# .processor for unet, .self_attn for text encoder
self.split_keys = [".processor", ".self_attn"]
# we add a hook to state_dict() and load_state_dict() so that the
# naming fits with `unet.attn_processors`
def map_to(module, state_dict, *args, **kwargs):
new_state_dict = {}
for key, value in state_dict.items():
num = int(key.split(".")[1]) # 0 is always "layers"
new_key = key.replace(f"layers.{num}", module.mapping[num])
new_state_dict[new_key] = value
return new_state_dict
def remap_key(key, state_dict):
for k in self.split_keys:
if k in key:
return key.split(k)[0] + k
raise ValueError(
f"There seems to be a problem with the state_dict: {set(state_dict.keys())}. {key} has to have one of {self.split_keys}."
)
def map_from(module, state_dict, *args, **kwargs):
all_keys = list(state_dict.keys())
for key in all_keys:
replace_key = remap_key(key, state_dict)
new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
state_dict[new_key] = state_dict[key]
del state_dict[key]
self._register_state_dict_hook(map_to)
self._register_load_state_dict_pre_hook(map_from, with_module=True)
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