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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.
"""
State dict utilities: utility methods for converting state dicts easily
"""
import enum
from .logging import get_logger
logger = get_logger(__name__)
class StateDictType(enum.Enum):
"""
The mode to use when converting state dicts.
"""
DIFFUSERS_OLD = "diffusers_old"
KOHYA_SS = "kohya_ss"
PEFT = "peft"
DIFFUSERS = "diffusers"
# We need to define a proper mapping for Unet since it uses different output keys than text encoder
# e.g. to_q_lora -> q_proj / to_q
UNET_TO_DIFFUSERS = {
".to_out_lora.up": ".to_out.0.lora_B",
".to_out_lora.down": ".to_out.0.lora_A",
".to_q_lora.down": ".to_q.lora_A",
".to_q_lora.up": ".to_q.lora_B",
".to_k_lora.down": ".to_k.lora_A",
".to_k_lora.up": ".to_k.lora_B",
".to_v_lora.down": ".to_v.lora_A",
".to_v_lora.up": ".to_v.lora_B",
".lora.up": ".lora_B",
".lora.down": ".lora_A",
".to_out.lora_magnitude_vector": ".to_out.0.lora_magnitude_vector",
}
DIFFUSERS_TO_PEFT = {
".q_proj.lora_linear_layer.up": ".q_proj.lora_B",
".q_proj.lora_linear_layer.down": ".q_proj.lora_A",
".k_proj.lora_linear_layer.up": ".k_proj.lora_B",
".k_proj.lora_linear_layer.down": ".k_proj.lora_A",
".v_proj.lora_linear_layer.up": ".v_proj.lora_B",
".v_proj.lora_linear_layer.down": ".v_proj.lora_A",
".out_proj.lora_linear_layer.up": ".out_proj.lora_B",
".out_proj.lora_linear_layer.down": ".out_proj.lora_A",
".lora_linear_layer.up": ".lora_B",
".lora_linear_layer.down": ".lora_A",
"text_projection.lora.down.weight": "text_projection.lora_A.weight",
"text_projection.lora.up.weight": "text_projection.lora_B.weight",
}
DIFFUSERS_OLD_TO_PEFT = {
".to_q_lora.up": ".q_proj.lora_B",
".to_q_lora.down": ".q_proj.lora_A",
".to_k_lora.up": ".k_proj.lora_B",
".to_k_lora.down": ".k_proj.lora_A",
".to_v_lora.up": ".v_proj.lora_B",
".to_v_lora.down": ".v_proj.lora_A",
".to_out_lora.up": ".out_proj.lora_B",
".to_out_lora.down": ".out_proj.lora_A",
".lora_linear_layer.up": ".lora_B",
".lora_linear_layer.down": ".lora_A",
}
PEFT_TO_DIFFUSERS = {
".q_proj.lora_B": ".q_proj.lora_linear_layer.up",
".q_proj.lora_A": ".q_proj.lora_linear_layer.down",
".k_proj.lora_B": ".k_proj.lora_linear_layer.up",
".k_proj.lora_A": ".k_proj.lora_linear_layer.down",
".v_proj.lora_B": ".v_proj.lora_linear_layer.up",
".v_proj.lora_A": ".v_proj.lora_linear_layer.down",
".out_proj.lora_B": ".out_proj.lora_linear_layer.up",
".out_proj.lora_A": ".out_proj.lora_linear_layer.down",
"to_k.lora_A": "to_k.lora.down",
"to_k.lora_B": "to_k.lora.up",
"to_q.lora_A": "to_q.lora.down",
"to_q.lora_B": "to_q.lora.up",
"to_v.lora_A": "to_v.lora.down",
"to_v.lora_B": "to_v.lora.up",
"to_out.0.lora_A": "to_out.0.lora.down",
"to_out.0.lora_B": "to_out.0.lora.up",
}
DIFFUSERS_OLD_TO_DIFFUSERS = {
".to_q_lora.up": ".q_proj.lora_linear_layer.up",
".to_q_lora.down": ".q_proj.lora_linear_layer.down",
".to_k_lora.up": ".k_proj.lora_linear_layer.up",
".to_k_lora.down": ".k_proj.lora_linear_layer.down",
".to_v_lora.up": ".v_proj.lora_linear_layer.up",
".to_v_lora.down": ".v_proj.lora_linear_layer.down",
".to_out_lora.up": ".out_proj.lora_linear_layer.up",
".to_out_lora.down": ".out_proj.lora_linear_layer.down",
".to_k.lora_magnitude_vector": ".k_proj.lora_magnitude_vector",
".to_v.lora_magnitude_vector": ".v_proj.lora_magnitude_vector",
".to_q.lora_magnitude_vector": ".q_proj.lora_magnitude_vector",
".to_out.lora_magnitude_vector": ".out_proj.lora_magnitude_vector",
}
PEFT_TO_KOHYA_SS = {
"lora_A": "lora_down",
"lora_B": "lora_up",
# This is not a comprehensive dict as kohya format requires replacing `.` with `_` in keys,
# adding prefixes and adding alpha values
# Check `convert_state_dict_to_kohya` for more
}
PEFT_STATE_DICT_MAPPINGS = {
StateDictType.DIFFUSERS_OLD: DIFFUSERS_OLD_TO_PEFT,
StateDictType.DIFFUSERS: DIFFUSERS_TO_PEFT,
}
DIFFUSERS_STATE_DICT_MAPPINGS = {
StateDictType.DIFFUSERS_OLD: DIFFUSERS_OLD_TO_DIFFUSERS,
StateDictType.PEFT: PEFT_TO_DIFFUSERS,
}
KOHYA_STATE_DICT_MAPPINGS = {StateDictType.PEFT: PEFT_TO_KOHYA_SS}
KEYS_TO_ALWAYS_REPLACE = {
".processor.": ".",
}
def convert_state_dict(state_dict, mapping):
r"""
Simply iterates over the state dict and replaces the patterns in `mapping` with the corresponding values.
Args:
state_dict (`dict[str, torch.Tensor]`):
The state dict to convert.
mapping (`dict[str, str]`):
The mapping to use for conversion, the mapping should be a dictionary with the following structure:
- key: the pattern to replace
- value: the pattern to replace with
Returns:
converted_state_dict (`dict`)
The converted state dict.
"""
converted_state_dict = {}
for k, v in state_dict.items():
# First, filter out the keys that we always want to replace
for pattern in KEYS_TO_ALWAYS_REPLACE.keys():
if pattern in k:
new_pattern = KEYS_TO_ALWAYS_REPLACE[pattern]
k = k.replace(pattern, new_pattern)
for pattern in mapping.keys():
if pattern in k:
new_pattern = mapping[pattern]
k = k.replace(pattern, new_pattern)
break
converted_state_dict[k] = v
return converted_state_dict
def convert_state_dict_to_peft(state_dict, original_type=None, **kwargs):
r"""
Converts a state dict to the PEFT format The state dict can be from previous diffusers format (`OLD_DIFFUSERS`), or
new diffusers format (`DIFFUSERS`). The method only supports the conversion from diffusers old/new to PEFT for now.
Args:
state_dict (`dict[str, torch.Tensor]`):
The state dict to convert.
original_type (`StateDictType`, *optional*):
The original type of the state dict, if not provided, the method will try to infer it automatically.
"""
if original_type is None:
# Old diffusers to PEFT
if any("to_out_lora" in k for k in state_dict.keys()):
original_type = StateDictType.DIFFUSERS_OLD
elif any("lora_linear_layer" in k for k in state_dict.keys()):
original_type = StateDictType.DIFFUSERS
else:
raise ValueError("Could not automatically infer state dict type")
if original_type not in PEFT_STATE_DICT_MAPPINGS.keys():
raise ValueError(f"Original type {original_type} is not supported")
mapping = PEFT_STATE_DICT_MAPPINGS[original_type]
return convert_state_dict(state_dict, mapping)
def convert_state_dict_to_diffusers(state_dict, original_type=None, **kwargs):
r"""
Converts a state dict to new diffusers format. The state dict can be from previous diffusers format
(`OLD_DIFFUSERS`), or PEFT format (`PEFT`) or new diffusers format (`DIFFUSERS`). In the last case the method will
return the state dict as is.
The method only supports the conversion from diffusers old, PEFT to diffusers new for now.
Args:
state_dict (`dict[str, torch.Tensor]`):
The state dict to convert.
original_type (`StateDictType`, *optional*):
The original type of the state dict, if not provided, the method will try to infer it automatically.
kwargs (`dict`, *args*):
Additional arguments to pass to the method.
- **adapter_name**: For example, in case of PEFT, some keys will be pre-pended
with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in
`get_peft_model_state_dict` method:
https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92
but we add it here in case we don't want to rely on that method.
"""
peft_adapter_name = kwargs.pop("adapter_name", None)
if peft_adapter_name is not None:
peft_adapter_name = "." + peft_adapter_name
else:
peft_adapter_name = ""
if original_type is None:
# Old diffusers to PEFT
if any("to_out_lora" in k for k in state_dict.keys()):
original_type = StateDictType.DIFFUSERS_OLD
elif any(f".lora_A{peft_adapter_name}.weight" in k for k in state_dict.keys()):
original_type = StateDictType.PEFT
elif any("lora_linear_layer" in k for k in state_dict.keys()):
# nothing to do
return state_dict
else:
raise ValueError("Could not automatically infer state dict type")
if original_type not in DIFFUSERS_STATE_DICT_MAPPINGS.keys():
raise ValueError(f"Original type {original_type} is not supported")
mapping = DIFFUSERS_STATE_DICT_MAPPINGS[original_type]
return convert_state_dict(state_dict, mapping)
def convert_unet_state_dict_to_peft(state_dict):
r"""
Converts a state dict from UNet format to diffusers format - i.e. by removing some keys
"""
mapping = UNET_TO_DIFFUSERS
return convert_state_dict(state_dict, mapping)
def convert_all_state_dict_to_peft(state_dict):
r"""
Attempts to first `convert_state_dict_to_peft`, and if it doesn't detect `lora_linear_layer` for a valid
`DIFFUSERS` LoRA for example, attempts to exclusively convert the Unet `convert_unet_state_dict_to_peft`
"""
try:
peft_dict = convert_state_dict_to_peft(state_dict)
except Exception as e:
if str(e) == "Could not automatically infer state dict type":
peft_dict = convert_unet_state_dict_to_peft(state_dict)
else:
raise
if not any("lora_A" in key or "lora_B" in key for key in peft_dict.keys()):
raise ValueError("Your LoRA was not converted to PEFT")
return peft_dict
def convert_state_dict_to_kohya(state_dict, original_type=None, **kwargs):
r"""
Converts a `PEFT` state dict to `Kohya` format that can be used in AUTOMATIC1111, ComfyUI, SD.Next, InvokeAI, etc.
The method only supports the conversion from PEFT to Kohya for now.
Args:
state_dict (`dict[str, torch.Tensor]`):
The state dict to convert.
original_type (`StateDictType`, *optional*):
The original type of the state dict, if not provided, the method will try to infer it automatically.
kwargs (`dict`, *args*):
Additional arguments to pass to the method.
- **adapter_name**: For example, in case of PEFT, some keys will be pre-pended
with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in
`get_peft_model_state_dict` method:
https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92
but we add it here in case we don't want to rely on that method.
"""
try:
import torch
except ImportError:
logger.error("Converting PEFT state dicts to Kohya requires torch to be installed.")
raise
peft_adapter_name = kwargs.pop("adapter_name", None)
if peft_adapter_name is not None:
peft_adapter_name = "." + peft_adapter_name
else:
peft_adapter_name = ""
if original_type is None:
if any(f".lora_A{peft_adapter_name}.weight" in k for k in state_dict.keys()):
original_type = StateDictType.PEFT
if original_type not in KOHYA_STATE_DICT_MAPPINGS.keys():
raise ValueError(f"Original type {original_type} is not supported")
# Use the convert_state_dict function with the appropriate mapping
kohya_ss_partial_state_dict = convert_state_dict(state_dict, KOHYA_STATE_DICT_MAPPINGS[StateDictType.PEFT])
kohya_ss_state_dict = {}
# Additional logic for replacing header, alpha parameters `.` with `_` in all keys
for kohya_key, weight in kohya_ss_partial_state_dict.items():
if "text_encoder_2." in kohya_key:
kohya_key = kohya_key.replace("text_encoder_2.", "lora_te2.")
elif "text_encoder." in kohya_key:
kohya_key = kohya_key.replace("text_encoder.", "lora_te1.")
elif "unet" in kohya_key:
kohya_key = kohya_key.replace("unet", "lora_unet")
elif "lora_magnitude_vector" in kohya_key:
kohya_key = kohya_key.replace("lora_magnitude_vector", "dora_scale")
kohya_key = kohya_key.replace(".", "_", kohya_key.count(".") - 2)
kohya_key = kohya_key.replace(peft_adapter_name, "") # Kohya doesn't take names
kohya_ss_state_dict[kohya_key] = weight
if "lora_down" in kohya_key:
alpha_key = f'{kohya_key.split(".")[0]}.alpha'
kohya_ss_state_dict[alpha_key] = torch.tensor(len(weight))
return kohya_ss_state_dict
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