code: 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)
  
imports: import enum
label_desc: Use image node_id 3 for calling a global function with example usage: convert_state_dict_to_diffusers(state_dict, original_type) and returns: convert_state_dict, state_dict