# Copyright 2023 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", } 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", } 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", } 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") 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